Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
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Parametric Optimization of Electric Discharge Machining for AISI 1045 Steel:
A Comprehensive Study
Optimización paramétrica del mecanizado por electroerosión para acero AISI
1045: un estudio exhaustivo
Otimização Paramétrica da Usinagem por Eletroerosão para Aço AISI 1045: Um
Estudo Abrangente
Muhammad Mansoor Uz Zaman Siddiqui
1
(*), Syed Amir Iqbal
2
, Ali Zulqarnain
3
, Adeel Tabassum
4
Recibido: 20/11/2024 Aceptado: 02/03/2025
Summary. - This study investigates the optimization of Electric Discharge Machining (EDM) parameters for AISI
1045. It is a medium carbon steel which is commonly used in automotive and aerospace industries because of its
balanced strength, toughness and machinability. However, achieving optimal machining efficiency with excellent
surface finish in short time and without wasting excess material with EDM remains a challenge at large. The research
focuses on optimizing key EDM input parameters like current (LV), voltage (HV), pulse on time (Ton) and pulse off
time (Toff), to improve machining time (Tm), material removal rate (MRR), electrode wear rate (EWR), surface
roughness (Ra) and base radius (R). Full factorial design and Response Surface Methodology (RSM) were used to
conduct experiments, and ANOVA was employed to identify the most significant factors influencing the output
responses. Multi-objective optimization was performed through the desirability function and the findings were
validated by repeated experiments. The results showed that pulse on time (Ton), its interaction with pulse off time
(Toff) and the three-factor interaction between current (LV), Ton and Toff were the most significant factors affecting
machining performance. Optimizing these parameters reduced machining time (Tm) to 623.21 seconds thus
significantly improving EDM efficiency. The material removal rate (MRR) was maximized at 0.0173 g/min resulting
in considerable increase in material removal efficiency. The electrode wear rate (EWR) was minimized to 0.0088
g/min, which prolongs electrode life and reduces operational costs. Surface roughness (Ra) was improved to 0.0253
mm, ensuring a high-quality surface finish. The base radius (R) was successfully optimized to 1.5298 mm, aligning
closely with the desired target of 1.5 mm thus ensuring dimensional accuracy. This investigative study of optimization
of parameters for EDM of AISI 1045 material is extremely significant for automotive and aerospace industries that
rely on precision machining, as the optimized EDM parameters lead to improved efficiency, reduced material waste
and enhanced product quality. These findings offer valuable insights for improving EDM processes, particularly in
sectors requiring complex geometries and high precision, such as automotive and aerospace manufacturing.
Keywords: Electric Discharge Machining; AISI 1045; Parametric Optimization; Material Removal Rate; Electrode
Wear Rate; Surface Roughness; Machining Time; Response Surface Methodology; ANOVA; Base Radius
Resumen. - Este estudio investiga la optimización de los parámetros de mecanizado por descarga eléctrica (EDM)
para AISI 1045. Es un acero de carbono medio que se utiliza comúnmente en las industrias automotriz y aeroespacial
debido a su resistencia, tenacidad y maquinabilidad equilibradas. Sin embargo, lograr una eficiencia de mecanizado
óptima con un excelente acabado superficial en poco tiempo y sin desperdiciar material sobrante con EDM sigue
(*) Corresponding author.
1
Master of Engineering, Department of Industrial Engineering, University of Engineering & Technology (Pakistan),
2023phdmnf1@student.uet.edu.pk,
ORCID iD: https://orcid.org/0009-0007-8992-7601
2
Dean, Department of Industrial & Manufacturing Engineering, NEDUET (Pakistan), deanmme@neduet.edu.pk,
ORCID iD: https://orcid.org/0000-0002-6812-6238/
3
Director Industrial Liaison, Department of Industrial & Manufacturing Engineering, NEDUET (Pakistan), dil@neduet.edu.pk,
ORCID iD: https://orcid.org/0000-0003-2762-5409
4
Mechanical Engineer, Department of Mechanical Engineering, NUST (Pakistan), adeeltabassum1@gmail.com,
ORCID iD: https://orcid.org/0009-0006-9375-1090
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 223
siendo un desafío en general. La investigación se centra en la optimización de los parámetros de entrada clave de
EDM como la corriente (LV), el voltaje (HV), el tiempo de activación del pulso (Ton) y el tiempo de desactivación del
pulso (Toff), para mejorar el tiempo de mecanizado (Tm), la tasa de remoción de material (MRR), la tasa de desgaste
del electrodo (EWR), la rugosidad superficial (Ra) y el radio base (R). Se utilizaron el diseño factorial completo y la
Metodología de Superficie de Respuesta (RSM) para realizar experimentos y se empleó ANOVA para identificar los
factores más significativos que influyen en las respuestas de salida. Se realizó una optimización multiobjetivo a través
de la función de deseabilidad y los hallazgos se validaron mediante experimentos repetidos. Los resultados mostraron
que el tiempo de activación del pulso (Ton), su interacción con el tiempo de desactivación del pulso (Toff) y la
interacción de tres factores entre la corriente (LV), Ton y Toff fueron los factores más significativos que afectaron el
rendimiento del mecanizado. La optimización de estos parámetros redujo el tiempo de mecanizado (Tm) a 623,21
segundos, mejorando así significativamente la eficiencia de la electroerosión. La tasa de eliminación de material
(MRR) se maximizó a 0,0173 g/min, lo que resultó en un aumento considerable en la eficiencia de eliminación de
material. La tasa de desgaste del electrodo (EWR) se minimizó a 0,0088 g/min, lo que prolonga la vida útil del
electrodo y reduce los costos operativos. La rugosidad superficial (Ra) se mejoró a 0,0253 mm, lo que garantiza un
acabado superficial de alta calidad. El radio base (R) se optimizó con éxito a 1,5298 mm, alineándose estrechamente
con el objetivo deseado de 1,5 mm, lo que garantiza la precisión dimensional. Este estudio de investigación sobre la
optimización de parámetros para la electroerosión de material AISI 1045 es fundamental para las industrias
automotriz y aeroespacial que dependen del mecanizado de precisión, ya que la optimización de los parámetros de la
electroerosión mejora la eficiencia, reduce el desperdicio de material y mejora la calidad del producto. Estos
hallazgos ofrecen información valiosa para mejorar los procesos de electroerosión, especialmente en sectores que
requieren geometrías complejas y alta precisión, como la fabricación automotriz y aeroespacial.
Palabras clave: Mecanizado por electroerosión; AISI 1045; Optimización paramétrica; Tasa de remoción de material;
Tasa de desgaste de electrodos; Rugosidad superficial; Tiempo de mecanizado; Metodología de superficie de
respuesta; ANOVA; Radio base
Resumo. - Este estudo investiga a otimização dos parâmetros de usinagem por descarga elétrica (EDM) para AISI
1045. É um aço de médio carbono comumente usado nas indústrias automotiva e aeroespacial devido à sua resistência,
tenacidade e usinabilidade equilibradas. No entanto, atingir a eficiência de usinagem ideal com excelente acabamento
superficial em curto espaço de tempo e sem desperdiçar excesso de material com EDM continua sendo um grande
desafio. A pesquisa se concentra na otimização dos principais parâmetros de entrada de EDM, como corrente (LV),
tensão (HV), tempo de pulso ligado (Ton) e tempo de pulso desligado (Toff), para melhorar o tempo de usinagem (Tm),
taxa de remoção de material (MRR), taxa de desgaste do eletrodo (EWR), rugosidade da superfície (Ra) e raio da
base (R). O planejamento fatorial completo e a Metodologia de Superfície de Resposta (RSM) foram usados para
conduzir experimentos e ANOVA foi empregada para identificar os fatores mais significativos que influenciam as
respostas de saída. A otimização multiobjetivo foi realizada por meio da função de desejabilidade e os resultados
foram validados por experimentos repetidos. Os resultados mostraram que o tempo de pulso ligado (Ton), sua
interação com o tempo de pulso desligado (Toff) e a interação de três fatores entre corrente (LV), Ton e Toff foram os
fatores mais significativos que afetaram o desempenho da usinagem. A otimização desses parâmetros reduziu o tempo
de usinagem (Tm) para 623,21 segundos, melhorando significativamente a eficiência da EDM. A taxa de remoção de
material (MRR) foi maximizada em 0,0173 g/min, resultando em um aumento considerável na eficiência da remoção
de material. A taxa de desgaste do eletrodo (EWR) foi minimizada para 0,0088 g/min, o que prolonga a vida útil do
eletrodo e reduz os custos operacionais. A rugosidade da superfície (Ra) foi melhorada para 0,0253 mm, garantindo
um acabamento superficial de alta qualidade. O raio da base (R) foi otimizado com sucesso para 1,5298 mm,
alinhando-se estreitamente com o alvo desejado de 1,5 mm, garantindo assim a precisão dimensional. Este estudo
investigativo sobre a otimização de parâmetros para eletroerosão do material AISI 1045 é extremamente significativo
para as indústrias automotiva e aeroespacial que dependem de usinagem de precisão, visto que os parâmetros
otimizados de eletroerosão levam a uma maior eficiência, redução do desperdício de material e melhoria da qualidade
do produto. Essas descobertas oferecem insights valiosos para o aprimoramento dos processos de eletroerosão,
particularmente em setores que exigem geometrias complexas e alta precisão, como a indústria automotiva e
aeroespacial.
Palavras-chave: Usinagem por Descarga Elétrica; AISI 1045; Otimização Paramétrica; Taxa de Remoção de
Material; Taxa de Desgaste do Eletrodo; Rugosidade da Superfície; Tempo de Usinagem; Metodologia de Superfície
de Resposta; ANOVA; Raio da Base.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 224
1. Introduction. - The machining techniques that are generally used in the industry can be categorized into
conventional and non-conventional methods. The conventional machining process removes material from a workpiece
using mechanical techniques such as cutting, shearing, and abrasion. The techniques used can be milling, grinding,
drilling and turning (1). When the conventional machining techniques are applied, hard tools are used to shape the
workpiece to the required size and surface finish. Conversely, the non-conventional machining methods use alternative
techniques that are dependent on sources of high energy or other methods that can support in removal of material from
the workpiece. These methods include water jet machining (2), ultrasonic machining (3) (4), laser cutting (5) (6),
electrical discharge machining (EDM) (7) (8) and electrochemical machining (9) (10). The common applications for
the non-conventional machining processes are hard materials, complex geometries and those areas where the
conventional processes lose their effect. The parts produced are relatively accurate machined flexibly when these
techniques are utilized.
