Title: Modeling and Optimization of Surface Roughness in Machining of Brass Using Multi-linear Regression in Conjunction With Genetic Algorithm
Abstract: In this chapter, the authors present a multi-linear regression based approach for the modeling of surface roughness during the turning of a commercial brass alloy. Three regression models are developed by utilizing the experimental data gathered following a full-factorial based design-of-experiments (DOE) methodology. While the conventional practice has been to develop regression models using the entire experimental datasets, we deviate from the same and employ only a subset of the available data for the purpose, the remaining data is then used for the model validation. The results obtained herein reveal that the second-order regression model is statistically better than the other two in predicting the surface roughness for both the datasets. The global minimum surface roughness is determined by using the developed regression models in conjunction with the genetic algorithm based single-objective optimization. The regression models serve as candidate objective functions for the genetic algorithm, and the optimization is performed while subjecting the objective function to several experimental constraints. The optimization results reveal that the global minimum obtained using the second-order regression model is in close agreement with the experimentally obtained minimum surface roughness.
Publication Year: 2020
Publication Date: 2020-11-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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