Analysis method for factors influencing gear hobbing quality based on density peak clustering and improved multi-objective differential evolution algorithm. Issue 4 (3rd April 2021)

• Record Type: Journal Article
• Authors: Guo, You; Yan, Ping; Wu, Dayuan; Zhou, Han; Shi, Yancheng; Yi, Runzhong
• Is Part Of: International journal of computer integrated manufacturing. Volume 34:Issue 4(2021)
• Page Start: 385
• Page End: 406
• Publication Date: 2021-04-03
• Subjects: Process parameters reduction -- quality analysis -- characteristic value -- dimension reduction -- multi-objective differential evolution (MODE) algorithm; Computer integrated manufacturing systems -- Periodicals; 670.427
• DOI: 10.1080/0951192X.2021.1885063
• Languages: English
• ISSNs: 0951-192X

Abstract

ABSTRACT: For addressing the problem that the quality indicators of gear hobbing are complicated and the influencing factors are unknown, a characteristic processing method combining improved multi-objective differential evolution (IMODE) and clustering based on peak density (DPCA) is proposed. This method can extract the characteristic parameters that strongly influence gear hobbing quality for multi-process parameters and multi-quality indicators, and quantify their importance to the comprehensive quality indicators. First, based on correlation analysis of the quality inspection parameters by DPCA, a set of relatively independent gear hobbing quality inspection indicators is obtained, and the dimensions of the quality inspection parameters are reduced for more effectively reflecting the hobbing processing quality. Next, multi-threshold Birch (IBirch) clusters are obtained for different gear hobbing quality inspection data under different process parameters to obtain cluster labels. Finally, Rough Sets theory and IMODE are used to reduce the gear hobbing process parameters and design parameters. Feature parameters that significantly affect the hobbing process quality are extracted from the process parameters and their importance is quantified. The validity and practicability of the method are verified by processing experiments, and the advantages of the proposed method are proved.