There’s always a need for usage of versatile materials that can be used for a wide range of applications in which the
material should show a balance between toughness, strength and wear resistance. Electric Discharge Machining (EDM)
is particularly well-suited for machining AISI 1045 due to its ability to handle hard materials and complex geometries
without inducing mechanical stress. AISI 1045 is a medium carbon steel with a moderate carbon content (0.43-0.5%)
and is widely used in industries requiring high strength and wear resistance, such as automotive and aerospace.
However, its hardness makes it challenging to machine using conventional methods, especially for intricate shapes and
tight tolerances. EDM is a contact less process and uses electrical impulses to erode material, making it ideal for such
applications. Additionally, EDM provides excellent surface finish quality, reducing the need for post-processing steps
like polishing or grinding. These advantages make EDM the preferred choice for machining AISI 1045 in precision-
critical applications.
Electrical Discharge Machining (EDM) is particularly well-suited for machining AISI 1045 due to the material's
properties and the unique capabilities of the EDM process. AISI 1045, a medium-carbon steel, is known for its good
tensile strength and wear resistance, making it a popular choice for components such as gears, shafts, and machinery
parts. However, its hardness and toughness can pose challenges for conventional machining methods, especially when
intricate shapes or fine surface finishes are required. EDM, being a non-contact machining process that uses electrical
discharges to remove material, is ideal for such scenarios. It can efficiently machine hard materials like AISI 1045
without inducing mechanical stress or tool wear, which are common issues in traditional machining. This makes EDM
a preferred method for achieving precise geometries and high-quality surface finishes on AISI 1045 components.
There are several industrial applications of AISI 1045 material which includes construction usage, tool and die making,
automotives industry and agriculture. In industrial applications, this material is used to make shafts, gears and
couplings, bolts and studs, crankshafts and connecting rods etc. In construction applications, its usage comes for those
structural components where a balance between strength and toughness is the task. In the dies and tools where wear
resistance and medium strength is required, this material is utilized. In automotive industry, this material is used to
manufacture axles as well as engine components (11). Similarly, it is used to produce components which are assembled
in agricultural machinery. This material has also found its way in the nuclear industry (12).
In EDM process, the manufacturing is carried out via electric discharge to obtain the desired shape. It works on the
workpiece as the material is eroded thermally. For usage of EDM on AISI 1045, it is preferable in some circumstances
which encompass a number of factors. The need for EDM on this material arises when the geometries are complex as
EDM is capable of producing intricate shapes with fine details that are not possible with conventional methods. Also,
when thin walls and sharp corners are required, EDM supports prevention of deformation in the components. AISI
1045 is good at heat treatment hardening as it becomes wear resistant at the same time it’s a challenge to machine it
uses conventional cutting processes (13). EDM is capable to machine this material with minimal tool wear. EDM on
AISI 1045 is also needed when there are tight tolerances and high precision to produce exact dimensions in critical
parts. After optimizing the input parameters and output responses, this manufacturing procedure reduces the need for
final processing steps like polishing or grinding. Since EDM is a non-contact process, deflection of tool and wearing
can be eliminated as it happens when processing hard materials conventionally. EDM also has better accessibility and
reaches internal cavities where conventional machining process doesn’t support.
The advantages of using EDM on AISI 1045 are quite impressive. There isn’t any mechanical stress as there isn’t any
type of direct contact between the electrode and workpiece as EDM is capable to machine hard materials with high
accuracy and precision with excellent surface finish due to which multiple and additional process requirements as in
non-conventional machining are eliminated (14). The application of EDM on AISI 1045 includes die and mold making
for injection molding as well as metal forming and stamping (15). Its’s application in tool and die industry for
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
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customized jigs and fixtures preparation is also noticeable (16). It is also used for rapid prototyping where complex
geometries are required. It can also be utilized in preparation of surgical instruments and medical implants in medical
devices (17). Most importantly, in automotive and aerospace industry, AISI 1045 is the choice for intricate parts
manufacturing for engines and also precision components (18).
EDM process has its own set of limitations which are an area of interest for researchers (19). EDM usually has a slower
rate when it comes to material removal i.e. MRR thus resulting in higher machining times (Tm). The conventional
processes are much faster in terms of material removal rate. Secondly, the initial setup cost of EDM is much higher as
compared to conventional subtractive manufacturing techniques. Material should also be electrically conductive in
order to be used for EDM. On reviewing the literature, it came to the authors’ knowledge that a very limited work has
been carried out on AISI 1045 when it comes to die sinking EDM process as most of the research work is carried out
with wire EDM (20) or conventional machining processes (21). Haron et al had performed experiment with varying
copper electrode diameter (9.5, 12 and 20 mm) and current value (3.5 and 6.5 A) to determine the optimum value of
material removal rate (MRR) and electrode wear rate (EWR) (22). Kumar and Agarwal had performed machining
parameters optimization for surface roughness in the EDM processing of AISI 1045 (23). There seemed a need to carry
out a comprehensive study to determine the effect of various input parameters including current (LV), voltage (HV),
pulse on time (Ton) and pulse off time (Toff) and monitor the various output parameters including MRR, EW,
machining time (Tm), base radius (R) and machined surface roughness (Ra) and optimize them accordingly. For large
scale manufacturers and designers, all output responses like Machining time (Tm), material removal rate (MRR),
electrode wear rate (EWR), surface roughness (Ra), base radius (R) are of extreme importance and compromise on any
of the responses means major loss in productivity or product quality. Currently there is no single study present at this
point of time where the afore mentioned parameters and their output responses have been considered in totality when
the EDM process of AISI 1045 is considered (24) (8) (25) (20) (26). This is of great importance for manufacturers in
automotive engine manufacturing, dies and mold makers, aerospace industry, bio medical machine manufacturing etc.
The current experimental research for optimizing the parameters was carried out in a meticulous setting and results of
the study were positive.
2. Materials and methods. -
2.1 Materials. - AISI-1045 is a low-cost alloy suitable for most engineering and construction applications. It is a
medium carbon steel with adequate strength and toughness characteristics and is valuable for induction or flame
hardened components and can provide a typical surface hardness of up to 58 HRC. The typical applications include
construction applications, bolts, axles, connecting rods, pins, rams, studs spindles, ratchets etc.
The authors have used copper electrode as it is a good performer in surface finishing and quality compared to graphite.
When using a graphite electrode, increased tool wear and poor surface quality are observed (27). The dielectric used
in the experiment is kerosene oil. K., Masoud Pour & S. Ehsan Layegh (2022) have conducted a study to optimize
MRR, Ra and surface topography on tool steels including AISI 1045 under the influence of ZnO nanoparticles. The
study concluded that the optimized values for input factors AISI 1045 had been achieved using 2 g of the ZnO
nanoparticles that had reduced the Ra by 16.66% (18).
In the current research, the experimentation has been carried out in a controlled environment with lower levels of input
factors, these resulted in positive output. The results will be discussed in detail in the results section.
The chemical composition of AISI-1045 is listed in Table
Element
%
Carbon (C)
0.45
Manganese (Mn)
0.75
Silicon (Si)
0.25
Sulphur (S)
0.05 max.
Phosphorous (P)
0.05 max.
Iron (Fe)
Balance
Table I. Chemical Composition of AISI 1045.
When transistorized, pulse-type power supplies, either electrolytic or pure were developed, the metallic electrode that
became preferable was copper as copper along with specific levels of power supply supports in low burning due to
wear. If graphite is consumed in the same setting, the tool wear is high. Moreover, for advanced power supply circuits
with polishing performed, copper is compatible. Copper produces a good surface finish due to its structural integrity
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
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ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 226
compared to the counterpart graphite. This property further resists DC arcing where flushing is poor. On a wire EDM,
Female electrodes are commonly utilized in copper for usage in reverse burning punches and cores in the sinker EDM
(25) (26).
2.2 Methods. - In this study, the researchers had planned the experiments to optimize the output responses like
machining time (Tm), material removal rate (MRR), electrode wear rate (EWR), surface roughness (Ra) and base
radius (R) using design of experiments (DOE) and Response Surface Methodology (RSM).
2.2.1 Design of experiments (DOE). - Planning any data collection activities in the face of variability, whether or not
the experimenter has complete control, is known as design of experiments (DOE). It entails a group of tests or a
sequence of tests in which the input variables of a system or process are purposefully changed. The goal is to
methodically monitor and pinpoint the reasons for variations in the output responses (28).
2.2.2 Response Surface Methodology (RSM). - It is a statistical and mathematical method for optimizing processes
and determining the correlations between numerous input factors and one or more output replies. It is especially
effective for modeling and analyzing issues whose outcomes are influenced by multiple variables. RSM combines
experimental design, regression analysis and optimization approach to create a mathematical model (usually a second-
order polynomial) that predicts response behavior based on input elements. The method aids in determining ideal
conditions for processes and is frequently used in engineering, manufacturing and other sectors to increase efficiency,
product quality and performance.
3. Experimental Methodology. - The experimental design was based on the Design of Experiment (DOE) technique,
especially a full factorial design. This method enables a thorough examination of the essential effects and interactions
among the four selected input parameters: pulse on time (Ton), pulse off time (Toff), current (LV) and voltage (HV).
Each of these parameters was examined at two different levels, high and low, allowing for a thorough examination of
their effect on output responses. Values of the input parameters are mentioned in the Table .
Factor
Levels
No. of Levels
Workpiece
AISI 1045
1
Pulse on time (Ton)
4 µs, 6.5µs
2
Pulse off time (Toff)
5.5 µs, 6.5 µs
2
Current (LV)
30 A, 50 A
2
Voltage (HV)
0.3 V, 0.7 V
2
Table II. Values of input parameters along with levels.
Pulse on time (Ton) was chosen because it directly affects the energy delivered to the workpiece during each pulse.
