An intelligent wheel position searching algorithm for cutting tool grooves with diverse machining precision requirements. (November 2017)

• Record Type: Journal Article
• Authors: Li, Guochao; Zhou, Honggen; Jing, Xuwen; Tian, Guizhong; Li, Lei
• Is Part Of: International journal of machine tools & manufacture. Volume 122(2017)
• Page Start: 149
• Page End: 160
• Publication Date: 2017-11
• Subjects: Cutting tool groove -- Machining -- Wheel position -- NPSO; Machine-tools -- Periodicals; Manufacturing processes -- Periodicals; Machines-outils -- Périodiques; Fabrication -- Périodiques; Electronic journals; 621.902
• DOI: 10.1016/j.ijmachtools.2017.07.003
• Languages: English
• ISSNs: 0890-6955

Abstract

Abstract: Searching for and adjusting to the appropriate wheel positions for designed grooves are among the key issues in the manufacturing of the end mill, drill and other integral cutting tools. Along with the increasing requirements of product categories and quality, a large number of non-traditional and customized grooves continuously appear. However, as multivariable, nonlinear and multiple target problems, it is difficult to obtain the desired wheel position. Therefore, we introduce an intelligent method to search for the optimum wheel position for the designed groove with the known wheel geometry. According to practice, the basic parameters of the groove and wheel geometries are introduced, and the problems studied in this paper are explained. As a foundation, a robust algorithm is built to predict machined grooves with a series of equal distribution points and three parameters: the rake angle, core radius and groove width. The influence that the wheel positions have on groove geometries is then analyzed. Then, an objective function considering different machining requirements is built, and the optimum wheel positions are searched for while the function is solved. Furthermore, an enhanced niche particle swarm optimization (NPSO) algorithm is developed to solve the problem. Finally, 6 experiments are carried out to verify and analyze the algorithm. The results show that the algorithm can effectively find the desired wheel positions according to different machining precision requirements. Highlights: A wheel position searching method for cutting tool groove machining is proposed. A robust prediction model for the groove that will be machined is introduced. The wheel position parameters have different influences on the groove geometries. Different groove machining requirements is realized by an objective function. A customized niche particle swarm optimization algorithm is built for solution.