Prediction and optimization of robot processing technology based on neural network and genetic algorithm

WU Fusen

doi:10.13394/j.cnki.jgszz.2024.0045

Volume 45 Issue 2

Apr. 2025

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Article Navigation > Diamond & Abrasives Engineering > 2025 > 45(2): 256-265

WU Fusen. Prediction and optimization of robot processing technology based on neural network and genetic algorithm[J]. Diamond & Abrasives Engineering, 2025, 45(2): 256-265. doi: 10.13394/j.cnki.jgszz.2024.0045

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WU Fusen. Prediction and optimization of robot processing technology based on neural network and genetic algorithm[J]. Diamond & Abrasives Engineering, 2025, 45(2): 256-265. doi: 10.13394/j.cnki.jgszz.2024.0045

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Prediction and optimization of robot processing technology based on neural network and genetic algorithm

doi: 10.13394/j.cnki.jgszz.2024.0045

WU Fusen

National Special Robot Product Quality Supervision and Inspection Center, Fujian Special Equipment Inspection Research Institute, Quanzhou 362021, Fujian, China

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Received Date: 2024-03-12
Accepted Date: 2024-06-17
Rev Recd Date: 2024-05-27

Available Online: 2024-06-21

Abstract

Abstract

Objectives With the rapid development of industrial automation and intelligent manufacturing, the application of industrial robots in the stone processing industry has garnered increasing attention. However, compared to other advanced manufacturing sectors, the mechanization, automation and intelligence of the stone processing industry remain relatively underdeveloped. This study aims to explore the optimal processing methods for stone industrial robots' grinding operations using BP neural networks and genetic algorithms, taking the processing of sandstone as an example. Methods Taking the KUKA KR60L30HA industrial robot equipped with a brazed flat grinding head as the representative, the effects of different grinding process parameters on grinding force signals were systematically analyzed by the orthogonal test method. Firstly, the grinding force signal data were collected using different grinding test settings. Subsequently, a three-layer grinding force prediction model based on a BP neural network was established, and linear regression analysis was conducted using the orthogonal experimental data as samples to compare the predicted values with the experimental values. Finally, the genetic algorithm was applied to optimize grinding process parameters with material removal rates as the indicator. Results The grinding process parameters have significant effects on grinding forces, but the order of major and secondary effects of different parameters varies with grinding force components. The order of influence on tangential grinding force is the axial cutting depth a_p, followed by radial cutting depth a_e, the feed rate v_w and the spindle speed n, while the order of influence on normal grinding force is a_p, v_w, a_e and n. In contrast, the order of influence of axial grinding force is n, a_e, v_w and a_p, while the total grinding force is most affected by v_w, in the order of v_w, a_e, n and a_p. Additionally, all components of grinding force generally increase with the rise of a_e, a_p and v_w, and decrease with the increase of spindle speed n. As the radial cutting depth a_e increases, the ratio of normal to tangential grinding force shows a continuous downward trend. As the axial cutting depth a_p increases, the grinding force ratio fluctuates within a certain range. As the spindle speed n increases, the grinding force ratio first increases, then decreases, and then slightly increases. When the feed rate v_w increases, the grinding force ratio shows an initial decrease followed by an increasing trend. After training and predicting using the BP neural network model, the predicted values of tangential, normal and axial grinding forces are compared with the experimental data. The maximum absolute relative error of the axial grinding force is 7.84%, and the correlation coefficient of the model is as high as 0.998 09, indicating significant prediction accuracy of the mpdel. The optimal process parameter combination determined through genetic algorithm optimization is a radial cutting depth of 2.28 mm, an axial cutting depth of 2.98 mm, a spindle speed of 9 586.65 r/min and a feed rate of 2 207.67 mm/min. Under the optimal process parameter combination, the predicted material removal rate of the workpiece is 14 999.79 mm³/min, with a relative error of −5.37% compared to the actual experimental value of 14 194.44 mm³/min. This further demonstrates the effectiveness of the proposed optimization strategy. Conclusions The constructed grinding force prediction and process parameter optimization model has achieved systematic analysis and optimization of grinding force in robot sandstone processing. This model can clearly reveal the role of various grinding process parameters in machining and reflect their importance in improving machining efficiency and material removal rate. The changing trend of grinding force varies with different processing conditions, and there are significant differences in the influences of different parameter combinations on grinding forces, especially the influences of axial cutting depth and the feed rate, which are particularly significant. The optimal process parameter combination for material removal rate in stone processing is determined through BP neural network and genetic algorithm, and the relative error between the predicted value and the experimental value is relatively small.
- robot processing,
- orthogonal test,
- BP neural network,
- genetic algorithm,
- optimization of process parameters