Longer pulse durations result in higher energy input consequently resulting in increased material removal rate but this
can also lead to higher electrode wear and surface roughness. In order to optimize all the output lower Ton values (4
µs and 6.5 µs) were selected to minimize electrode wear and reduce excessive heat generation, which is crucial for
precision applications. Similarly, pulse off time (Toff) was included because it controls the cooling time between
pulses. A slightly higher Toff (5.5 µs and 6.5 µs) was chosen to enhance flushing efficiency, ensuring better debris
removal and maintaining process stability. This helps prevent short circuits and improves surface finish. The study
avoided very low Toff values, as they could lead to insufficient cooling and debris removal, causing instability in the
machining process.
Current (LV) was another critical parameter selected for optimization because it influences the intensity of the electrical
discharge. The study chose current levels of 30 A and 50 A to balance power consumption and material removal
efficiency. Lower currents (30 A) are more energy-efficient and suitable for fine machining, while higher currents (50
A) increase MRR but may also increase electrode wear and surface roughness. Very high currents were avoided
because they could lead to excessive electrode wear and thermal damage, while very low currents might result in
insufficient material removal, making the process inefficient. Voltage (HV) was also included because it affects the
spark gap and the energy of each discharge. The study selected lower voltage levels (0.3 V and 0.7 V) to reduce thermal
damage and improve surface finish. Lower voltages are more suitable for precision machining, as they help achieve
finer surface finishes and tighter tolerances. Higher voltages were not considered because they could lead to larger
craters on the workpiece surface, increasing surface roughness and reducing dimensional accuracy.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
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ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 227
Other parameters, such as electrode material and dielectric fluid, were kept constant to isolate the effects of the primary
electrical parameters under investigation. Copper electrodes were chosen because they are known for their good surface
finish and lower wear rates compared to graphite electrodes, which tend to produce poorer surface quality and are less
suitable for precision applications. Kerosene was selected as the dielectric fluid due to its effectiveness in flushing
debris and cooling the workpiece and electrode. Other dielectric fluids, such as deionized water or oil-based fluids,
were not considered because kerosene is widely used in EDM processes and provides a good balance between cost and
performance. The duty factor, which is the ratio of Ton to the total cycle time, was indirectly controlled by the selection
of Ton and Toff. The study did not explicitly vary the duty factor as a separate parameter because it is linked to Ton
and Toff. The chosen Ton and Toff values already provided a reasonable range of duty factors (38% to 54%), which
were sufficient to study the effects on machining performance. Parameters such as flushing pressure was not varied in
this study. Flushing pressure is crucial for debris removal. It was kept constant because the focus was on optimizing
electrical parameters rather than mechanical factors. The study assumed a constant flushing pressure that was sufficient
to maintain process stability.
Based on these input parameters, basic experimental runs were performed and data of output responses against input
factors were recorded. These basic experimental runs are mentioned in Table III. Basic experimental runs for AISI-
1045 on Table .
3.1 Workpiece preparation. - The workpieces (Figure ) used in these experiments consisted of two grounded blocks,
each with dimensions of 100 x 10 x 20 mm, secured in place using dowel pins. Electrode is of copper material (Figure
). Dielectric is of kerosene + C10 material.
Figure I. Parted Workpiece Snapshot before Machining.
Figure II. Copper Electrode Tip at 17 X prior to Machining.
3.2 Equipment used:
EDM machine = Genspark E5B1041
Weighing scale = AND GF-200 with least count of 0.001 g
Surface roughness tester = Wilson Wolpert CM T2
Microscope = Stereo microscope with 45X magnification with CMOS chip
EDM machine is available in Figure , weighing scale in Figure , surface roughness tester in Figure and microscope in
Figure .
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
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ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 228
Figure III. Genspark E5B1041.
Figure IV. Precision weighing scale AND GF-200.
Figure V. Surface Roughness Tester WW CM T2.
Figure VI. Stereo microscope.
EWR was calculated using the equation mentioned below in Equation 1:
𝐸𝑊𝑅 = 𝐸𝑏 −𝐸𝑎
𝑇𝑚 (𝑔
/min
)
Equation 1 Equation to calculate electrode wear rate
MRR was calculated using the equation mentioned below in Equation 2:
𝑀𝑅𝑅 = 𝑊𝑏 −𝑊𝑎
𝑇𝑚 (𝑔/min)
Equation 2 Equation to calculate material removal rate
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 229
3.3 Objectives. - The objectives of this experimental study are as follows:
1. To reduce the machining time (Tm) for AISI 1045 steel via the EDM technique.
2. To improve the material removal rate (MRR) during EDM machining of AISI 1045 steel.
3. To reduce the electrode wear rate (EWR) when treating AISI 1045 steel using EDM.
4. To improve surface roughness (Ra) on AISI 1045 steel with EDM.
5. Minimize variations in the base radius (R) of AISI 1045 steel machined with EDM.
3.4 Analysis of Variance (ANOVA). - The experimental data that was performed on the basis of basic experimental
runs as mentioned in Table was examined by using statistical techniques and conclusions were drawn based on the
significance of the components and their interactions. A 95% confidence interval was used and factors with the p-
values less than 0.05 were considered as significant. The normal plot of standardized effects was used to distinguish
between significant and non-significant components, whilst residual plots evaluated the model's fit. After that the
model was then refitted by removing the non-significant factors and a revised ANOVA table was created. Now this
revised ANOVA table consists of only significant factors and all non-significant factors are eliminated in order to
better understand the impact of input factors on output responses. Main effect plot and interaction plots were also
created. A high slope in the main effects plot showed significant factors, while non-parallel lines in the interaction plot
indicated significant relationships at the factor level. The Response Optimizer tool was used to perform optimization,
with targets such as response minimization, maximization or equating established. The desirability function was
studied by specifying lower, target and upper bounds, with a desirability (d) value near to 1 indicating that the response
is close to the target. The results (Table and Table ) produced optimal values for the major factors, their response
values and the desirability factor. The findings were validated by replicating the studies and the optimal solutions were
put into practice. Detailed experimental results and replicates are available in Appendix 7 and Appendix 8.
4. Results and Discussion. -
4.1 Experimental results and analysis. -
4.1.1 Optimized results for machining time (Tm). - The machining time (Tm) for AISI 1045 was optimized using
ANOVA in Minitab in which all the input factors were considered and their interaction with output responses were
calculated to determine significant causes to the variation in machining time. The purpose of this investigation was to
reduce machining time thereby increasing the efficiency of the EDM process. All detailed graphs and table are present
in Appendix 2. Machining pictures of electrode and workpiece are given in Appendix 9 and Appendix 10.
Initially all input parameters like pulse on time (Ton), pulse off time (Toff), current (LV) and voltage (HV) were
considered along with their interactions with output response of machining time. Significant factors were identified
using p-value and a 0.05 threshold for significance was considered. The p-value is used to determine the statistical
significance of each input factor and their interactions. A p-value less than 0.05 indicates that the factor or interaction
has a significant effect on the machining time (Tm). The ANOVA Table and normal probability plots (Figure and
Figure ) and residual plots (Figure and Figure ) showed that the input factor i.e. pulse on time (Ton), interaction
between pulse on time (Ton) and pulse off time (Toff) (Ton*Toff) and the three-factor interaction between LV, Ton
and Toff (LV*Ton*Toff) were statistically significant in terms of their influence on the output response which is
machining time. These significant input factors p-values are listed below (Table ):
Ton = 0.000
Ton*Toff = 0.021
LV*Ton*Toff = 0.024
The interaction between Ton and Toff is significant because it represents the balance between energy input and cooling
time. Longer Ton increases the energy delivered per pulse, leading to higher material removal rates (MRR), but it also
generates more heat, which can increase electrode wear and surface roughness. Toff, on the other hand, provides time
for cooling and debris removal. The study found that specific combinations of Ton and Toff can optimize MRR while
minimizing electrode wear and surface roughness. For example, a longer Ton combined with a slightly longer Toff
can enhance material removal efficiency without causing excessive heat buildup or debris accumulation. This
interaction highlights the need to carefully balance energy input and cooling to achieve optimal machining
performance.
This three-factor interaction is significant because it reflects the combined effect of current, pulse duration, and cooling
time on machining performance. Higher current (LV) increases the intensity of the electrical discharge, leading to
higher MRR, but it also increases electrode wear and surface roughness. When combined with longer Ton, the energy
input is further amplified, which can lead to excessive material removal and thermal damage if not balanced with an
appropriate Toff. The study found that optimizing this three-factor interaction can significantly reduce machining time
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
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(Tm) while maintaining acceptable levels of electrode wear and surface finish. For instance, a higher current combined
with longer Ton and a slightly longer Toff can maximize material removal efficiency while ensuring sufficient cooling
and debris removal. This interaction underscores the importance of coordinating current, pulse duration, and cooling
time to achieve a balance between productivity and quality.
The model was then refitted by eliminating non-significant factors as mentioned in Table . The revised model in main
effect plot (Figure ) and residual plot (Figure ) showed that both the model and the major factors Ton, Ton*Toff and
LV*Ton*Toff were still significant.
For machining time optimization, the target value was set to '0', while the upper bound value was set at 343 seconds,
which reflected the shortest observed machining time during the experiment. The desirability function in Figure was
used to calculate the optimized values of the machining time. The desirability function's target was set at '0' for
machining time to minimize processing duration, as shorter machining times are desirable for industrial efficiency.
The resulting desirability value (d = 0) suggested that the response (Tm) was far from the target value, implying that
the target of '0' was unsuitable for this particular response. The response was much below the highest limit (343
seconds), resulting in a lower desirability. If the target had been set closer to 600 seconds with a larger upper bound
(e.g., 1000 seconds), the desirability would have approached one, indicating a greater alignment with the optimization
goal.
For the optimized value of output response of Tm, following values of input factors came out to be significant where
lowest machining time was achieved.
Current (LV) = 30 A; Pulse on time (Ton) = 6.5 µs and pulse off time (Toff) = 5.0 µs.
The minimal machining time for these optimized parameters was found to be 623.2083 seconds.
