Energy consumption considering tool wear and optimization of cutting parameters in micro milling process. (15th July 2020)

• Record Type: Journal Article
• Authors: Zhang, Xuewei; Yu, Tianbiao; Dai, Yuanxing; Qu, Sheng; Zhao, Ji
• Is Part Of: International journal of mechanical sciences. Volume 178(2020)
• Publication Date: 2020-07-15
• Subjects: Micro milling -- Energy consumption -- Stochastic tool wear -- Cutting parameters optimization; Mechanical engineering -- Periodicals; Génie mécanique -- Périodiques; Mechanical engineering; Maschinenbau; Mechanik; Zeitschrift; Periodicals; 621.05
• DOI: 10.1016/j.ijmecsci.2020.105628
• Languages: English
• ISSNs: 0020-7403

Abstract

Highlights: An improved analytical energy consumption model is presented for micro milling. The effect of stochastic tool wear is integrated in energy consumption model. Particle filter algorithm is used to obtain tool wear with online measurement data. The hybrid CS–GWO algorithm is used to determine optimal cutting parameters. Abstract: Micro milling process aims to manufacture complex micro/meso structures, and the reduction of material removal volume determines the possible decrease of the total energy use, which would put less pressure on the environment. However, the energy consumption of micro milling influenced by tool wear and tool run-out would be augmented and result in the drawback of more energy consumption. In order to reduce the energy consumption of micro milling process, a new analytical energy consumption model and the related optimization of cutting parameters are presented in this paper. Although the influence of tool wear is inevitable, it hasn't been thoroughly concerned in the existing energy consumption models. Therefore, the stochastic tool wear progression, which can be obtained from a probabilistic approach based on the online measured cutting forces, is integrated into the proposed energy model. In addition, the process nonlinearities caused by tool run-out and the trochoidal trajectories of cutting edge are also considered in the model. With the developed prediction model of energy consumption, a hybrid cuckoo search and grey wolf algorithm is used to determine the optimum cutting parameters for minimizing the total energy consumption. The micro milling experiments are performed to validate the accuracy and availability of the proposed energy consumption model and the optimization method. The improved optimization method based on the proposed energy model can reduce the energy consumption by 7.89% compared with the empirical selection. Graphical abstract: Image, graphical abstract