Hybrid Grey–Fuzzy Approach for Optimizing Circular Quality Responses in Plasma Jet Manufacturing of Aluminum Alloy
Plasma jet cutting is a non-conventional process commonly used in modern industry for processing metal sheets and preparing them for subsequent technological steps. In this context it is very important to achieve the best possible final-quality workpiece to minimize additional post-processing costs,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/13/7447 |
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| Summary: | Plasma jet cutting is a non-conventional process commonly used in modern industry for processing metal sheets and preparing them for subsequent technological steps. In this context it is very important to achieve the best possible final-quality workpiece to minimize additional post-processing costs, and time. This is especially challenging by the plasma jet processing of aluminum and its alloys. In this paper, a comprehensive analysis regarding the machinability and optimal circular quality of aluminum alloy 5083 was performed. Process parameters whose effects were analyzed are the cutting speed, arc current and cutting height. The circular quality was considered through responses: the circular kerf width, circular bevel angle, and circularity error on the top and bottom sheet of the metal side. To design functional relations between the process inputs and quality performances, an artificial intelligence fuzzy logic technique supported by ANOVA was applied. In order to define the process conditions that result in optimal cut quality responses, the multi-objective optimization of hybrid grey relational analysis (GRA) and the fuzzy logic approach was presented. Corresponding surface plots were created to determine the Pareto front of optimal solutions that simultaneously optimize all circular quality objective functions. The optimization procedure was confirmed through a test in which the mean absolute percentage error represented as the validation metric. |
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| ISSN: | 2076-3417 |