Optimization of EDM process of titanium alloy using EPSDE technique
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ORIGINAL PAPER
Optimization of EDM process of titanium alloy using EPSDE technique Mahendra Raj Singh1 · Pankaj Kumar Shrivastava1 · Pushpendra Singh2 Received: 2 September 2020 / Accepted: 13 October 2020 © Springer Nature Switzerland AG 2020
Abstract Electrical discharge machining (EDM) is now a widely adopted subtractive manufacturing process to shape titanium and its alloys to obtain desired profile and surface integrity. Predicting and optimizing the process behavior is the need of today’s manufacturing paradigm. Many new and better optimization algorithms have came into existence in the recent past to extract best out of any manufacturing processes. In this paper, EDM has been carried out on Ti–6Al–4 V alloy by varying important electrical parameters to estimate the two of the major performances i.e. material removal rate (MRR) and tool wear rate (TWR). Second-order regression equation using statistical analysis has been obtained for both the performances. Single objective optimization has been done using four evolutionary optimization algorithms. All the four algorithms have been compared for their performances in the present research environment. Keywords Electrical discharge machining · EPSDE · Teacher learning-based optimization · Particle swarm optimization · Genetic algorithm
1 Introduction Titanium and its alloys are one of relatively advanced materials having high strength, toughness, corrosions resistance and capacity to sustain these properties even at elevated temperature (Jaber et al. 2018). Because of these superior qualities, it is extremely uneconomical and arduous to shape it by applying conventional machining methods. Advanced machining processes (AMPs) are better alternative to impart desired surface quality and shape to these kinds of innovative materials (Shrivastava and Dubey 2015). Electrical discharge machining (EDM) is a subtractive manufacturing process, belonging to AMP, which relies on thermal energy of spark to process these types of materials (Shrivastava and Dubey 2016). Due to non-contact type characteristics of EDM, several constraints inherited in conventional machining methods such as hardness of tool material, vibration, residual stresses are eliminated.
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Pankaj Kumar Shrivastava [email protected] Pushpendra Singh [email protected]
1
Mechanical Engineering Department, AKS University, Satna, Madhya Pradesh 485001, India
2
Department of Electrical Engineering, Rajkiya Engineering College, Banda, Uttar Pradesh 210201, India
Considerable work has beenss reported with respect to process prediction and optimization of EDM during EDM of titanium alloys. Khan et al. (2014) developed artificial neural network (ANN) model for estimating the average surface roughness (ASR) during EDM of titanium alloy. They tried network with different architecture and found that ANN with 4–12–1 architecture gives best performance by considering mean square error (MSE) criterion, as MSE obtained was 9.02 × 10–6 , which is almost negligible. Manjaiah et al. (2014) performed wire EDM (WED
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