A cutting sequence optimization method based on tabu search algorithm for complex parts machining. (February 2019)

• Record Type: Journal Article
• Authors: Zhang, Chuanwei; Han, Feiyan; Zhang, Wu
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 3(2019)
• Page Start: 745
• Page End: 755
• Publication Date: 2019-02
• Subjects: Cutting sequence -- optimization model -- the tabu search algorithm -- complex parts; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405417752527
• Languages: English
• ISSNs: 0954-4054

Abstract

Defining the cutting sequence of each cutter scientifically in the process of removing the allowance has an important influence on the machining efficiency for complex parts, which have multiple machining features. In order to satisfy the needs of high efficiency for rough machining, after determining the tool path of the machining region, a cutting sequence optimization method based on the tabu search algorithm is presented to define the cutting order in rough machining of complex parts. First, a cutting sequence optimization mathematical model is established, which relates to the shortest total length of the tool path. Second, through the problem analysis, the cutting sequence optimization model is converted into an open and constrained traveling salesman problem. And then, the optimization model is solved by dealing with an open and constrained traveling salesman problem using the tabu search algorithm. Finally, the optimal cutting sequence of machining a casing part is calculated, and a simulation and experiment are carried out. The result shows that the optimization approach presented in this article can optimize the cutting sequence and cutter position of advance and retract. Compared with the non-optimized cutting sequence method, the total length of tool path is reduced by 16.7%, the cutter lifting times are reduced to 26, and the efficiency is increased by 21.62%.