Significant input factors that were calculated for output response of machining time shows both direct and inverse
relation Tm. Pulse on time (Ton) was found to be directly proportional to Tm and this shows that as Ton will increase
machining time will also increase which is understandable as longer Ton increases the energy input each for pulse thus
increasing the machining time. On the other hand, the interaction between Ton and pulse off time (Toff) (Ton*Toff)
showed a complex relationship because certain combinations of these two parameters resulted in shorter machining
times. Furthermore, the three-factor interaction (LV*Ton*Toff) showed that when current is considered along with
pulse on and off times overall machining time will reduce because more material will be removed from workpiece
surface as current is higher along with increased pulse duration. These interactions show that the Tm is highly sensitive
to both individual factors and their interactions and this suggests that the input parameters interactions must be carefully
adjusted to get the best and optimized results.
4.1.2 Optimized results for material removal rate (MRR). - The optimization of material removal rate (MRR) for
AISI 1045 was carried out using ANOVA in Minitab. All input factors, including pulse on time (Ton), pulse off time
(Toff), current (LV) and voltage (HV) were considered with the goal of maximizing MRR. Significant factors were
identified by examining the p-values in the ANOVA table, with a significance threshold of 0.05. All tables and figures
are present in Appendix 3.
From the ANOVA Table and the normal probability plot (Figure and Figure ) and residual plot (Figure and Figure ),
Ton and the interaction between Ton and Toff (Ton*Toff) were considered to be significant factors that are affecting
MRR. The p-values of significant factors are listed below (Table ):
Ton = 0.000
Ton*Toff = 0.034
Following this, the model was refitted by excluding non-significant factors and focusing only on Ton and Ton*Toff
interaction as mentioned in Table .
Now the main effects plot in Figure and interaction plot in Figure for MRR were prepared. The main effects plot
showed a steep slope for means showing the importance of Ton and Ton*Toff. Additionally, the interaction plot
revealed non-parallel lines, highlighting the significant interaction between Ton and Toff in calculating MRR.
The desirability function is a widely used approach in multi-objective optimization to convert multiple response
variables into a single composite desirability score, ranging from 0 (least desirable) to 1 (most desirable). In the
optimization of Material Removal Rate (MRR) for EDM machining of AISI 1045, the desirability function was
employed to determine the best combination of pulse on time (Ton) and pulse off time (Toff) that maximizes MRR
while ensuring process stability and efficiency. For the optimization of MRR, the target value was set to '1', while the
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
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lower bound was set at 0.0304 g/min, which represented the maximum observed MRR during the experiment. The
desirability function (Figure ) was used to assess how closely the optimized values aligned with the target. A
desirability value of d = 0 indicated that the response (MRR) was far from the set target of '1', suggesting that this
target was unrealistic for the given response. The response was much lower than the upper limit (0.0304 g/min),
resulting in a lower desirability. Had the target been set closer to 0.02 g/min with a lower upper limit (e.g., 0.001
g/min), the desirability would have approached one, indicating better alignment with the optimization objective.
The optimization process resulted in the following significant input factor values for maximizing MRR:
Ton (Pulse on time): 6.5 µs
Toff (Pulse off time): 5.5 µs
On these optimized settings, the maximum MRR achieved was 0.0173 g/min, which reflects the optimized material
removal under the given experimental conditions. This optimization highlights the critical influence of both Ton and
its interaction with Toff on the material removal rate during EDM machining of AISI 1045 steel.
Ton and MRR are directly proportional to each other because higher energy cycle will lead to more material removed
from the workpiece leading to higher MRR. While on the other hand interaction between Ton and Toff is complex in
nature. As Ton increased material removal rate, optimal time is needed so that the workpiece temperature of that
particular section cools down but not completely solidified between pulses ensuring efficient material removal.
4.1.3 Optimized results for electrode wear rate (EWR). - The analysis of Electrode Wear Rate (EWR) for AISI
1045 was carried out using ANOVA in Minitab. All input factors were considered including pulse on time (Ton), pulse
off time (Toff), current (LV) and voltage (HV). The objective of this analysis was to minimize the EWR thereby
increasing electrode life and improving overall machining efficiency. Significant factors affecting EWR were
identified by evaluating the p-values from the ANOVA table, with a threshold of 0.05 indicating statistical significance.
All detailed graphs and table are present in Appendix 4.
ANOVA Table , normal probability plot (Figure ) and residual plot (Figure and Figure ) showed that Ton was the
only significant factor affecting EWR and its p-value was 0.001 (Table ). Now the model was refitted by excluding all
the non-significant factors and only Ton as the primary influencing variable. The p-values in the revised ANOVA
Table confirmed that the refitted model, as well as the factor Ton, were statistically significant in determining the
variation in EWR.
Now the main effects plot (Figure ) and interaction plot (Figure ) were prepared for EWR. The main effects plot showed
a steep slope thus confirming the significance of Ton in influencing EWR. The interaction plot further showed non-
parallel lines meaning that interactions among other factors did not contribute significantly to EWR. This established
the fact that Ton as the key variable in this analysis.
For optimization of EWR, the target value was set to '0', while the upper bound value was established at 0.00551 g/min,
which represented the minimum observed EWR in the experiments. The desirability function (Figure ) was utilized to
determine how closely the optimized values aligned with the desired target. A desirability value of d = 0 showed that
the response (EWR) was far from the set target of '0', highlighting that this target was not practically attainable for this
specific response. The actual EWR was much below the upper bound, resulting in a lower desirability score. If the
target had been set closer to 0.009 g/min and the upper bound set to a larger value (e.g., 0.01 g/min), the desirability
would have approached one, signaling better alignment with the optimization goal.
Through the optimization process, the significant input factor (Ton) was determined to have the following optimized
value for minimizing EWR:
Pulse on time (Ton) = 4.0 µs
With this optimized Ton value, the minimum EWR was calculated to be 0.0088 g/min, reflecting the ideal electrode
wear rate achievable under these experimental conditions. This optimization highlights the critical role of Ton in
controlling electrode wear, as shorter pulse durations reduce electrode erosion, lowering the wear rate during EDM
machining of AISI 1045 steel. Optimized results for surface roughness (Ra)
The surface roughness (Ra) for AISI 1045 was analyzed using ANOVA in Minitab. The goal was to minimize the
surface roughness, thus improving the surface quality of the workpiece. All input factors, including voltage (HV),
pulse on time (Ton), pulse off time (Toff) and current (LV) were initially considered to identify significant input factors
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
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that can have possible impact on the output response i.e. surface roughness. The analysis was conducted by assessing
p-values from the ANOVA table, with a significance threshold set at 0.05. All detailed graphs and table are present in
Appendix 5.
ANOVA table (Table ), normal probability plot (Figure and Figure ) and residual plot (Figure and Figure ) showed
that the three-way interaction between HV, Ton and Toff (HV*Ton*Toff) was a significant factor affecting surface
roughness (Ra) and its p-value came out to be 0.039 (Table ). Now the model was refitted (Table ) by removing non-
significant factors, retaining only this three-way interaction as a significant input factor. The p-values from the revised
ANOVA table confirmed that the refitted model and the interaction HV*Ton*Toff remained significant for surface
roughness.
The interaction between voltage, Ton, and Toff is significant because it influences the spark gap and the energy
distribution during the EDM process. Lower voltages (HV) reduce the spark gap and the energy of each discharge,
leading to finer surface finishes but potentially lower MRR. When combined with longer Ton, the energy input is
increased, which can improve MRR but may also increase surface roughness if not balanced with an appropriate Toff.
The study found that optimizing this interaction can minimize surface roughness (Ra) by controlling the energy
delivered to the workpiece. For example, a lower voltage combined with longer Ton and a slightly longer Toff can
achieve a smoother surface finish by reducing the size of the craters formed during machining. This interaction
highlights the need to carefully adjust voltage, pulse duration, and cooling time to achieve the desired surface quality.
Now the main effects plot (Figure ) and interaction plot (Figure ) were prepared. The main effects plot showed steep
slopes of the means, proving the importance of the HV*Ton*Toff interaction on surface roughness. Additionally, the
interaction plot exhibited non-parallel lines, confirming that the interaction between these three factors had a significant
impact on the output response of Ra.
For optimizing Ra, the target value was set to '0' and the upper bound value was fixed at 0.01 mm, representing the
minimum observed value of surface roughness in the experiment. The desirability function (Figure ) was applied to
check how closely the optimized values aligned with the desired target. A desirability value of d = 0 indicated that the
response (Ra) was far from the target of '0', suggesting that the target was not feasible for this response. The response
was much lower than the upper bound (0.01 mm), resulting in lower desirability. If the target had been set closer to
0.025 mm, with a larger upper bound (e.g., 0.05 mm), the desirability would have approached one, indicating a more
realistic optimization scenario.
Based on this optimization, the following input factors were identified as the optimal values for minimizing surface
roughness (Ra):
Voltage (HV) = 0.70 V
Pulse on time (Ton) = 6.50 µs
Pulse off time (Toff) = 6.50 µs
With these optimized values, the minimum surface roughness (Ra) achieved was calculated to be 0.0253 mm. This
showed the effectiveness of optimizing these specific parameters for improving surface quality. The interaction of HV,
Ton and Toff shows that when voltage and pulse times are balanced, the energy delivered during the machining process
becomes more controlled, leading to smaller crater formation and resulting in a smoother surface finish and reduced
roughness.
4.1.4 Optimized results for base radius (R). - The base radius (R) for AISI 1045 steel was analyzed using ANOVA
in Minitab. The goal was to optimize the output response i.e. base radius. All input factors were considered initially
including pulse on time (Ton), pulse off time (Toff) and current (LV. The p-values from the ANOVA Table were
assessed, with a threshold of 0.05 for significance. All detailed graphs and table are present in Appendix 6.
