An investigative study on the parameters optimization of the electric discharge machining of Ti6Al4V

Muhammad Mansoor Uz Zaman Siddiqui; Syed Amir Iqbal; Ali Zulqarnain; Adeel Tabassum; Muhammad Mansoor Uz Zaman Siddiqui; Syed Amir Iqbal; Ali Zulqarnain; Adeel Tabassum

doi:10.3934/ctr.2024003

Clean Technologies and Recycling

2024, Volume 4, Issue 1: 43-60. doi: 10.3934/ctr.2024003

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Research article

An investigative study on the parameters optimization of the electric discharge machining of Ti6Al4V

Muhammad Mansoor Uz Zaman Siddiqui ^{1,2
,
,},
Syed Amir Iqbal ¹,
Ali Zulqarnain ¹,
Adeel Tabassum ^2,3

1.
Department of Industrial & Manufacturing Engineering, NED University of Engineering & Technology, Karachi-75270, Pakistan
2.
Quality Department, PEL, 14 km Ferozepur Road, Lahore-54600, Pakistan
3.
Department of Mechanical Engineering, NUST College of EME, Rawalpindi, Pakistan

Received: 22 January 2024 Revised: 20 May 2024 Accepted: 29 May 2024 Published: 21 June 2024

This investigative study explored the field of electrical discharge machining (EDM), with a particular focus on the machining of Ti6Al4V, a titanium alloy that finds widespread application in aerospace, airframes, engine components, and non-aerospace applications such as power generation and marine and offshore environments. Ti6Al4V presents difficulties for conventional metal cutting techniques because of high cutting forces, poor surface integrity, and tool wear. This has led to the adoption of unconventional techniques like EDM. However, problems like high electrode wear rates, low material removal rates, long machining times, and less-than-ideal surface finishes still exist, especially in large-scale applications. By addressing the particular difficulties associated with large-scale electrical discharge machining and by putting forth a multi-objective optimization strategy, this research makes a substantial contribution to the field. With an emphasis on the optimization of input parameters like pulse on time (T_on), pulse off time (T_off), voltage (HV), and current (LV), which are critical in large-scale industrial applications, the study attempts to evaluate the optimal parameter states that simultaneously accomplish multiple goals during the machining process. This work is the first to simultaneously optimize all relevant output responses, such as material removal rate (MRR), electrode wear rate (EWR), machining time (Tm), surface roughness (Ra), and base radius. Previous studies have concentrated on one or two output responses. To optimize MRR, EWR, Tm, Ra, and base radius, the experiments were carefully planned using design of experiment (DOE) and response Surface methodology (RSM). Regression analysis and ANOVA are two statistical techniques that were used with Minitab 15 to help interpret experimental data and build a solid regression model specifically for Ti6Al4V. Throughout the experiment, a variety of input factor settings were employed, and the responses to those were noted. The following parameters were used to obtain the experimental data: current (LV) at 30 and 50 A, voltage (HV) at 0.3 and 0.7 V, pulse on time (T_on) at 4 and 6.5 µs, and pulse off time (T_off) at 5.5 and 6.5 µs. T_on and current are the most significant variables that influence most of the output responses. By addressing the simultaneous optimization of multiple output responses, this investigative study not only sets a new standard in the field but also identifies current bottlenecks and offers solutions.
- electric discharge machining,
- material removal rate,
- electrode wear rate,
- surface roughness,
- machining time,
- Ti6Al4V,
- design of experiment,
- analysis of variance
Citation: Muhammad Mansoor Uz Zaman Siddiqui, Syed Amir Iqbal, Ali Zulqarnain, Adeel Tabassum. An investigative study on the parameters optimization of the electric discharge machining of Ti6Al4V[J]. Clean Technologies and Recycling, 2024, 4(1): 43-60. doi: 10.3934/ctr.2024003

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Abstract

This investigative study explored the field of electrical discharge machining (EDM), with a particular focus on the machining of Ti6Al4V, a titanium alloy that finds widespread application in aerospace, airframes, engine components, and non-aerospace applications such as power generation and marine and offshore environments. Ti6Al4V presents difficulties for conventional metal cutting techniques because of high cutting forces, poor surface integrity, and tool wear. This has led to the adoption of unconventional techniques like EDM. However, problems like high electrode wear rates, low material removal rates, long machining times, and less-than-ideal surface finishes still exist, especially in large-scale applications. By addressing the particular difficulties associated with large-scale electrical discharge machining and by putting forth a multi-objective optimization strategy, this research makes a substantial contribution to the field. With an emphasis on the optimization of input parameters like pulse on time (T_on), pulse off time (T_off), voltage (HV), and current (LV), which are critical in large-scale industrial applications, the study attempts to evaluate the optimal parameter states that simultaneously accomplish multiple goals during the machining process. This work is the first to simultaneously optimize all relevant output responses, such as material removal rate (MRR), electrode wear rate (EWR), machining time (Tm), surface roughness (Ra), and base radius. Previous studies have concentrated on one or two output responses. To optimize MRR, EWR, Tm, Ra, and base radius, the experiments were carefully planned using design of experiment (DOE) and response Surface methodology (RSM). Regression analysis and ANOVA are two statistical techniques that were used with Minitab 15 to help interpret experimental data and build a solid regression model specifically for Ti6Al4V. Throughout the experiment, a variety of input factor settings were employed, and the responses to those were noted. The following parameters were used to obtain the experimental data: current (LV) at 30 and 50 A, voltage (HV) at 0.3 and 0.7 V, pulse on time (T_on) at 4 and 6.5 µs, and pulse off time (T_off) at 5.5 and 6.5 µs. T_on and current are the most significant variables that influence most of the output responses. By addressing the simultaneous optimization of multiple output responses, this investigative study not only sets a new standard in the field but also identifies current bottlenecks and offers solutions.

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