Directional diffusion of carbon atoms and lattice distortion in the high-speed changing dynamic temperature field – A novel method to manufacture high tribological performance cermets

Liu, Junbo; You, Qianbing; Huo, Yunliang; Xiong, Ji; Guo, Zhixing; Lu, Yang; Zhang, Hong; Zhao, Jianfeng; Wang, Longgang

doi:10.1016/j.ijrmhm.2022.105963

Cited by 1 publication

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cutting tool selection plays a primary role in improving productivity and exibility of the manufacturing, which involves imprecise, vague, and uncertain information [1,2]. However, with the rapid development of metallurgical technology [3,4], more and more types of cutting tool materials have been widely used in the cutting eld [5][6][7]. In addition, due to the differences in coating types, tool geometers, cutting-edge treatment states, manufacturers, and other factors [8,9], users have to face orders of magnitude of tool information and make multi-dimensional and multi-criteria decisions when adapting tools.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive cutting tool adaptation method of multi-feature part based on evolutionary game

Huo,

Zhou,

Liu

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The adaptation of cutting tools is the key link of machining, although a lot of work has been done to achieve tool selection for single features, relatively little research on tool scheme adaptation for multi-feature parts as a whole. In order to take into account the factors of matching, efficiency, cost, manufacturability, and environment, a comprehensive optimization method of a multi-feature tool scheme is proposed in this work. Firstly, the critical tool of a complex feature is defined, and a tool combination method with complex single features is proposed to generate an efficient tool strategy. Secondly, a tool scheme optimization model of multi-feature parts is constructed, where each machining feature is mapped to a game player in the model space, and the set of available tools is mapped to the player strategies. In addition, the classical evolutionary game algorithm is improved to adapt to the model, according to the large difference in the number and type of features of parts. Finally, a square test piece is taken as an example to verify the proposed method, and the results show that the method presented in the paper can efficiently obtain the customer-preferred tool scheme.

show abstract