Tool Performance Optimization While Machining Aluminium-Based Metal Matrix Composite
Finite element (FE) models and the multi objective genetic algorithm (MOGA-II) have been applied for tool performance optimization while machining aluminum-based metal matrix composites. The developed and verified FE models are utilized to generate data for the full factorial design of experiment (D...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/10/6/835 |
| _version_ | 1797564333308248064 |
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| author | Usama Umer Mustufa Haider Abidi Jaber Abu Qudeiri Hisham Alkhalefah Hossam Kishawy |
| author_facet | Usama Umer Mustufa Haider Abidi Jaber Abu Qudeiri Hisham Alkhalefah Hossam Kishawy |
| author_sort | Usama Umer |
| collection | DOAJ |
| description | Finite element (FE) models and the multi objective genetic algorithm (MOGA-II) have been applied for tool performance optimization while machining aluminum-based metal matrix composites. The developed and verified FE models are utilized to generate data for the full factorial design of experiment (DOE) plan. The FE models consist of a heterogenous workpiece, which assumes uniform distribution of reinforced particles according to size and volume fraction. Cutting forces, chip morphology, temperature contours, stress distributions in the workpiece and tool by altering cutting speed, feed rate, and reinforcement particle size can be estimated using developed FE models. The DOE data are then utilized to develop response surfaces using radial basis functions. To reduce computational time, these response surfaces are used as solver for optimization runs using MOGA-II. Tool performance has been optimized with regard to cutting temperatures and stresses while setting a limit on specific cutting energy. Optimal solutions are found with low cutting speed and moderate feed rates for each particle size metal matrix composite (MMC). |
| first_indexed | 2024-03-10T18:55:51Z |
| format | Article |
| id | doaj.art-ee46f144e19a492580fc30418ec1ed37 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-10T18:55:51Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-ee46f144e19a492580fc30418ec1ed372023-11-20T04:49:44ZengMDPI AGMetals2075-47012020-06-0110683510.3390/met10060835Tool Performance Optimization While Machining Aluminium-Based Metal Matrix CompositeUsama Umer0Mustufa Haider Abidi1Jaber Abu Qudeiri2Hisham Alkhalefah3Hossam Kishawy4Advanced Manufacturing, Institute, King Saud University, Riyadh 11421, Saudi ArabiaAdvanced Manufacturing, Institute, King Saud University, Riyadh 11421, Saudi ArabiaMechanical Engineering Department, College of Engineering, United Arab Emirates University, Al-Ain-15551, UAEAdvanced Manufacturing, Institute, King Saud University, Riyadh 11421, Saudi ArabiaMachining Research Laboratory, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, CanadaFinite element (FE) models and the multi objective genetic algorithm (MOGA-II) have been applied for tool performance optimization while machining aluminum-based metal matrix composites. The developed and verified FE models are utilized to generate data for the full factorial design of experiment (DOE) plan. The FE models consist of a heterogenous workpiece, which assumes uniform distribution of reinforced particles according to size and volume fraction. Cutting forces, chip morphology, temperature contours, stress distributions in the workpiece and tool by altering cutting speed, feed rate, and reinforcement particle size can be estimated using developed FE models. The DOE data are then utilized to develop response surfaces using radial basis functions. To reduce computational time, these response surfaces are used as solver for optimization runs using MOGA-II. Tool performance has been optimized with regard to cutting temperatures and stresses while setting a limit on specific cutting energy. Optimal solutions are found with low cutting speed and moderate feed rates for each particle size metal matrix composite (MMC).https://www.mdpi.com/2075-4701/10/6/835finite element model (FEM)metal matrix composite (MMC)cutting tools |
| spellingShingle | Usama Umer Mustufa Haider Abidi Jaber Abu Qudeiri Hisham Alkhalefah Hossam Kishawy Tool Performance Optimization While Machining Aluminium-Based Metal Matrix Composite Metals finite element model (FEM) metal matrix composite (MMC) cutting tools |
| title | Tool Performance Optimization While Machining Aluminium-Based Metal Matrix Composite |
| title_full | Tool Performance Optimization While Machining Aluminium-Based Metal Matrix Composite |
| title_fullStr | Tool Performance Optimization While Machining Aluminium-Based Metal Matrix Composite |
| title_full_unstemmed | Tool Performance Optimization While Machining Aluminium-Based Metal Matrix Composite |
| title_short | Tool Performance Optimization While Machining Aluminium-Based Metal Matrix Composite |
| title_sort | tool performance optimization while machining aluminium based metal matrix composite |
| topic | finite element model (FEM) metal matrix composite (MMC) cutting tools |
| url | https://www.mdpi.com/2075-4701/10/6/835 |
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