New Paradigm for Reliable Manufacturing of CNC Machine Tools：Upgrading from “Functional Possibilities” to “Performance Reliability”

doi:10.3901/JME.2025.12.293

Abstract

Abstract: Numerical control machine tools, as the core equipment in high-end manufacturing, are crucial and fundamental to modern manufacturing development. Traditional machine tool manufacturing mainly focuses on the “functional possibility”, i.e., satisfying the basic functionalities of NC machine tools. However, with the rapid advancement of intelligent manufacturing and high-end equipment, simply meeting functional requirements can no longer address the challenges of increasingly complex manufacturing environments. Therefore, this paper proposes a novel paradigm for reliable manufacturing of NC machine tools, shifting the focus from “functional possibility” to “performance reliability”. Based on key life-cycle technologies, including forward reliability design, reliability process control, an optimized reliability testing system, and in-service health management, this paradigm establishes an innovative, multidisciplinary collaborative mechanism to systematically enhance the performance reliability and stability of machine tools. It aims to provide new theoretical guidance and technical pathways for the NC machine tool industry, thereby supporting the sustainable development of high-end equipment manufacturing.

Key words: CNC machine tools, reliable manufacturing, whole lifecycle, key technologies

CLC Number:

TG659

LIU Zhifeng, CHEN Chuanhai, GUO Jinyan, LI Zhijie. New Paradigm for Reliable Manufacturing of CNC Machine Tools：Upgrading from “Functional Possibilities” to “Performance Reliability”[J]. Journal of Mechanical Engineering, 2025, 61(12): 293-304.

