An adaptive neuro-fuzzy and NSGA-II-based hybrid approach for modelling and multi-objective optimization of WEDM quality characteristics during machining titanium alloy

Goyal, Ashish; Gautam, Nipun; Pathak, Vimal Kumar

doi:10.1007/s00521-021-06261-7

Cited by 33 publications

(4 citation statements)

References 26 publications

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“…In the last few years, several researchers made attempts to optimize the process parameters of WEDM considering multiple performance characteristics using gray relational analysis [27], utility concept [28], desirability [29], and artificial intelligence (AI) [30] techniques. Out of those mentioned above, AI techniques are preferred due to their quick response with a near-optimal solution.…”

Section: Introductionmentioning

confidence: 99%

A Soft Computing-Based Analysis of Cutting Rate and Recast Layer Thickness for AZ31 Alloy on WEDM Using RSM-MOPSO

et al. 2022

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In the present research, the AZ31 alloy is machined by wire-cut electric discharge machining (WEDM). The experiments were designed according to the Box-Behnken design (BBD) of response surface methodology (RSM). The input process variables, namely servo feed (SF), pulse on-time (Ton), servo voltage (SV), and pulse off-time (Toff), were planned by BBD, and experiments were performed to investigate the cutting rate (CR) and recast layer thickness (RCL). The analysis of variance (ANOVA) was performed to determine the influence of machining variables on response characteristics. The empirical models developed for CR and RCL were solved using Multi-Objective Particle Swarm Optimization (MOPSO). Pareto optimal front is used for the collective optimization of CR and RCL. The optimal solution suggested by the hybrid approach of RSM-MOPSO is further verified using a confirmation test on the random setting indicated by the hybrid algorithm. It is found that the minimum RCL (6.34 µm) is obtained at SF: 1700; SV: 51 V; Toff: 10.5 µs; and Ton: 0.5 µs. However, maximum CR (3.18 m/min) is predicted at SF: 1900; SV: 40 V; Toff: 7 µs; and Ton: 0.9 µs. The error percentage of ±5.3% between the experimental results and predicted solutions confirms the suitability of the proposed hybrid approach for WEDM of AZ31.

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Section: Introductionmentioning

confidence: 99%

A Soft Computing-Based Analysis of Cutting Rate and Recast Layer Thickness for AZ31 Alloy on WEDM Using RSM-MOPSO

et al. 2022

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“…Particle swarm, TLBO, grey wolf, and Jaya algorithms were used to find the best setting [38]. Goyal et al [39] used an adaptive neurofuzzy inference system and NSGA-II to find relations between process input and output parameters, minimize the value of WWR, and maximize the MRR in WEDM of Ti-6Al-4 V titanium alloy. Kumar et al [40] demonstrated the optimization by implementing regression, weighted sum method, Analytic Hierarchy Process (AHP), and Genetic Algorithm (GA) to identify optimum parametric settings MRR, Spark Gap (SG), and SR. Phate et al [41] employed principal component analysis coupled with an artificial neural network in their research work to perform a multi-response optimization of the WEDM process of Aluminum Silicate metal matrix composite.…”

Section: Introductionmentioning

confidence: 99%

A hybrid algorithm for adjusting the input parameters of the wirecut EDM machine in order to obtain maximum customer satisfaction

et al. 2022

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This study presents an algorithm to optimally adjust the input parameters of the wirecut to align its output with the customer’s expectations. For this, AHP and QFD are used to identify and prioritize customer needs in the form of a desirability function. Then, using the Taguchi method, variance analysis, and regression, a fitness function is prepared and optimized by the multi-objective genetic algorithm. Through a case study, the proposed method is validated in terms of flexibility, simplicity, speed, cost-effectiveness, and updateability. Also, customer satisfaction is calculated for two groups of 45 people, with and without using the proposed method. The growth of the customer satisfaction index (CSAT) from 57.6 to 70.3, and the customer satisfaction score from 30.2 to 54.2, show the positive performance of the method. This converts regular customers into loyal ones. It also makes them encourages others to use the mentioned services and widen the customer network. It is clearly seen in the growth of the net promoter score from 6.67 to 31.11. All in all, it can be said that this algorithm helps the survival, profitability, and expansion of an industrial organization.

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“…The developed ANFIS model demonstrated efficient prediction of WEDM responses such as MRR and roughness of nitinol alloy and concluded that the ANFIS had a better accuracy in anticipation of WEDM attributes (Naresh et al , 2020). Integration of ANFIS and NSGA-II to optimise WEDM process helped in achieving optimal outcomes with enhanced values of MRR with optimal machining characteristics in the tolerance limits (Goyal et al , 2021). The ANN model for WEDM failure prediction based on sensor fusion and pulse train analysis was very successful in avoiding the WEDM failure, thus by ensuring failure free machining (Abhilash and Chakradhar, 2022).…”

Section: Introductionmentioning

confidence: 99%

Modelling and prediction of WEDM parameters for sustainable machining of titanium grade-2 alloy

Mussada

2022

WJE

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Purpose The purpose of the study is to establish a predictive model for sustainable wire electrical discharge machining (WEDM) by using adaptive neuro fuzzy interface system (ANFIS). Machining was done on Titanium grade 2 alloy, which is also nicknamed as workhorse of commercially pure titanium industry. ANFIS, being a state-of-the-art technology, is a highly sophisticated and reliable technique used for the prediction and decision-making. Design/methodology/approach Keeping in the mind the complex nature of WEDM along with the goal of sustainable manufacturing process, ANFIS was chosen to construct predictive models for the material removal rate (MRR) and power consumption (Pc), which reflect environmental and economic aspects. The machining parameters chosen for the machining process are pulse on-time, wire feed, wire tension, servo voltage, servo feed and peak current. Findings The ANFIS predicted values were verified experimentally, which gave a root mean squared error (RMSE) of 0.329 for MRR and 0.805 for Pc. The significantly low RMSE verifies the accuracy of the process. Originality/value ANFIS has been there for quite a time, but it has not been used yet for its possible application in the field of sustainable WEDM of titanium grade-2 alloy with emphasis on MRR and Pc. The novelty of the work is that a predictive model for sustainable machining of titanium grade-2 alloy has been successfully developed using ANFIS, thereby showing the reliability of this technique for the development of predictive models and decision-making for sustainable manufacturing.

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An adaptive neuro-fuzzy and NSGA-II-based hybrid approach for modelling and multi-objective optimization of WEDM quality characteristics during machining titanium alloy

Cited by 33 publications

References 26 publications

A Soft Computing-Based Analysis of Cutting Rate and Recast Layer Thickness for AZ31 Alloy on WEDM Using RSM-MOPSO

A Soft Computing-Based Analysis of Cutting Rate and Recast Layer Thickness for AZ31 Alloy on WEDM Using RSM-MOPSO

A hybrid algorithm for adjusting the input parameters of the wirecut EDM machine in order to obtain maximum customer satisfaction

Modelling and prediction of WEDM parameters for sustainable machining of titanium grade-2 alloy

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