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References

[1]	张永长, 杨涛, 纪静, 等. 厦门市翔安区2017年石材加工行业职业卫生现状调查 [J]. 职业卫生与应急救援,2018,36(4):328-330,353. doi: 10.16369/j.oher.issn.1007-1326.2018.04.013 ZHANG Yongchang, YANG Tao, JI Jing, et al. Investigation on occupational health work of stone industries in Xiang’an District of Xiamen City in 2017 [J]. Occup Health and Emergency Rescue,2018,36(4):328-330,353. doi: 10.16369/j.oher.issn.1007-1326.2018.04.013
[2]	曾晓立, 李英娥, 林立, 等. 某厂石雕作业工人生活质量调查 [J]. 中国工业医学杂志,2018,31(2):135-137. doi: 10.13631/j.cnki.zggyyx.2018.02.023 ZENG Xiaoli, LI Yinge, LIN Li, et al. Survey on life quality of stone carving workers [J]. Chinese Journal of Industrial Medicine,2018,31(2):135-137. doi: 10.13631/j.cnki.zggyyx.2018.02.023
[3]	NGUYEN T M, SURDILOVIC D, KRUGER J, et al. Efficient application of industrial robots for automated stone sculptures milling: 1st international conference on stone and concrete machining [C]// Hannover: Institute of Production Engineering and Machine Tool, 2011.
[4]	黄吉祥, 尹方辰, 黄身桂, 等. 机器人石材雕刻粗加工能耗建模与优化分析 [J]. 华侨大学学报(自然科学版),2024,45(4):471-477. doi: 10.11830/ISSN.1000-5013.202311024 HUANG Jixiang, YIN Fangchen, HUANG Shengui, et al. Modeling and optimization analysis of energy consumption in rough machining of robotic stone carving [J]. Journal of Huaqiao University (Natural Science),2024,45(4):471-477. doi: 10.11830/ISSN.1000-5013.202311024
[5]	周进. 基于时间尺度函数的工业机器人加工系统节能优化方法与典型应用研究 [D]. 重庆: 重庆大学, 2022. ZHOU Jin. Time-scaling-based energy-saving optimization method and typical application study of robot machining system [D]. Chongqing: Chongqing University, 2022.
[6]	籍永建, 姚利诚. 机器人铣削加工颤振自适应识别方法研究 [J]. 中国机械工程,2023,34(18):2165-2176. doi: 10.3969/j.issn.1004-132X.2023.18.003 JI Yongjian, YAO Licheng. Research on self-adaptive chatter recognition method for robotic milling [J]. China Mechanical Engineering,2023,34(18):2165-2176. doi: 10.3969/j.issn.1004-132X.2023.18.003
[7]	LI J, LI B, SHEN N, et al. Effect of the cutter path and the workpiece clamping position on the stability of the robotic milling system [J]. International Journal of Advanced Manufacturing Technology,2017,89(9/10/11/12):2919-2933. doi: 10.1007/s00170-016-9759-x
[8]	陈齐志. 工业机器人铣削系统颤振分析与加工精度提升方法研究 [D]. 济南: 山东大学, 2022. CHEN Qizhi. Research on chatter analysis and machining accuracy improvement method of industrial robotic milling system [D]. Jinan: Shandong University, 2022.
[9]	巩亚东, 赵显力, 张伟健, 等. 机器人砂带磨削单磨粒材料去除影响因素 [J]. 东北大学学报(自然科学版),2023,44(9):1285-1291. doi: 10.12068/j.issn.1005-3026.2023.09.009 GONG Yadong, ZHAO Xianli, ZHANG Weijian, et al. Factors influencing single abrasive material removal for robotic abrasive belt grinding [J]. Journal of Northeastern University ( Natural Science),2023,44(9):1285-1291. doi: 10.12068/j.issn.1005-3026.2023.09.009
[10]	陈庚, 向华, 叶寒, 等. 考虑法向接触力变化的机器人砂带磨削参数研究 [J]. 中国机械工程,2023,34(7):803-811,820. doi: 10.3969/j.issn.1004-132X.2023.07.006 CHEN Geng, XIANG Hua, YE Han, et al. Research on robot abrasive belt grinding parameters considering the change of normal contact force [J]. China Mechanical Engineering,2023,34(7):803-811,820. doi: 10.3969/j.issn.1004-132X.2023.07.006