Both the ANOVA table (Table ), normal probability plot (Figure and Figure ) showed that the interaction between LV
and Toff (LV*Toff), as well as the three-factor interaction LV, Ton and Toff (LV*Ton*Toff) were significant factors
effecting the base radius. The p-values of significant factors are listed below (Table ):
LV*Toff = 0.037
LV*Ton*Toff = 0.010
The interaction between current and Toff is significant because it reflects the relationship between the intensity of the
electrical discharge and the cooling time. Higher currents increase the energy of each spark, leading to higher MRR
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
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but also higher electrode wear and surface roughness. When combined with a longer Toff, the cooling time is increased,
which can help mitigate the thermal effects of higher currents. The study found that this interaction is particularly
important for achieving dimensional accuracy (base radius, R). For example, a higher current combined with a slightly
longer Toff can improve material removal efficiency while ensuring sufficient cooling to maintain dimensional
accuracy. This interaction emphasizes the need to balance current and cooling time to achieve both productivity and
precision.
Now the model (Table ) was refitted by eliminating the non-significant factors thus keeping only the significant
interactions. The p-values from the refitted ANOVA table confirmed that the revised model and these significant
interactions remained statistically valid for optimizing the base radius (R).
After identifying the significant factors, the main effects plot (Figure ) and interaction plot (Figure ) were generated.
The main effects plot displayed a steep slope of means, emphasizing the importance of the interactions between LV,
Ton and Toff on the base radius. Similarly, the interaction plot showed non-parallel lines, confirming that the
interactions between current, pulse on time and pulse off time had a significant impact on the output response (R).
For optimization, the target value for the base radius was set at 1.5 mm, with an upper bound of 1.55 mm and a lower
bound of 1.45 mm, reflecting the desired dimensions of the electrode. The desirability function (Figure ) was applied
to assess the closeness of the optimized values to the target. A desirability value of d = 0.40233 indicated that the
response (R) was about 40% closer to the target, showing a moderate alignment with the desired value of 1.5 mm.
Based on the optimization analysis, the following input parameters were determined to be optimal for achieving the
desired base radius:
LV (Current): 30 A
Ton (Pulse on time): 6.5 µs
Toff (Pulse off time): 5.5 µs
With these optimized input values, the base radius (R) was calculated to be 1.5298 mm, indicating a close match to the
target radius. The interactions between LV, Ton and Toff show that when the current and pulse times are balanced, the
material removal process is controlled more precisely thus enabling the electrode to achieve a base radius near the
desired dimensions. The non-parallel lines in the interaction plot further reinforce that these factors do not operate
independently, but in combination, they significantly influence the output response.
5. Conclusions. - This study aimed to optimize the electrical discharge machining (EDM) process for AISI 1045 steel.
A strict focus was on optimization of key output parameters such as machining time (Tm), material removal rate
(MRR), electrode wear rate (EWR), surface roughness (Ra) and base radius (R) by. By employing ANOVA analysis
in Minitab, significant input factors, including pulse on time (Ton), pulse off time (Toff), current (LV) and voltage
(HV), along with their interactions, were systematically analyzed to identify their impact on the five output responses
mentioned above. The results of this investigative study provide key insights into how each of these input parameters
impact on the five output responses, both individually and in combination, thereby contributing to a more efficient and
controlled manufacturing process with the final product being manufactured in less time with reduced material
wastages of both workpiece and electrodes and having excellent surface finish. Machining pictures of electrode and
workpiece are given in Appendix 9 and Appendix 10.
The optimization of machining time revealed that pulse on time (Ton), its interaction with pulse off time (Ton*Toff)
and the three-factor interaction between current, pulse on and pulse off times (LV*Ton*Toff) were the most significant
factors affecting Tm. The optimized values of Ton = 6.5 µs, Toff = 5.0 µs and LV = 30A resulted in a minimal
machining time of 623.2083 seconds. This showed that while Ton is directly proportional to machining time, specific
interactions with Toff and LV can lead to significant reductions in machining time by increasing material removal
efficiency.
Similarly, the optimization of the material removal rate (MRR) showed that Ton and the Ton*Toff interaction were
significant factors. The optimized parameters, Ton = 6.5 µs and Toff = 5.5 µs, resulted in a maximum MRR of 0.0173
g/min. The relationship between Ton and MRR was found to be directly proportional. This means that with longer
pulse durations and higher energy input will lead to more material removal. However, the interaction with Toff required
precise timing to ensure that sufficient material was removed without cooling down or solidifying between pulses thus
showing the complexity of achieving maximum MRR.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
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Electrode wear rate (EWR) optimization highlighted that pulse on time (Ton) was the sole significant factor influencing
EWR. The optimized value of Ton = 4.0 µs yielded a minimal EWR of 0.0088 g/min, showcasing that shorter pulse
durations reduce electrode erosion and prolong electrode life. This result emphasizes the importance of balancing Ton
to minimize wear while maintaining machining efficiency.
Surface roughness (Ra) analysis revealed that the interaction between voltage (HV), Ton and Toff (HV*Ton*Toff)
was critical in determining surface quality. The optimized values of HV = 0.7V0 V, Ton = 6.5 µs and Toff = 6.5 µs
achieved a minimum Ra of 0.0253 mm. This optimization demonstrated that fine control over these interactions reduces
crater formation during machining, leading to smoother surface finishes.
Lastly, the base radius (R) optimization showed that the interaction between current (LV) and pulse off time (Toff)
(LV*Toff), as well as the three-factor interaction between LV, Ton and Toff (LV*Ton*Toff), were significant in
achieving the desired base radius. The optimized values of LV = 30A A, Ton = 6.5 µs and Toff = 5.5 µs resulted in a
base radius of 1.5298 mm, closely aligning with the target of 1.5 mm. These findings demonstrate that the precise
adjustment of current and pulse timing significantly enhances the dimensional accuracy of the electrode.
In conclusion, this research provides a comprehensive optimization framework for EDM machining of AISI 1045 steel,
addressing the critical parameters that influence machining efficiency, quality and precision. By understanding the
complex interactions between input parameters, this study offers valuable guidelines for achieving desired machining
outcomes while minimizing defects and inefficiencies. The application of these findings in industrial EDM processes
can lead to significant improvements in productivity, material usage and overall machining quality.
6. Limitations of the study. - The study has several limitations, including its focus on only four input parameters
(Ton, Toff, LV, HV) and AISI 1045 steel, which limits its applicability to other materials and conditions. It did not
explore factors like flushing pressure, electrode geometry, or dielectric fluid variations, nor did it consider surface
integrity aspects such as recast layer thickness or residual stresses. The experiments were conducted under controlled
laboratory conditions, potentially limiting real-world applicability, and the reliance on statistical methods like RSM
may not capture complex, non-linear interactions. Additionally, the study did not address economic or environmental
impacts, such as cost-effectiveness or the use of kerosene as a dielectric fluid, nor did it compare EDM with other
machining methods. These limitations suggest areas for future research to enhance the study's robustness and industrial
relevance.
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[26] Application of Response Surface Methodology For Determining MRR and TWR Model In Die Sinking EDM of
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M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
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Appendix 1
RUN
LV
HV
PULSE ON TIME
PULSE OFF TIME
1
0.3
30
4
5.5
2
0.7
30
4
5.5
3
0.3
50
4
5.5
4
0.7
50
4
5.5
5
0.3
30
6.5
5.5
6
0.7
30
6.5
5.5
7
0.3
50
6.5
5.5
8
0.7
50
6.5
5.5
9
0.3
30
4
6.5
10
0.7
30
4
6.5
11
0.3
50
4
6.5
12
0.7
50
4
6.5
13
0.3
30
6.5
6.5
14
0.7
30
6.5
6.5
15
0.3
50
6.5
6.5
16
0.7
50
6.5
6.5
Table III. Basic experimental runs for AISI-1045
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Appendix 2
Table IV. ANOVA Table of Tm for AISI-1045 considering all factors.
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Figure VII. Normal Probability Plot of the standardized effects of Tm for AISI-1045 considering all factors.
Figure VIII. Residual Plot of Tm for AISI-1045 considering all factors.
Factorial Fit: Tm versus LV, Ton, Toff
Estimated Effects and Coefficients for Tm (coded units)
Term Effect Coef SE Coef T P
Constant 845.6 23.65 35.76 0.000
LV -60.8 -30.4 23.65 -1.29 0.206
Ton -261.0 -130.5 23.65 -5.52 0.000
Toff 21.0 10.5 23.65 0.44 0.660
Ton*Toff 113.3 56.6 23.65 2.40 0.021
LV*Ton*Toff -110.4 -55.2 23.65 -2.33 0.024
S = 163.841 R-Sq = 50.86% R-Sq(adj) = 45.01%
Analysis of Variance for Tm (coded units)
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Source DF Seq SS AdjSS MS F P
Main Effects 3 866810 866810 288937 10.76 0.000
2-Way Interactions 1 154020 154020 154020 5.74 0.021
3-Way Interactions 1 146192 146192 146192 5.45 0.024
Residual Error 42 1127448 1127448 26844
Lack of Fit 2 157216 157216 78608 3.24 0.050
Pure Error 40 970233 970233 24256
Total 47 2294469
Unusual Observations for Tm
ObsStdOrder Tm Fit SE Fit Residual St Resid
4 4 668.00 1022.80 59.92 -354.80 -2.33R
9 9 495.00 942.40 55.34 -447.40 -2.90R
R denotes an observation with a large standardized residual.
Estimated Coefficients for Tm using data in uncoded units
Term Coef
Constant 3833.95
LV 7.22155
Ton -648.183
Toff -454.867
Ton*Toff 103.663
LV*Ton*Toff -0.325750
Table V. ANOVA Table of Tm for AISI-1045 considering significant factors.
Figure IX. Normal Probability Plot of the standardized effects of Tm for AISI-1045 considering significant factors.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
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Figure X. Residual Plot of Tm for AISI-1045 considering all factors.
Figure XI. Main Effects Plot of Tm for AISI-1045 considering significant factors.
Figure XII. Residual Plot of Tm for AISI-1045 considering Ton & LV.
