A novel hybrid discrete differential evolution algorithm for workingsteps sequencing in multi-channel turn-milling complex machining. (1st December 2015)

• Record Type: Journal Article
• Authors: Shi, Xusheng; Qiao, Lihong; Zhu, Zuowei
• Is Part Of: Advances in mechanical engineering. Volume 7:Number 12(2015:Dec.)
• Publication Date: 2015-12-01
• Subjects: Manufacturing processes -- neural network -- manufacturing technology -- digital manufacturing -- evolutionary algorithm; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814015619376
• Languages: English
• ISSNs: 1687-8132

Abstract

To address the problem of workingsteps sequencing in multi-channel turn-milling complex machining, a novel hybrid optimization algorithm titled hybrid discrete differential evolution has been proposed, which improves the algorithm to achieve better results for the workingsteps sequencing problem. The main thrust of this article is twofold: (1) to analyze the characteristics of synchronous machining in multi-channel turn-milling complex machining and their constraints, and to propose a zero-wait micro-resource allocation strategy; (2) to develop a hybrid discrete differential evolution algorithm for process planning in multi-channel turn-milling complex machining, and to describe in detail about the operation of crossover, mutation, and selection. Then, analysis of variance has been used to investigate the contribution and effects of variables (parameters) in hybrid discrete differential evolution, so the optimal parameters can be obtained. Finally, a comparison of the performance and efficiency between the proposed algorithm and the classical differential evolution algorithm is made. And experimental results show that the hybrid discrete differential evolution algorithm is good at solving the sequencing problem, and the results approximate the optimum expectation.