Research on Analytical Model and DDQN-SVR Prediction Model of Turning Surface Roughness

被引：0

作者：

Chen C. ^{[1
]}

Lu J. ^{[1
,2
]}

Chen K. ^{[1
]}

Li Y. ^{[1
]}

Ma J. ^{[1
]}

Liao X. ^{[1
]}

机构：

[1] Guangxi Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology, Guangxi University, Nanning

[2] Department of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou

来源：

Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2021年 / 57卷 / 13期

关键词：

Deep reinforcement learning; Support vector regression; Surface roughness; Theoretical model; Turning;

D O I：

10.3901/JME.2021.13.262

中图分类号：

学科分类号：

摘要：

In turning process, the quality of parts is a problem that producers need to focus on. The surface roughness is an important index to evaluate the quality of parts. Selecting satisfactory cutting parameters to improve surface roughness can effectively improve part quality. In order to improve the prediction accuracy of surface roughness, a segmented analytical model of surface roughness theory is proposed based on the previous research. At the same time, double deep Q network (DDQN) is used to optimize the Support vector regression (SVR) to improve the predictive performance of the data-driven model. The environment design of DDQN to optimize the internal parameters of SVR is explored, and its optimization effect and stability are compared with other algorithms. Based on the turning experiment of 45 steel, the validity of segmented surface roughness theory model and DDQN-SVR prediction model is verified, which provides better technical support for the selection of cutting parameters based on surface roughness. © 2021 Journal of Mechanical Engineering.

引用

页码：262 / 272

页数：10

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