An ensemble evolutionary approach in evaluation of surface finish reduction of vibratory finishing process. Issue 5 (6th July 2015)

• Record Type: Journal Article
• Authors: Garg, Akhil; Vijayaraghavan, Venkatesh; Tai, Kang; Singru, Pravin M; Gao, Liang; Sangwan, K S
• Is Part Of: Engineering computations. Volume 32:Issue 5(2015)
• Page Start: 1214
• Page End: 1229
• Publication Date: 2015-07-06
• Subjects: Surface finish prediction -- Vibratory finishing process modelling -- Vibratory modelling; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-03-2014-0047
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: – The functioning of multi-gene genetic programming (MGGP) algorithm suffers from the problem of difficulty in model selection. During the preliminary analysis, it is observed that there are many models in the population whose performance is better than that of the model selected with a little compromise on training error. Therefore, an ensemble evolutionary (Ensemble-MGGP) approach is proposed and applied to the data obtained from the vibratory finishing process. The paper aims to discuss these issues. Design/methodology/approach: – Unlike the standard GP, each model participating in Ensemble-MGGP approach is made by combining the set of genes. Predicted residual sum of squares criterion (PRESS) criterion is integrated to improve its evolutionary search. The parametric analysis and sensitivity analysis ( SA ) conducted on the proposed model validates its robustness by unveiling dominant input parameters and hidden non-linear relationships. Findings: – The results indicate that the proposed Ensemble-MGGP model outperforms the standardized MGGP model. SA and parametric analysis reveals relationships and insights into vibratory finishing process. Originality/value: – Literature emphasises on characterization of vibratory finishing process using the experimental-based-studies. In addition, the issue of difficulty in model selection in genetic programming is addressed. This work proposes a new ensemble evolutionary approach to counter these issues.