Optimization of Process Control Parameters for WEDM of Al-LM25/Fly Ash/B4C Hybrid Composites Using Evolutionary Algorithms: A Comparative Study
| Autor: | Danil Yurievich Pimenov, N. Lenin, Tadeusz Mikolajczyk, Vinothkumar Sivalingam, G. Selvakumar, Devaraj Rajamani, Munish Kumar Gupta, Mahalingam Sivakumar |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Materials science
Mining engineering. Metallurgy Machinability Metals and Alloys Evolutionary algorithm TN1-997 Particle swarm optimization 02 engineering and technology 021001 nanoscience & nanotechnology volume removal rate Electrical discharge machining Machining surface roughness 0202 electrical engineering electronic engineering information engineering Surface roughness Process control 020201 artificial intelligence & image processing General Materials Science Orthogonal array Composite material Grass–Hooper optimization algorithm 0210 nano-technology hybrid composites wire electrical discharge machining (WEDM) |
| Zdroj: | Metals, Vol 11, Iss 1105, p 1105 (2021) Metals Volume 11 Issue 7 |
| ISSN: | 2075-4701 |
| Popis: | In this work, wire electrical discharge machining (WEDM) of aluminum (LM25) reinforced with fly ash and boron carbide (B4C) hybrid composites was performed to investigate the influence of reinforcement wt% and machining parameters on the performance characteristics. The hybrid composite specimens were fabricated through the stir casting process by varying the wt% of reinforcements from 3 to 9. In the machinability studies, the WEDM process control parameters such as gap voltage, pulse-on time, pulse-off time, and wire feed were varied to analyze their effects on machining performance including volume removal rate and surface roughness. The WEDM experiments were planned and conducted through the L27 orthogonal array approach of the Taguchi methodology, and the corresponding volume removal rate and surface roughness were measured. In addition, the multi-parametric ANOVA was performed to examine the statistical significance of the process control parameters on the volume removal rate and surface roughness. Furthermore, the spatial distribution of the parameter values for both the responses were statistically analyzed to confirm the selection of the range of the process control parameters. Finally, the quadratic multiple linear regression models (MLRMs) were formulated based on the correlation between the process control parameters and output responses. The Grass–Hooper Optimization (GHO) algorithm was proposed in this work to identify the optimal process control parameters through the MLRMs, in light of simultaneously maximizing the volume removal rate and minimizing the surface roughness. The effectiveness of the proposed GHO algorithm was tested against the results of the particle swarm optimization and moth-flame optimization algorithms. From the results, it was identified that the GHO algorithm outperformed the others in terms of maximizing volume removal rate and minimizing the surface roughness values. Furthermore, the confirmation experiment was also carried out to validate the optimal combination of process control parameters obtained through the GHO algorithm. |
| Databáze: | OpenAIRE |
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