Experimental investigation and parametric optimization for minimizing surface roughness during WEDM of Ti6Al4V alloy using modified TLBO algorithm

Abstract

Wire-cut electrical discharge machining (WEDM) is a widely used non-contact machining process used to cut difficultto-machine materials such as titanium, nickel and inconel alloys. WEDM is famous for producing components in diverse engineering applications, including automotive, marine, space, medical sectors with the highest accuracy and consistency. In this paper, experimental investigation on surface roughness (SR) of Ti6Al4V alloy employing WEDM process is presented using Taguchi L 16 experimental plan by varying four process variables, viz., pulse on time (T on), pulse off time (T off), current (I) and wire speed (WS) at four different levels. The novelty of the present work is minimization of surface roughness (SR) in WEDM of Ti6Al4V alloy employing reusable wire technology during experimental work and optimizes the problem with modified teaching-learning-based optimization (M-TLBO) algorithm. A novel technique for fitness curve fitting is also illustrated to obtain global optima for minimization of SR. Results showed that SR decreased by 2.65% at optimal parameter setting T on = 13 µs, T off = 10 µs, I = 1 A and WS = 850 rpm and global optima is obtained as 3.749 µm. Pulse on time (44.06%) and current (28.69%) are recognized as the most dominant process variables affecting SR followed by pulse off time (15.80%) and wire speed (7.47%). Surface morphology study of machined Ti alloy using SEM images showed that smoother surface is obtained at lower settings of pulse on time and current. The proposed M-TLBO algorithm is found to be highly accurate and consistent during several runs conducted with mean fitness value and standard deviation recorded as 3.751 µm and 0.006417, respectively.

Experimental investigation on WEDM performance analysis using grey-fuzzy integrated with TLBO algorithm for Inconel 625: comparison with GA and SA

“…This algorithm requires only population size and number of generations for its working with minimum computational time. Several research articles could be found with the passage of time toward the advantages and acceptance among optimization researchers communities Devarasiddappa et al (2020), Abhishek et al (2017); George et al (2019), Krishnasamy and Nanjundappan (2014). Motivated by this, the present study explores the TLBO algorithm for multi-optimization and single-optimization process performance yields during WEDM of In-625.…”

“…TLBO and GRA methodology is also the unique optimization tool used to find the machining process for satisfactory machining outcomes. Devarasiddappa et al (2020) apply the TLBO algorithm to optimize the RA data of WEDMed Ti6Al4V alloy. This methodology achieves the improved result of RA by 2.65%.…”

Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface methodology

“…Literatures where MCDM techniques such as TOPSIS [18][19][20], VIKOR [21,22], MOORA [23][24][25], ARAS [26] and others [27][28][29] are used in selecting the best machining parameters. Meta-heuristic techniques such as non-dominated sorting algorithm-II [30][31][32], particle swarm optimization [33,34], teaching-learning based optimization [35], artificial bee colony algorithm [11] etc. to evaluate the optimal performance parameters by WEDM process.…”

Multi-Attribute Optimization of the WEDM Process for Surface Characteristics and Productivity

“…The results indicated that the improvements in the MRR, Ra, and DL were 22.37%, 44.59%, and 15.07%, respectively. The reused wire technology was applied to decrease the Ra of the WEDM of Ti6Al4V alloy [10]. The author stated that the Ra was decreased by 2.65% with the aid of the new operation.…”