Modeling and optimization of machining parameters using regression and cuckoo search in deep hole drilling process
This study presents the modeling and optimization of the machining parameters in deep hole drilling process using statistical and soft computing technique. Regression analysis is used for modeling and Cuckoo Search, CS algorithm is used for the optimization process. Design of Experiment (DoE), have...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Published: |
Trans Tech Publications Ltd.
2019
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/88165/ https://doi.org/10.4028/www.scientific.net/AMM.892.177 |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | This study presents the modeling and optimization of the machining parameters in deep hole drilling process using statistical and soft computing technique. Regression analysis is used for modeling and Cuckoo Search, CS algorithm is used for the optimization process. Design of Experiment (DoE), have been carried using a Full Factorial design with added centre point that comprises of machining parameters (feed rate (f), spindle speed (s), depth of hole (d) and minimum quantity lubrication, MQL (m)) and machining performance which is surface roughness, Ra. Next, the mathematical models (Multiple Linear Regression, MLR and 2-factor interaction, 2FI) are developed for the experimental results of Ra and Analysis of variance, ANOVA are used to check the significance of the models developed. The results showed that both of mathematical models (MLR and 2FI) have outperformed the minimum Ra value compared to the experimental result. |
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