[11]	李东阳, 谢海龙, 王清辉, 等. 基于力反馈的机器人砂带磨削虚拟示教轨迹规划方法 [J]. 现代制造工程,2022,(5):30-35. doi: 10.16731/j.cnki.1671-3133.2022.05.005 LI Dongyang, XIE Hailong, WANG Qinghui, et al. Haptic feedback based virtual teaching method of toolpath planning for robot belt grinding processes [J]. Modern Manufacturing Engineering,2022,(5):30-35. doi: 10.16731/j.cnki.1671-3133.2022.05.005
[12]	李弘毅, 阮玉镇, 汤绍钊, 等. 机器人磨削工艺参数对系统固有频率影响的试验研究 [J]. 机床与液压,2022,50(3):49-53. doi: 10.3969/j.issn.1001-3881.2022.03.009 LI Hongyi, RUAN Yuzhen, TANG Shaozhao, et al. Experimental study on the influence of robot grinding parameters on system natural frequency [J]. Machine Tool & Hydraulics,2022,50(3):49-53. doi: 10.3969/j.issn.1001-3881.2022.03.009
[13]	杨平, 杨冰雅, 肖硕男. 合模线飞边在线测量与机器人智能磨抛加工技术 [J]. 制造业自动化,2024,46(2):129-135. doi: 10.3969/j.issn.1009-0134.2024.02.026 YANG Ping, YANG Bingya, XIAO Shuonan. On-line measurement and intelligent grinding processing technology by using robots for joint fin [J]. Manufacturing Automation,2024,46(2):129-135. doi: 10.3969/j.issn.1009-0134.2024.02.026
[14]	王超, 郑乾健, 肖聚亮, 等. 考虑时变磨损的机器人磨抛参数优化研究 [J]. 天津大学学报(自然科学与工程技术版),2023,56(2):127-136. doi: 10.11784/tdxbz202110006 WANG Chao, ZHENG Qianjian, XIAO Juliang, et al. Research on optimization of robot grinding and polishing parameters considering time-varying wear [J]. Journal of Tianjin University (Science and Technology),2023,56(2):127-136. doi: 10.11784/tdxbz202110006
[15]	田凤杰, 车长林, 李孝辉, 等. 机器人磨抛微量去除飞机蒙皮包铝工艺试验 [J]. 航空制造技术,2022,65(13):56-62. doi: 10.16080/j.issn1671-833x.2022.13.056 TIAN Fengjie, CHE Changlin, LI Xiaohui, et al. Experiment on micro-removal for aluminum clad of aircraft skin by robotic grinding [J]. Aeronautical Manufacturing Technology,2022,65(13):56-62. doi: 10.16080/j.issn1671-833x.2022.13.056
[16]	黄吉祥, 刘舒颖, 黄辉, 等. 机械臂加工花岗岩的力和工具磨损特性 [J]. 华侨大学学报(自然科学版),2018,39(2):159-165. doi: 10.11830/ISSN.1000-5013.201712065 HUANG Jixiang, LIU Shuying, HUANG Hui, et al. Forces and tool wear characteristics in granite grinding by robotic [J]. Journal of Huaqiao University (Natural Science),2018,39(2):159-165. doi: 10.11830/ISSN.1000-5013.201712065
[17]	武沙桐. 磨削力约束下的立体石雕机器人加工轨迹规划研究 [D]. 厦门: 华侨大学, 2022. WU Shatong. Research on robot trajectory planning of three-dimensional stone carving based on grinding force constraint [D]. Xiamen: Huaqiao University, 2022.
[18]	王雷. 基于BP神经网络的钛合金加工切削力预测模型研究 [D]. 天津: 天津理工大学, 2016. WANG Lei. Research on the cutting force prediction model of titanium alloy with BP neural network [D]. Tianjin: Tianjin University of Technology, 2016.
[19]	刘寿军, 肖开永, 牛腾, 等. 基于神经网络-遗传算法的泵体零件热锻模具磨损与应力分析 [J]. 锻压技术,2024,49(2):208-214. doi: 10.13330/j.issn.1000-3940.2024.02.026 LIU Shoujun, XIAO Kaiyong, NIU Teng, et al. Analysis on wear and stress of hot forging die for pump body parts based on neural network-genetic algorithm [J]. Forging & Stamping Technology,2024,49(2):208-214. doi: 10.13330/j.issn.1000-3940.2024.02.026
[20]	王晶. 基于神经网络的机器人加工花岗石多目标工艺优化 [D]. 厦门: 华侨大学, 2020. WANG Jing. Multi-objective process parameters optimization of granite robot machining based on neural network [D]. Xiamen: Huaqiao University, 2020.
[21]	梁强, 张贤明, 杜彦斌, 等. 基于灰色关联分析的齿环热精锻成形工艺参数优化 [J]. 计算机集成制造系统,2022,28(4):1020-1029. doi: 10.13196/j.cims.2022.04.006 LIANG Qiang, ZHANG Xianming, DU Yanbin, et al. Parameters optimization in hot precision forging process of synchronizer ring based on grey relational analysis [J]. Computer Integrated Manufacturing Systems,2022,28(4):1020-1029. doi: 10.13196/j.cims.2022.04.006