Figure XIII. Optimization Plot of Tm for AISI-1045 for significant factors
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Appendix 3
Factorial Fit: MRR versus HV, LV, Ton, Toff
Estimated Effects and Coefficients for MRR (coded units)
Term Effect Coef SE Coef T P
Constant 0.012883 0.000575 22.41 0.000
HV 0.000160 0.000080 0.000575 0.14 0.890
LV 0.001598 0.000799 0.000575 1.39 0.174
Ton 0.004607 0.002304 0.000575 4.01 0.000
Toff -0.001590 -0.000795 0.000575 -1.38 0.176
HV*LV 0.000896 0.000448 0.000575 0.78 0.441
HV*Ton 0.000844 0.000422 0.000575 0.73 0.468
HV*Toff -0.000010 -0.000005 0.000575 -0.01 0.993
LV*Ton 0.001988 0.000994 0.000575 1.73 0.093
LV*Toff -0.001694 -0.000847 0.000575 -1.47 0.151
Ton*Toff -0.002541 -0.001271 0.000575 -2.21 0.034
HV*LV*Ton 0.000405 0.000202 0.000575 0.35 0.727
HV*LV*Toff 0.000083 0.000041 0.000575 0.07 0.943
HV*Ton*Toff 0.000814 0.000407 0.000575 0.71 0.484
LV*Ton*Toff 0.000966 0.000483 0.000575 0.84 0.407
HV*LV*Ton*Toff 0.000795 0.000397 0.000575 0.69 0.495
S = 0.00398335 R-Sq = 50.71% R-Sq(adj) = 27.60%
Analysis of Variance for MRR (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 4 0.00031597 0.00031597 0.00007899 4.98 0.003
2-Way Interactions 6 0.00017753 0.00017753 0.00002959 1.86 0.118
3-Way Interactions 4 0.00002120 0.00002120 0.00000530 0.33 0.853
4-Way Interactions 1 0.00000758 0.00000758 0.00000758 0.48 0.495
Residual Error 32 0.00050775 0.00050775 0.00001587
Pure Error 32 0.00050775 0.00050775 0.00001587
Total 47 0.00103002
Unusual Observations for MRR
ObsStdOrder MRR Fit SE Fit Residual StResid
8 8 0.031840 0.019720 0.002300 0.012120 3.73R
24 24 0.012790 0.019720 0.002300 -0.006930 -2.13R
R denotes an observation with a large standardized residual.
Table VI. ANOVA Table of MRR for AISI-1045 considering all factors.
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Figure XIV. Normal Probability Plot of the standardized effects of MRR for AISI-1045 considering all factors.
Figure XV. Residual Plot of MRR for AISI-1045 considering all factors.
Factorial Fit: MRR versus Ton, Toff
Estimated Effects and Coefficients for MRR (coded units)
Term Effect Coef SE Coef T P
Constant 0.012883 0.000562 22.92 0.000
Ton 0.004607 0.002304 0.000562 4.10 0.000
Toff -0.001590 -0.000795 0.000562 -1.41 0.164
Ton*Toff -0.002541 -0.001271 0.000562 -2.26 0.029
S = 0.00389492 R-Sq = 35.20% R-Sq(adj) = 30.78%
Analysis of Variance for MRR (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 2 0.00028502 0.00028502 0.00014251 9.39 0.000
2-Way Interactions 1 0.00007750 0.00007750 0.00007750 5.11 0.029
Residual Error 44 0.00066750 0.00066750 0.00001517
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Pure Error 44 0.00066750 0.00066750 0.00001517
Total 47 0.00103002
Unusual Observations for MRR
ObsStdOrder MRR Fit SE Fit Residual StResid
8 8 0.031840 0.017252 0.001124 0.014588 3.91R
9 9 0.019150 0.011055 0.001124 0.008095 2.17R
32 32 0.022470 0.013121 0.001124 0.009349 2.51R
37 37 0.009300 0.017252 0.001124 -0.007952 -2.13R
R denotes an observation with a large standardized residual.
Estimated Coefficients for MRR using data in uncoded units
Term Coef
Constant -0.0512942
Table VII. ANOVA Table of MRR for AISI-1045 considering significant factors.
Figure XVI. Normal Probability Plot of the standardized effects of MRR for AISI-1045 considering significant
factors
Figure XVII. Residual Plot of MRR for AISI-1045 considering all factors.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
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Figure XVIII. Main Effects Plot of MRR for AISI-1045 considering significant factors.
Figure XIX. Residual Plot of MRR for AISI-1045 considering Ton & LV.
Figure XX. Optimization Plot of MRR for AISI-1045 for significant factors.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
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Appendix 4
Factorial Fit: EWR versus HV, LV, Ton, Toff
Estimated Effects and Coefficients for EW (coded units)
Term Effect Coef SE Coef T P
Constant 0.010172 0.000382 26.66 0.000
HV -0.000559 -0.000279 0.000382 -0.73 0.469
LV 0.001468 0.000734 0.000382 1.92 0.063
Ton 0.002742 0.001371 0.000382 3.59 0.001
Toff -0.000344 -0.000172 0.000382 -0.45 0.655
HV*LV 0.000028 0.000014 0.000382 0.04 0.971
HV*Ton 0.000919 0.000459 0.000382 1.20 0.238
HV*Toff 0.000263 0.000131 0.000382 0.34 0.733
LV*Ton 0.001214 0.000607 0.000382 1.59 0.122
LV*Toff -0.001214 -0.000607 0.000382 -1.59 0.122
Ton*Toff -0.001386 -0.000693 0.000382 -1.82 0.079
HV*LV*Ton 0.000284 0.000142 0.000382 0.37 0.713
HV*LV*Toff 0.000583 0.000291 0.000382 0.76 0.451
HV*Ton*Toff 0.000227 0.000114 0.000382 0.30 0.768
LV*Ton*Toff 0.001205 0.000603 0.000382 1.58 0.124
HV*LV*Ton*Toff -0.000048 -0.000024 0.000382 -0.06 0.950
S = 0.00264388 PRESS = 0.000503286
R-Sq = 48.87% R-Sq(pred) = 0.00% R-Sq(adj) = 24.90%
Analysis of Variance for EW (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 4 0.00012125 0.00012125 0.00003031 4.34 0.006
2-Way Interactions 6 0.00006938 0.00006938 0.00001156 1.65 0.165
3-Way Interactions 4 0.00002310 0.00002310 0.00000577 0.83 0.518
4-Way Interactions 1 0.00000003 0.00000003 0.00000003 0.00 0.950
Residual Error 32 0.00022368 0.00022368 0.00000699
Pure Error 32 0.00022368 0.00022368 0.00000699
Total 47 0.00043744
Unusual Observations for EW
ObsStdOrder EW Fit SE Fit Residual St Resid
8 8 0.019770 0.013577 0.001526 0.006193 2.87R
9 9 0.016610 0.011313 0.001526 0.005297 2.45R
R denotes an observation with a large standardized residual.
Table VIII. ANOVA Table of EWR for AISI-1045 considering all factors.
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Figure XXI. Residual Plot of EW for AISI-1045 considering all factors.
Factorial Fit: EWR versus Ton
Estimated Effects and Coefficients for EW (coded units)
Term Effect Coef SE Coef T P
Constant 0.010172 0.000397 25.65 0.000
Ton 0.002742 0.001371 0.000397 3.46 0.001
S = 0.00274739 PRESS = 0.000378065
R-Sq = 20.63% R-Sq(pred) = 13.57% R-Sq(adj) = 18.90%
Analysis of Variance for EW (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 1 0.00009023 0.00009023 0.00009023 11.95 0.001
Residual Error 46 0.00034722 0.00034722 0.00000755
Pure Error 46 0.00034722 0.00034722 0.00000755
Total 47 0.00043744
Unusual Observations for EW
ObsStdOrder EW Fit SE Fit Residual St Resid
8 8 0.019770 0.011543 0.000561 0.008227 3.06R
9 9 0.016610 0.008801 0.000561 0.007809 2.90R
R denotes an observation with a large standardized residual.
Estimated Coefficients for EW using data in uncoded units
Term Coef
Constant 0.00441392
Ton 0.00109683
Table IX. Table of EWR for AISI-1045 considering significant factors.
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Figure XXII. Normal Probability Plot of the standardized effects ofAISI-1045 for AISI 1045 considering significant
factors.
Figure XXIII. Residual Plot of EWR for AISI-1045 considering all factors.
Figure XXIV. Main Effects Plot of EWR for AISI-1045 considering significant factors.
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Figure XXV. Residual Plot of EWR for AISI-1045 considering Ton & LV.
Figure XXVI. Optimization Plot of EWR for AISI-1045 for significant factors.
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Appendix 5
Factorial Fit: Ra versus HV, LV, Ton, Toff
Estimated Effects and Coefficients for Ra (coded units)
Term EffectCoef SE Coef T P
Constant 0.03696 0.004893 7.55 0.000
HV 0.00167 0.00083 0.004893 0.17 0.866
LV -0.00367 -0.00183 0.004893 -0.37 0.710
Ton -0.00175 -0.00088 0.004893 -0.18 0.859
Toff -0.00217 -0.00108 0.004893 -0.22 0.826
HV*LV 0.00675 0.00337 0.004893 0.69 0.495
HV*Ton 0.01433 0.00717 0.004893 1.46 0.153
HV*Toff 0.01158 0.00579 0.004893 1.18 0.245
LV*Ton 0.01183 0.00592 0.004893 1.21 0.235
LV*Toff 0.00358 0.00179 0.004893 0.37 0.717
Ton*Toff 0.00483 0.00242 0.004893 0.49 0.625
HV*LV*Ton -0.00908 -0.00454 0.004893 -0.93 0.360
HV*LV*Toff -0.01467 -0.00733 0.004893 -1.50 0.144
HV*Ton*Toff -0.02108 -0.01054 0.004893 -2.15 0.039
LV*Ton*Toff -0.00858 -0.00429 0.004893 -0.88 0.387
HV*LV*Ton*Toff 0.01050 0.00525 0.004893 1.07 0.291
S = 0.0339027 PRESS = 0.0827565
R-Sq = 33.03% R-Sq(pred) = 0.00% R-Sq(adj) = 1.63%
Analysis of Variance for Ra (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 4 0.0002878 0.0002878 0.00007194 0.06 0.992
2-Way Interactions 6 0.0067369 0.0067369 0.00112282 0.98 0.457
3-Way Interactions 4 0.0097896 0.0097896 0.00244740 2.13 0.100
4-Way Interactions 1 0.0013230 0.0013230 0.00132300 1.15 0.291
Residual Error 32 0.0367807 0.0367807 0.00114940
Pure Error 32 0.0367807 0.0367807 0.00114940
Total 47 0.0549179
Unusual Observations for Ra
ObsStdOrder Ra Fit SE Fit Residual StResid
1 1 0.042000 0.098333 0.019574 -0.056333 -2.04R
17 17 0.230000 0.098333 0.019574 0.131667 4.76R
24 24 0.111000 0.055000 0.019574 0.056000 2.02R
33 33 0.023000 0.098333 0.019574 -0.075333 -2.72R
R denotes an observation with a large standardized residual.
