4.3 Article

Multiresponse Particle Swarm Optimization of Wire-Electro-Discharge Machining Parameters of Nitinol Alloys

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MATHEMATICAL PROBLEMS IN ENGINEERING
卷 2021, 期 -, 页码 -

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HINDAWI LTD
DOI: 10.1155/2021/9059722

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  1. Deanship of Scientific Research at Umm Al-Qura University [20UQU072DSR]

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The conventional machining process of nitinol alloy can lead to extensive tool wear and poor surface quality. Using WEDM and PSO, the machining parameters can be optimized to improve metal removal rate and surface roughness. By studying response models and optimization methods, the optimal parameter combination for reducing surface roughness and maximizing metal removal rate can be determined.
The conventional process of machining of nitinol alloy which possesses excess strain hardening and low thermal conductivity makes a complex process that leads to extensive wear on the tool and inadequate surface quality. Wire-electro-discharge machining (WEDM) is widely accepted for machining this alloy involving various input factors, namely, P (pulse-on-duration), Q (pulse-off-duration), C, (maximum-current), and V (voltage). Using the PSO (particle swarm optimization) method, the effect of WEDM process factors on multiresponses such as MRR (metal removal rate) and SR (surface roughness) has been investigated. ANOVA was used to create a relationship model between input factors and response characteristics, which was then optimized using response surface methods (RSM). ANOVA revealed that variables A and C are the most significant. When investigated individually, the influence of WEDM process parameters on SR and MRR is contradictory, as no response provides the best process quality. To find the optimal ideal condition for decreasing SR and maximizing MRR, the MOOPSO approach was used. P = 25.47051 mu s, Q = 10.84998 mu s, C = 2.026317 A, and V = 49.50757 volts were used to calculate the optimal universal solution for machining characteristics (MRRmax = 3.536791 mm(3)/min and SRmin = 1.822372 mu m). PSO enhanced MRR and SR for every optimal combination of variables, according to the findings. Based on the findings, a wide range of optimal settings for achieving maximum MRR and minimum SR are given, depending on the product requirements.

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