Machine learning algorithms based advanced optimization of EDM parameters: An experimental investigation into shape memory alloys

In the era of advancement and progressive fourth industrial revolution culture, the demand of advanced and smart engineering materials has been increased. In this way, shape memory alloys are an excellent choice for industrial applications such as orthopedic implacers, actuators, micro-tools, fitting and screening elements, aircraft component components, military instruments, fabricating elements, and bio-medical devices, among others. This paper has been aimed to attempt the machine learning (ML) algorithms-based optimization of the different process inputs in electrical discharge machining of Cu-based shape memory alloy. The current study focused on study the behavior of response parameters along with the variation in machining input parameters The considered process input factors are namely as; pulse on time (Ton), pulse off time (Toff), peak current (Ip), and gap voltage (GV) and their effects were studied on dimensional deviation (DD) and tool wear rate (TWR). The central composite design matrix has been employed for planning the main runs. The 2-D and 3-D graphs represents the behavior of the response parameters along with variations in the machining inputs. The novelty of the work is machining of Cu-based Shape Memory Alloy (SMA) in EDM operations and optimization of parameters using Machine Learning techniques. Furthermore, machine learning based, single and multi-objective optimization of investigated responses were conducted using the desirability approach, Genetic Algorithm (GA) and Teacher Learning based Optimization (TLBO) techniques. The parametric combination attained for optimization of multiple responses (TWR and DD) is: Ton ​= ​90.10 ​μs, Toff ​= ​149.69 ​μs, Ip ​= ​24.59 A & GV ​= ​60 ​V; Ton ​= ​255 ​μs, Toff ​= ​15 ​μs, Ip ​= ​50 A & GV ​= ​15 ​V; Ton ​= ​255 ​μs, Toff ​= ​15 ​μs, Ip ​= ​50 A & GV ​= ​15 ​V, using desirability approach, GA method and TLBO method, respectively.