| DOI | Resolve DOI: https://doi.org/10.1016/j.procir.2013.06.153 |
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| Author | Search for: Aramesh, M.; Search for: Shi, B.1; Search for: Nassef, A. O.; Search for: Attia, H.1; Search for: Balazinski, M.; Search for: Kishawy, H. A. |
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| Affiliation | - National Research Council of Canada. Aerospace
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| Format | Text, Article |
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| Conference | 14th CIRP Conference on Modeling of Machining Operations, CIRP CMMO 2013, June 13-14, 2013, Turin |
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| Subject | Experimental conditions; Mechanical and physical properties; Meta-modeling technique; MMCs; Multi-dimensional space; Strength pareto evolutionary algorithm; Titanium metal matrix composites; Tool wear; Algorithms; Machining centers; Multiobjective optimization; Surface roughness; Wear of materials |
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| Abstract | The Outstanding characteristics of titanium metal matrix composites (Ti-MMCs) have brought them up as promising materials in different industries, such as aerospace and biomedical. They exhibit high mechanical and physical properties, in addition to their low weight, high stiffness and high wear resistance. The presence of the ceramic reinforcements in a metallic matrix further contributes to these preferable properties. However, the high abrasive nature of the ceramic particles limits greatly the machinability of this class of material, as they induce significant tool wear and poor surface finish. In this study an attempt is made to find the optimum cutting conditions in terms of minimizing the tool wear and surface roughness during machining Ti-MMCs. Meta-modeling optimization in performed to achieve the goal. In this study the three independent parameters under consideration are the cutting speed, feed rate and the depth of cut. The response parameters are the surface roughness and the tool wear rate. The independent parameters are divided into a set of levels at which the experiments are conducted. At each experimental condition the two response parameters are measured. Kriging meta-modeling technique is used to fit a model to the response parameters in the multi-dimensional space. These models are used, in turn, within a multi-objective optimization algorithm to find the optimum cutting condition space. The above-mentioned algorithm is based on an evolutionary multi-objective search technique known as SPEA (Strength Pareto Evolutionary Algorithm). Copyright © 2013 Elsevier B.V. |
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| Publication date | 2013-07-03 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 21271860 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 637fcc57-aeb9-48df-8be0-359f4542d345 |
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| Record created | 2014-04-24 |
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| Record modified | 2020-04-22 |
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