An intelligent approach for multi-response optimization: a case study of non-traditional machining process

The present work proposes an intelligent approach to solve multi-response optimization problem in electrical discharge machining of AISI D2 using response surface methodology (RSM) combined with optimization techniques. Four process parameters (factors) such as discharge current (Ip), pulse-on-time (Ton), duty factor (τ) and flushing pressure (Fp) and four important responses like material removal rate (MRR), tool wear rate (TWR), surface roughness (Ra) and circularity (r1/r2) of machined component are considered in this study. A Box-Behnken RSM design is used to collect experimental data and develop empirical models relating input parameters and responses. Genetic algorithm (GA), an efficient search technique, is used to obtain the optimal setting for desired responses. It is to be noted that there is no single optimal setting which will produce best performance satisfying all the responses. In industries, to solve such problems, managers frequently depend on their past experience and judgement. Human intervention causes uncertainties present in the decision making process gleaned into solution methodology resulting in inferior solutions. Fuzzy inference system has been a viable option to address multiple response problems considering uncertainties and impreciseness caused during judgement process and experimental data collection. However, choosing right kind of membership functions and development of fuzzy rule base happen to be cumbersome job for the managers. To address this issue, a methodology based on combined neuro-fuzzy system and particle swarm optimization (PSO) is adopted to optimize multiple responses simultaneously. To avoid the conflicting nature of responses, they are first converted to signal-to-noise (S/N) ratio and then normalized. The proposed neuro-fuzzy approach is used to convert the responses into a single equivalent response known as Multi-response Performance Characteristic Index (MPCI). The effect of parameters on MPCI values has been studied in detail and a process model has been developed. Finally, optimal parameter setting is obtained by particle swarm optimization technique. The optimal setting so generated that satisfy all the responses may not be the best one due to aggregation of responses into a single response during neuro-fuzzy stage. In this direction, a multi-objective optimization based on non-dominated sorting genetic algorithm (NSGA) has been adopted to optimize the responses such that a set of mutually dominant solutions are found over a wide range of machining parameters. The proposed optimal settings are validated using thermal-modeling of finite element analysis

Fuzzy based multi-response optimization: a case study on EDM machining process

Abstract The current study challenges the multi-objective optimization of electric discharge machining (EDM) parameters. EDM is used for creating profiles by machining of workpiece that are difficult to machine by conventional method. In the current work four responses such as material removal rate (production rate), tool wear rate, surface roughness (quality) and circularity (profile) are collectively investigated with varying controlling parameters. The human decision for best combination of controlling parameters for highest performance has uncertainties, which results in inferior solution. The multiple responses along with uncertainties and impreciseness can be addressed by combining a neuro-fuzzy system with particle swarm optimization (PSO). To illustrate the superiority of the proposed approach a set of experiment have been conducted in EDM process using AISI D2 tool steel as workpiece and brass tool. The experimental plan was made according to the Box-Behnken response surface methodology design with four process parameters namely discharge current, pulse-on-time, duty factor, and flushing pressure. The four response parameters such as material removal rate, tool wear rate, surface roughness, and circularity of machined components were optimized simultaneously. One unique Multi-response Performance Characteristic Index was obtained by combining the four responses using the proposed neuro-fuzzy technique. A regression model was developed on single response and optimized by PSO to obtain the optimal parameter setting. An experiment was conducted on optimal parameter to test the optimum performance. It is observed that the EDM responses were affected significantly by discharge current and pulse-on-time. The increase in pulse-on-time leads to larger surface cracks and more micro-pores on the machined surface. Article Highlights RSM was proven to be an effective statistical tool for reducing the experimental runs, and also establishes the relation between multiple inputs and single output. The neuro-fuzzy system combined with PSO results a suitable model to convert multiple response into an equivalent single response. The presented approach can be a practical method for situations where multiple conflicting objectives are needed to be optimized at the same time