Table X. ANOVA Table of Ra for AISI-1045 considering all factors.
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Figure XXVII. Normal Probability Plot of the standardized effects of Ra for AISI-1045 considering all factors.
Figure XXVIII. Residual Plot of Ra for AISI-1045 considering all factors.
Factorial Fit: Ra versus HV, Ton, Toff
Estimated Effects and Coefficients for Ra (coded units)
Term Effect Coef SE Coef T P
Constant 0.03696 0.004895 7.55 0.000
HV 0.00167 0.00083 0.004895 0.17 0.866
Ton -0.00175 -0.00088 0.004895 -0.18 0.859
Toff -0.00217 -0.00108 0.004895 -0.22 0.826
HV*Ton*Toff -0.02108 -0.01054 0.004895 -2.15 0.037
S = 0.0339142 PRESS = 0.0616278
R-Sq = 9.94% R-Sq(pred) = 0.00% R-Sq(adj) = 1.57%
Analysis of Variance for Ra (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 3 0.0001264 0.0001264 0.00004214 0.04 0.990
3-Way Interactions 1 0.0053341 0.0053341 0.00533408 4.64 0.037
Residual Error 43 0.0494574 0.0494574 0.00115017
Lack of Fit 3 0.0043558 0.0043558 0.00145192 1.29 0.292
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Pure Error 40 0.0451017 0.0451017 0.00112754
Total 47 0.0549179
Unusual Observations for Ra
ObsStdOrder Ra Fit SE Fit Residual St Resid
17 17 0.230000 0.048625 0.010946 0.181375 5.65R
R denotes an observation with a large standardized residual.
* NOTE * Estimated regression coefficients in uncoded units are not available
because the model is non-hierarchical.
Table XI. ANOVA Table of Ra for AISI-1045 considering significant factors.
Figure XXIX. Normal Probability Plot of the standardized effects of Ra for AISI-1045 considering significant factors.
Figure XXX. Residual Plot of Ra for AISI-1045 considering significant factors.
Figure XXXI. Main Effects Plot of Ra for AISI-1045 considering significant factors.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 253
Figure XXXII. Residual Plot of Ra for AISI-1045 considering HV, Ton &Toff.
Figure XXXIII. Optimization Plot of Ra for AISI-1045 for significant factors.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 254
Appendix 6
Factorial Fit: R versus HV, LV, Ton, Toff
Estimated Effects and Coefficients for R (coded units)
Term Effect Coef SE Coef T P
Constant 1.55752 0.004904 317.62 0.000
HV -0.00396 -0.00198 0.004904 -0.40 0.689
LV 0.00754 0.00377 0.004904 0.77 0.448
Ton 0.00304 0.00152 0.004904 0.31 0.758
Toff 0.00246 0.00123 0.004904 0.25 0.804
HV*LV -0.00196 -0.00098 0.004904 -0.20 0.843
HV*Ton 0.00421 0.00210 0.004904 0.43 0.671
HV*Toff -0.00521 -0.00260 0.004904 -0.53 0.599
LV*Ton 0.00304 0.00152 0.004904 0.31 0.758
LV*Toff -0.02138 -0.01069 0.004904 -2.18 0.037
Ton*Toff 0.00096 0.00048 0.004904 0.10 0.923
HV*LV*Ton 0.01504 0.00752 0.004904 1.53 0.135
HV*LV*Toff 0.01046 0.00523 0.004904 1.07 0.294
HV*Ton*Toff -0.00487 -0.00244 0.004904 -0.50 0.623
LV*Ton*Toff -0.02704 -0.01352 0.004904 -2.76 0.010
HV*LV*Ton*Toff -0.00271 -0.00135 0.004904 -0.28 0.784
S = 0.0339739 PRESS = 0.0831045
R-Sq = 35.60% R-Sq(pred) = 0.00% R-Sq(adj) = 5.41%
Analysis of Variance for R (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 4 0.0010541 0.0010541 0.00026352 0.23 0.920
2-Way Interactions 6 0.0061888 0.0061888 0.00103147 0.89 0.511
3-Way Interactions 4 0.0130878 0.0130878 0.00327194 2.83 0.040
4-Way Interactions 1 0.0000880 0.0000880 0.00008802 0.08 0.784
Residual Error 32 0.0369353 0.0369353 0.00115423
Pure Error 32 0.0369353 0.0369353 0.00115423
Total 47 0.0573540
Unusual Observations for R
ObsStdOrder R Fit SE Fit Residual St Resid
21 21 1.58300 1.52533 0.01961 0.05767 2.08R
36 36 1.61200 1.53567 0.01961 0.07633 2.75R
R denotes an observation with a large standardized residual.
Table XII. ANOVA Table of R for AISI-1045 considering all factors.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
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ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 255
Figure XXXIV. Normal Probability Plot of the standardized effects of R for AISI-1045 considering all factors.
Figure XXXV. Residual Plot of R for AISI-1045 considering all factors.
Factorial Fit: R versus LV, Ton, Toff
Estimated Effects and Coefficients for R (coded units)
Term Effect Coef SE Coef T P
Constant 1.55752 0.004577 340.30 0.000
LV 0.00754 0.00377 0.004577 0.82 0.415
Ton 0.00304 0.00152 0.004577 0.33 0.741
Toff 0.00246 0.00123 0.004577 0.27 0.790
LV*Toff -0.02138 -0.01069 0.004577 -2.34 0.024
LV*Ton*Toff -0.02704 -0.01352 0.004577 -2.95 0.005
S = 0.0317093 PRESS = 0.0551578
R-Sq = 26.37% R-Sq(pred) = 3.83% R-Sq(adj) = 17.60%
Analysis of Variance for R (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 3 0.0008661 0.0008661 0.00028869 0.29 0.834
2-Way Interactions 1 0.0054827 0.0054827 0.00548269 5.45 0.024
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 256
3-Way Interactions 1 0.0087750 0.0087750 0.00877502 8.73 0.005
Residual Error 42 0.0422302 0.0422302 0.00100548
Lack of Fit 2 0.0001220 0.0001220 0.00006102 0.06 0.944
Pure Error 40 0.0421082 0.0421082 0.00105270
Total 47 0.0573540
Unusual Observations for R
ObsStdOrder R Fit SE Fit Residual St Resid
3 3 1.62200 1.55571 0.01121 0.06629 2.23R
4 4 1.48900 1.55571 0.01121 -0.06671 -2.25R
34 34 1.61800 1.55383 0.01121 0.06417 2.16R
R denotes an observation with a large standardized residual.
* NOTE * Estimated regression coefficients in uncoded units are not available
because the model is non-hierarchical.
Table XIII. ANOVA Table of R for AISI-1045 considering significant factors.
Figure XXXVI. Normal Probability Plot of the standardized effects of R for AISI-1045 considering significant
factors.
Figure XXXVII. Residual Plot of R for AISI-1045 considering all factors.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 257
Figure XXXVIII. Main Effects Plot of R for AISI-1045 considering significant factors.
Figure XXXIX. Residual Plot of R for AISI-1045 considering Ton, Toff& LV.
