Study on Intelligent Prediction of Surface Roughness Based on Integration of Abrasive Characteristics and Process Parameters

被引：0
作者：
Fang C. [1 ]
Li H. [1 ]
Shen J. [1 ]
Wu X. [1 ]
机构：
[1] College of Mechanical Engineering and Automation, Huaqiao University, Xiamen
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2024年 / 60卷 / 07期
关键词：
abrasive machining; characteristic information; image processing; intelligent algorithm; surface roughness;
D O I：
10.3901/JME.2024.07.224
中图分类号：
学科分类号：
摘要：
Aiming at the problem that it is difficult to predict the surface roughness of the machined object effectively and accurately due to the comprehensive influence of the surface state of grinding tools and process parameters, an intelligent prediction method of surface roughness based on the integration of abrasive characteristics and processing parameters is proposed. Based on the collected diamond abrasive images on the tool surface, three key characteristic information of abrasive number per unit area, abrasive distribution uniformity and abrasive protrusion height on the tool surface are extracted by using image processing technology, and the effectiveness of the extracted characteristic information is verified. On this basis, a surface roughness intelligent prediction algorithm integrating abrasive characteristics and process parameters is proposed, the milling experiment of YG8 cemented carbide is carried out, and the machining results are compared with the predicted results. The results show that the proposed intelligent algorithm can greatly improve the accuracy and stability of surface roughness prediction, and its prediction accuracy can reach 95.6%, while the accuracy of regression model based on traditional process parameters is only 86.3%, which provides a reference for the intelligent prediction of surface roughness in abrasive machining. © 2024 Chinese Mechanical Engineering Society. All rights reserved.
引用
收藏
页码：224 / 235
页数：11
相关论文
共 34 条
  • [1] HONG T T, CUONG N V, KY L H, Et al., Effect of process parameters on surface roughness in surface grinding of 90CrSi tool steel[J], Solid State Phenomena, 305, pp. 191-197, (2020)
  • [2] WU Yuhou, WANG Qiang, WANG He, Et al., Internal grinding of silicon nitride ferrules and regression analysis, Journal of Shenyang Jianzhu University, 29, 1, pp. 156-162, (2013)
  • [3] MANDAL N, DOLOI B, MONDAL B., Surface roughness prediction model using zirconia toughened alumina (ZTA) turning inserts: Taguchi method and regression analysis, Journal of The Institution of Engineers (India):Series C, 97, pp. 77-84, (2016)
  • [4] CHAI Hua, HUANG Yun, WANG Yajie, Et al., Research on the optimization of the prediction model of magnesium alloy surface roughness, Mechanical Science and Technology for Aerospace Engineering, 31, 6, pp. 968-971, (2012)
  • [5] JOSHI K, PATIL B., Prediction of surface roughness by machine vision using principal components based regression analysis[J], Procedia Computer Science, 167, 9-12, pp. 382-391, (2020)
  • [6] GAO Chao, WANG Sheng, WANG Hui, Et al., Theoretical prediction and sensitivity analysis of surface roughness in belt grinding, Surface Technology, 47, 11, pp. 295-305, (2018)
  • [7] KHOIRUL EFFENDI M, SOEPANGKAT B O P, NOORCAHYO R, Et al., An analysis of frictional coefficient and surface roughness in surface grinding of SKD11 tool steel using minimum quantity lubrication (MQL) and dry techniques, IOP conference series: Materials Science and Engineering, 1034, 1, (2021)
  • [8] YU Tiefu, LV Yushan, SHU Qilin, Prediction and analysis of surface roughness of micro-grinding titanium alloy based on response surface method, Tool Engineering, 48, 4, pp. 79-82, (2014)
  • [9] HONG SON N, DUC TRUNG D, NGUYEN N., Surface roughness prediction in grinding process of the SKD11 steel by using response surface method[J], IOP Conference Series:Materials Science and Engineering, 758, 1, (2020)
  • [10] MALKIN S., Grinding technology: Theory and applications of machining with abrasives, (1989)
1234