References

[1] 刘志峰，滕学政，刘炳业，等. 高端数控机床的现状和发展[J]. 机床与液压，2024，52(22)：1-7 LIU Zhifeng，TENG Xuezheng，LIU Bingye，et al. Current situation and development of high-end CNC machine tools[J]. Machine Tool & Hydraulics，2024，52(22)：1-7
[2] 胡晓波. 基于元动作单元的数控机床故障分析与维修决策研究[D]. 重庆：重庆大学，2020. HU Xiaobo. Research on failure analysis and repair decision making of CNC machine tools based on meta-action units[D]. Chongqing：Chongqing University，2020.
[3] 王先芝，芮执元，任丽娜. 基于三角模糊数TOPSIS法的数控机床危害性分析[J]. 机械设计，2017，34(3)：48-53. WANG Xianzhi，RUI Jiyuan，REN Lina. Hazard analysis of CNC machine tools based on triangular fuzzy number TOPSIS method[J]. Machine Design，2017，34(3)：48-53.
[4] 胡炜. 考虑多元因素影响的数控机床故障过程建模与评估方法[D]. 长春：吉林大学，2022. HU Wei. A method for modeling and evaluating the failure process of CNC machine tools considering the influence of multiple factors[D]. Changchun：Jilin University，2022.
[5] 康永博. 数控齿轮机床的可靠性分配与评估研究[D]. 北京：北京工业大学，2022. KANG Yongbo. Research on reliability assignment and evaluation of CNC gear machine tools[D]. Beijing：Beijing Institute of Technology，2022.
[6] 段佳瑞. 考虑故障相关性的数控机床可靠性分配方法研究[D]. 长春：吉林大学，2023. DUAN Jarui. Research on reliability allocation method of CNC machine tools considering fault correlation[D]. Changchun：Jilin University，2023.
[7] 钟源. 考虑多元因素影响的数控机床精细化可靠性分配方法研究[D]. 长春：吉林大学，2023. ZHONG Yuan. Research on refined reliability allocation method of CNC machine tools considering the influence of multiple factors[D]. Changchun：Jilin University，2023.
[8] 宋婷婷，赵子任，杜世昌，等. 基于马尔科夫模型的关联多工序装配系统装配质量分析[J]. 上海交通大学学报，2018，52(3)：324-331. SONG Tingting，ZHAO Ziren，DU Shichang，et al. Assembly quality analysis of associative multi-process assembly system based on Markov model[J]. Journal of Shanghai Jiao Tong University，2018，52(3)：324-331.
[9] 张根保，葛红玉，刘佳，等. 可靠性驱动的装配过程建模及预测方法[J]. 计算机集成制造系统，2012，18(2)：349-355. ZHANG Genbao，GE Hongyu，LIU Jia，et al. Reliability-driven assembly process modeling and prediction method[J]. Computer Integrated Manufacturing Systems，2012，18(2)：349-355.
[10] 吴军，邓超，邵新宇，等. 基于加工动力学模型的工艺可靠性仿真方法[J]. 系统工程理论与实践，2011，31(7)：1403-1408. WU Jun，DANG Chao，SHAO Xinyu，et al. A process reliability simulation method based on machining dynamics model[J]. Systems Engineering Theory and Practice，2011，31(7)：1403-1408.
[11] 孔繁森. 制造系统可靠性分析的框架与动力学机制[J].机械工程学报，2020，56(20)：223-236. KONG Fansen. A framework and dynamical mechanism for reliability analysis of manufacturing systems[J]. Journal of Mechanical Engineering，2020，56(20)：223-236.
[12] 贾玉辉. 生产线可用性的指标体系及其建模[D]. 长春：吉林大学，2018. JIA Yuhui. Indicator system of production line availability and its modeling[D]. Changchun：Jilin University，2018.
[13] 柳剑. 制造系统运行可靠性分析与维修保障策略研究[D]. 重庆：重庆大学，2014. LIU Jian. Research on operation reliability analysis and maintenance assurance strategy of manufacturing system[D]. Chongqing：Chongqing University，2014.
[14] 李娟. 面向制造过程的质量与可靠性联合优化研究[D].重庆：重庆大学，2017. LI Juan. Research on joint optimization of quality and reliability for manufacturing process[D]. Chongqing：Chongqing University，2017.
[15] 池佳伟. M数控车床典型工况的载荷谱编制方法研究[D]. 长春：吉林大学，2022. CHI Jiawei. Research on the preparation method of load spectrum for typical working conditions of M CNC lathe[D]. Changchun：Jilin University，2022.
[16] 黄鑫. 数控车床切削力载荷谱的采集与编制[D]. 沈阳：东北大学，2018. HUANG Xin. Acquisition and preparation of cutting force load spectrum of CNC lathe[D]. Shenyang：Northeastern University，2018.
[17] 熊苗. 基于数控机床KPI的关键滚动功能部件载荷谱试验及应用研究[D]. 南京：南京理工大学，2020. XIONG Miao. Experimental and applied research on load spectrum of key rolling functional components based on KPI of CNC machine tools[D]. Nanjing：Nanjing University of Science and Technology，2020.
[18] 陈栋樑. 弧齿锥齿轮机床载荷谱编制研究[D]. 重庆：重庆理工大学，2021. CHEN Dongliang. Study on the load spectra of curved bevel gear machine tools[D]. Chongqing：Chongqing University of Technology，2021.
[19] 郭劲言. 模型不确定的电主轴加速退化试验多目标优化设计方法[D]. 长春：吉林大学，2021. GUO Jinyan. Multi-objective optimization design method for accelerated degradation test of electric spindles with model uncertainty[D]. Changchun：Jilin University，2021.
[20] 朱晓，冉琰，张根保，等. 数控机床的可靠性评估方法综述[J]. 兵器装备工程学报，2023，44(8)：88-97. ZHU Xiao，RAN Yan，ZHANG Genbao，et al. An overview of reliability assessment methods for CNC machine tools[J]. Journal of Engineering of Armament Equipment，2023，44(8)：88-97.
[21] 马晓琳. 面向离合器装配的质量监控及过程能力指数研究[D]. 天津：天津科技大学，2022. MA Xiaolin. Research on quality control and process capability index for clutch assembly[D]. Tianjin：Tianjin University of Science and Technology，2022.
[22] 杨恺霖. 制造数据与工艺知识库构建及应用技术研究[D]. 西安：西安电子科技大学，2022. YANG Kailin. Research on manufacturing data and process knowledge base construction and application technology[D]. Xi’an：Xi’an University of Electronic Science and Technology，2022.
[23] 朱本正. 卧式数控车床制造一致性分析与评价研究[D].长春：吉林大学，2024. ZHU Benzheng. Research on manufacturing consistency analysis and evaluation of horizontal CNC lathe[D]. Changchun：Jilin University，2024.
[24] JIN T，CHEN C，GUO J，et al. Double-classifier adversarial learning for fault diagnosis of rotating machinery considering cross domains[J]. Mechanical Systems and Signal Processing，2024，216：111490.
[25] LIN Z，LIU Z，YAN J，et al. Digital thread-driven cloud-fog-edge collaborative disturbance mitigation mechanism for adaptive production in digital twin discrete manufacturing workshop[J]. International Journal of Production Research，2024，1(1)：1-29.