Figure XL. Optimization Plot of R for AISI-1045 for significant factors
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 258
Appendix 7
Run
Order
HV
LV
Ton
Toff
Work
Piece
Material
Ea
Wa
Duty
Factor %
1
0.3
30
4
5.5
AISI-
1045
9.356
258.952
42%
2
0.7
30
4
5.5
AISI-
1045
9.216
258.786
42%
3
0.3
50
4
5.5
AISI-
1045
9.705
258.62
42%
4
0.7
50
4
5.5
AISI-
1045
9.565
258.468
42%
5
0.3
30
6.5
5.5
AISI-
1045
9.833
258.312
54%
6
0.7
30
6.5
5.5
AISI-
1045
9.712
258.163
54%
7
0.3
50
6.5
5.5
AISI-
1045
9.382
257.965
54%
8
0.7
50
6.5
5.5
AISI-
1045
9.259
257.792
54%
9
0.3
30
4
6.5
AISI-
1045
9.389
257.61
38%
10
0.7
30
4
6.5
AISI-
1045
9.252
257.452
38%
11
0.3
50
4
6.5
AISI-
1045
9.172
257.28
38%
12
0.7
50
4
6.5
AISI-
1045
9.034
257.12
38%
13
0.3
30
6.5
6.5
AISI-
1045
9.787
256.953
50%
14
0.7
30
6.5
6.5
AISI-
1045
9.652
256.785
50%
15
0.3
50
6.5
6.5
AISI-
1045
9.505
256.606
50%
16
0.7
50
6.5
6.5
AISI-
1045
9.362
256.452
50%
17
0.3
30
4
5.5
AISI-
1045
12.067
261.575
42%
18
0.7
30
4
5.5
AISI-
1045
11.941
261.397
42%
19
0.3
50
4
5.5
AISI-
1045
9.585
261.249
42%
20
0.7
50
4
5.5
AISI-
1045
9.439
261.076
42%
21
0.3
30
6.5
5.5
AISI-
1045
9.223
260.91
54%
22
0.7
30
6.5
5.5
AISI-
1045
9.098
260.725
54%
23
0.3
50
6.5
5.5
AISI-
1045
9.622
260.565
54%
24
0.7
50
6.5
5.5
AISI-
1045
9.49
260.386
54%
25
0.3
30
4
6.5
AISI-
1045
9.519
260.217
38%
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 259
26
0.7
30
4
6.5
AISI-
1045
9.375
260.052
38%
27
0.3
50
4
6.5
AISI-
1045
8.112
259.909
38%
28
0.7
50
4
6.5
AISI-
1045
7.966
259.763
38%
29
0.3
30
6.5
6.5
AISI-
1045
9.219
259.615
50%
30
0.7
30
6.5
6.5
AISI-
1045
9.092
259.448
50%
31
0.3
50
6.5
6.5
AISI-
1045
9.199
259.297
50%
32
0.7
50
6.5
6.5
AISI-
1045
9.056
259.131
50%
33
0.3
30
4
5.5
AISI-
1045
12.453
249.045
42%
34
0.7
30
4
5.5
AISI-
1045
12.332
248.884
42%
35
0.3
50
4
5.5
AISI-
1045
12.398
248.713
42%
36
0.7
50
4
5.5
AISI-
1045
12.167
248.542
42%
37
0.3
30
6.5
5.5
AISI-
1045
12.706
248.345
54%
38
0.7
30
6.5
5.5
AISI-
1045
12.591
248.198
54%
39
0.3
50
6.5
5.5
AISI-
1045
18.431
248.042
54%
40
0.7
50
6.5
5.5
AISI-
1045
18.299
247.864
54%
41
0.3
30
4
6.5
AISI-
1045
12.718
247.694
38%
42
0.7
30
4
6.5
AISI-
1045
12.58
247.524
38%
43
0.3
50
4
6.5
AISI-
1045
13.194
247.354
38%
44
0.7
50
4
6.5
AISI-
1045
13.064
247.172
38%
45
0.3
30
6.5
6.5
AISI-
1045
12.272
246.999
50%
46
0.7
30
6.5
6.5
AISI-
1045
12.137
246.83
50%
47
0.3
50
6.5
6.5
AISI-
1045
12.041
246.66
50%
48
0.7
50
6.5
6.5
AISI-
1045
11.905
246.506
50%
Table XIV. Experimental inputs for AISI-1045 material with three replicates.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
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Appendix 8
Run Order
Tm (sec)
MRR (g/min)
EW (g/min)
Base Radius
(mm)
Surface Roughness
(Ra)
(mm)
1
1094
0.0091
0.00768
1.56
0.042
2
1149
0.00867
0.00674
1.546
0.012
3
1034
0.00882
0.00812
1.622
0.014
4
668
0.01401
0.01105
1.489
0.04
5
602
0.01485
0.01126
1.495
0.013
6
779
0.01525
0.00986
1.575
0.038
7
505
0.02055
0.01461
1.554
0.042
8
343
0.03184
0.01977
1.586
0.033
9
495
0.01915
0.01661
1.594
0.026
10
729
0.01416
0.01111
1.536
0.057
11
1021
0.0094
0.00811
1.566
0.029
12
1124
0.00891
0.00817
1.543
0.052
13
822
0.01226
0.00985
1.598
0.02
14
867
0.01239
0.00941
1.568
0.043
15
720
0.01283
0.01192
1.564
0.038
16
694
0.01418
0.01141
1.551
0.023
17
957
0.01116
0.0079
1.568
0.23
18
1055
0.00842
0.00671
1.537
0.011
19
800
0.01298
0.01095
1.548
0.056
20
822
0.01212
0.00869
1.506
0.017
21
517
0.02147
0.01451
1.583
0.018
22
506
0.01897
0.01494
1.513
0.028
23
719
0.01494
0.01102
1.576
0.031
24
793
0.01279
0.00968
1.61
0.111
25
904
0.01095
0.00956
1.558
0.024
26
745
0.01152
0.00999
1.543
0.069
27
943
0.00929
0.00929
1.55
0.027
28
1069
0.00831
0.00769
1.566
0.03
29
772
0.01298
0.00987
1.586
0.05
30
813
0.01114
0.00989
1.536
0.033
31
789
0.01262
0.01087
1.508
0.036
32
478
0.02247
0.01607
1.535
0.025
33
1173
0.00824
0.00619
1.506
0.023
34
1248
0.00822
0.00644
1.618
0.013
35
1036
0.0099
0.01338
1.551
0.019
36
1231
0.0096
0.00551
1.612
0.019
37
948
0.0093
0.00728
1.498
0.018
38
872
0.01073
0.00853
1.503
0.054
39
490
0.0218
0.01616
1.607
0.01
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
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40
702
0.01453
0.01128
1.588
0.021
41
1066
0.00957
0.00777
1.51
0.014
42
1007
0.01013
0.00739
1.569
0.026
43
874
0.01249
0.00892
1.588
0.026
44
1182
0.00878
0.00726
1.558
0.032
45
976
0.01039
0.0083
1.601
0.018
46
928
0.01099
0.00873
1.589
0.051
47
756
0.01222
0.01071
1.537
0.043
48
772
0.01298
0.01111
1.556
0.069
Table XV. Experimental Responses for AISI-1045along with three Replicates.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
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Appendix 9. - The AISI-1045 workpiece surface outline images were taken at a magnification level of 22X
Experimental
Runs
Replicate # 1
Replicate # 2
Replicate # 3
1
Ton = 4µs,Toff5.5 µs,Duty Factor = 42%, HV = 0.3V, LV = 30A
2
Ton = 4µs,Toff5.5 µs,Duty Factor = 42%, HV = 0.7V, LV = 30A
3
Ton = 4µs,Toff5.5 µs,Duty Factor = 42%, HV = 0.3V, LV = 50A
4
Ton = 4µs,Toff5.5 µs,Duty Factor = 42%, HV = 0.7V, LV = 50A
5
Ton = 6.5µs,Toff5.5 µs,Duty Factor = 54%, HV = 0.3V, LV = 30A
6
Ton = 6.5µs,Toff5.5 µs,Duty Factor = 54%, HV = 0.7V, LV = 30A
7
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 263
Experimental
Runs
Replicate # 1
Replicate # 2
Replicate # 3
Ton = 6.5µs,Toff5.5 µs,Duty Factor = 54%, HV = 0.3V, LV = 50A
8
Ton = 6.5µs,Toff5.5 µs,Duty Factor = 54%, HV = 0.7V, LV = 50A
9
Ton = 4µs,Toff6.5 µs,Duty Factor = 38%, HV = 0.3V, LV = 30A
10
Ton = 4µs,Toff6.5 µs,Duty Factor = 38%, HV = 0.7V, LV = 30A
11
Ton = 4µs,Toff6.5 µs,Duty Factor = 38%, HV = 0.3V, LV = 50A
12
Ton = 4µs,Toff6.5 µs,Duty Factor = 38%, HV = 0.7V, LV = 50A
13
Ton = 6.5µs,Toff6.5 µs,Duty Factor = 50%, HV = 0.3V, LV = 30A
14
Ton = 6.5µs,Toff6.5 µs,Duty Factor = 50%, HV = 0.7V, LV = 30A
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 264
Experimental
Runs
Replicate # 1
Replicate # 2
Replicate # 3
15
Ton = 6.5µs,Toff6.5 µs,Duty Factor = 50%, HV = 0.3V, LV = 50A
16
Ton = 6.5µs,Toff6.5 µs,Duty Factor = 50%, HV = 0.7V, LV = 50A
Table XVI. AISI 1045 Workpiece Outline 22X.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
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Appendix 10. - The electrode images that machined AISI-1045 were taken at a magnification level of 20 X
Experimental
Runs
Replicate # 1
Replicate # 2
Replicate # 3
1
Ton = 4µs,Toff5.5 µs,Duty Factor = 42%, HV = 0.3, LV = 30
2
Ton = 4µs,Toff5.5 µs,Duty Factor = 42%, HV = 0.7, LV = 30
3
Ton = 4µs,Toff5.5 µs,Duty Factor = 42%, HV = 0.3, LV = 50
4
Ton = 4µs,Toff5.5 µs,Duty Factor = 42%, HV = 0.7, LV = 50
5
Ton = 6.5µs,Toff5.5 µs,Duty Factor = 54%, HV = 0.3, LV = 30
6
Ton = 6.5µs,Toff5.5 µs,Duty Factor = 54%, HV = 0.7, LV = 30
7
Ton = 6.5µs,Toff5.5 µs,Duty Factor = 54%, HV = 0.3, LV = 50
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 266
Experimental
Runs
Replicate # 1
Replicate # 2
Replicate # 3
8
Ton = 6.5µs,Toff5.5 µs,Duty Factor = 54%, HV = 0.7, LV = 50
9
Ton = 4µs,Toff6.5 µs,Duty Factor = 38%, HV = 0.3, LV = 30
10
Ton = 4µs,Toff6.5 µs,Duty Factor = 38%, HV = 0.7, LV = 30
11
Ton = 4µs,Toff6.5 µs,Duty Factor = 38%, HV = 0.3, LV = 50
12
Ton = 4µs,Toff6.5 µs,Duty Factor = 38%, HV = 0.7, LV = 50
13
Ton = 6.5µs,Toff6.5 µs,Duty Factor = 50%, HV = 0.3, LV = 30
14
Ton = 6.5µs,Toff6.5 µs,Duty Factor = 50%, HV = 0.7, LV = 30
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 267
Experimental
Runs
Replicate # 1
Replicate # 2
Replicate # 3
15
Ton = 6.5µs,Toff6.5 µs,Duty Factor = 50%, HV = 0.3, LV = 50
16
Ton = 6.5µs,Toff6.5 µs,Duty Factor = 50%, HV = 0.7, LV = 50
Table XVII. AISI 1045– Copper Electrode – 20 X.
M. M. Uz Zaman Siddiqui, S. Amir Iqbal, A. Zulqarnain, A. Tabassum
Memoria Investigaciones en Ingeniería, núm. 28 (2025). pp. 222-268
https://doi.org/10.36561/ING.28.15
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay 268
Author contribution:
1. Conception and design of the study
2. Data acquisition
3. Data analysis
4. Discussion of the results
5. Writing of the manuscript
6. Approval of the last version of the manuscript
MMUZS has contributed to: 1, 2, 3, 4, 5 and 6.
SAI has contributed to: 1, 2, 3, 4, 5 and 6.
AZ has contributed to: 1, 2, 3, 4, 5 and 6.
AT has contributed to: 1, 2, 3, 4, 5 and 6.
Acceptance Note: This article was approved by the journal editors Dr. Rafael Sotelo and Mag. Ing. Fernando A.
Hernández Gobertti.