Parametric optimization of EDD using RSM-Grey-TLBO-based MCDM approach for commercially pure titanium

Abstract

PurposeElectric discharge drilling (EDD) is used to drill quality microholes on any conductive materials. EDD process parameters play a crucial role in the drilling. Depending upon the material characteristics, the cost of drilling also changes. Therefore, a suitable method is required to control the process parameters and drill quality microholes.Design/methodology/approachThe input process parameters in the present work are peak current (Ip), pulse on-time (Ton) and pulse off-time (Toff). The trials were intended in accordance to central composite face-centered design of response surface methodology (RSM). The output responses, namely drilling rate (DR) and electrode wear ratio (EWR), were converted into a single response, that is, grade using Grey relational analysis (GRA). The grade value is further modeled by regression analysis. The empirical model was figured out using teaching–learning-based optimization (TLBO). The RSM-Grey-TLBO-based multicriteria decision-making (MCDM) is used to investigate the optimized process parameter setting.FindingsThe RSM-Grey-TLBO-based MCDM approach suggests that the optimized setting for DR and EWR is Ip: 3A; Ton: 40 µs; Toff: 42 µs. The percentage errors for the predicted and experimental results are 8.1 and 7.5% in DR and EWR, respectively.Originality/valueThe parametric optimization of EDD using RSM-Grey-TLBO-based MCDM approach while machining commercially pure titanium is still underway. Thus, this MCDM approach will give a path to the researchers working in this direction.

Production Wastage Avoidance Using Modified Multi-Objective Teaching Learning Based Optimization Embedded With Refined Learning Scheme

“…Similarly, three optimization goals to be attained by abrasive water jet machining of C360 brass (i.e., minimum surface roughness, maximization of material removal rate and hardness) are represented as a single objective function in [33] and solved using classical TLBO. Two response variables of electrode wear ratio and drilling rate for electric discharge drilling process of titanium in [34] were obtained using the response surface methodology and converted into a single objective function with grey relational analysis before searching for the best combinations of machining parameters (i.e., peak current, pulse-off and pulse-on time) using TLBO. A preference-based multiobjective TLBO (PBMOO-TLBO) was proposed in [35] to attain the sustainable machining of Ti-6Al-4V alloy with wire-cut EDM via the minimization of surface roughness and maximization of material removal rate.…”

“…Although numerous works related to TLBO were proposed by different researchers since its inception, some common drawbacks and technical challenges can be observed from these studies. First of all, it is noteworthy that the related works of [1][2][3][4][5][31][32][33][34][35] focused on applying the original TLBO to solve different real-world applications, particularly on the machining optimization problems. Despite having relatively good performances in solving these problems, the original TLBO tends to suffer with drastic performance degradation when dealing with more complex optimization problems with explosive numbers of local optima in fitness landscapes.…”

“…Finally, it is noteworthy that some TLBO variants in [30][31][32][33][34][35] were designed as priori approach to solve the multiobjective optimization problems. For priori approach, different weight values were assigned to the multiple numbers of objective functions that have contradictory goals by referring to their importance levels in order to construct a single objective function [43].…”

“…The inherent mechanisms used by both SML and IAL schemes to balance the exploration and exploitation searches is expected to assist the proposed MMTLBO-RLS to solve complex problems with better robustness. • The multiobjective optimization problems reported in [30][31][32][33][34][35] were solved using the prior approach that can only produce a unique optimum solution for each optimization run. This approach might not be feasible for real-world problems because the requirements of stakeholders might change with time and optimization processes need to be repeated for multiple times.…”

Optimizing The Machining Process of IS 2062 E250 Steel Plates with The Boring Operation Using a Hybrid Taguchi-Pareto Box Behnken-teaching Learning-based Algorithm

“…Several other authors have also focused on different materials as work material for optimization of machining input parameters to acquire optimal out parameters; those that used steel (alloys and grades) are Dave (2019), Patel et al (2018), Upadhyay (2022), Aouici et al (2012), Patil et al (2021, Suresh et al (2002). Some of the authors that employed titanium (alloys and grades) are Sharma et al (2020), Singh et al (2019), Upadhyay et al (2013), Sahu & Andhare (2015), and Zain et al (2010). Inconel was also used by the following authors: Dave (2019), Kumar et al (2020), Gupta et al (2019), Rao & Kalyankar (2011), George et al (2019), and Bhopale et al (2015).…”