Comprehensive Risk Assessment of Processes for Non-standard Customized Components

doi:10.3901/JME.260438

Abstract

Abstract: A comprehensive evaluation method that fuses multi-source knowledge is proposed to improve the machining efficiency of non-standard components, reduce manufacturing cost, and enhance the quality of machining risk assessment. Processing-parameter knowledge representation is carried out using interval fuzzy logic. From multiple dimensions—including part geometry and size, tool life, machine-tool life, and machining time—a dynamically updated, milling-process-based multi-source knowledge base is constructed. An expert confidence index and a random forest are introduced to resolve knowledge conflicts. Adaptive adjustment of machining parameters is achieved according to rule-based outputs. It is shown that the proposed model effectively identifies machining risks. After rule optimization, machining accuracy is improved by up to 65%, surface roughness is reduced by 57%, and machining time is shortened by 33%. Furthermore, compared with the conventional multi-objective genetic algorithm NSGA-II, superior quality-control performance is observed: machining accuracy is increased by 55% and surface roughness is reduced by 60%, demonstrating the multi-objective balancing advantage conferred by multi-source knowledge fusion.

Key words: machining risk assessment, parameter optimization, dynamic knowledge base, multi-source knowledge fusion

CLC Number:

TG156

LIU Qinghua, CAI Maolin, TONG Xiaomeng, LI Yibo, TANG Xiaolin, LONG Anlin. Comprehensive Risk Assessment of Processes for Non-standard Customized Components[J]. Journal of Mechanical Engineering, 2026, 62(11): 76-89.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260438

http://www.cjmenet.com.cn/EN/Y2026/V62/I11/76

References

[1] GÓMEZ A，OLMOS V，RACERO J，et al. Manufacturing of custom-made parts for assembly of large aircraft components[J]. Procedia Engineering，2015，132：1006-1013.
[2] 徐敬华，张树有，裘乐淼. 基于多粒度宏建模的MEMS非标结构设计方法[J]. 机械工程学报，2011，47(9)：97-104. XU Jinghua，ZHANG Shuyou，QIU Lemiao. Design method of non-standard structure for mems based on multi-granularity macro-modeling[J]. Journal of Mechanical Engineering，2011，47(9)：97-104.
[3] HIROGAKI T，AOYAMA E，OGAWA K，et al. Decision methodology of end-milling conditions using data-mining[J]. Precision Engineering，2011，35(2)：197-203.
[4] BAGIRAN H E. Risk assessment application in milling machines[J]. Ergonomi，2023，6(1)：1-18.
[5] KAHLOUL H，CHAIB R，BELLAOUAR A. Hazard identification and risk analysis for a reinforced concrete rolling mill[J]. World Journal of Engineering，2017，14(1)：1-6.
[6] INDRAWATI S，RIDWANSYAH M. Manufacturing continuous improvement using lean six sigma：an iron ores industry case application[J]. Procedia Manufacturing，2015，4：528-534.
[7] HUANG Weiwei，ZHANG Yang，ZHANG Xinquan，et al. Wall thickness error prediction and compensation in end milling of thin-plate parts[J]. Precision Engineering，2020，66：550-563.
[8] YADAV A K，KUMAR M，BAJPAI V，et al. FE modeling of burr size in high-speed micro-milling of Ti6Al4V[J]. Precision Engineering，2017，49：287-292.
[9] GAO Xikun，CHENG Xiang，LING Siying，et al. Research on optimization of micro-milling process for curved thin wall structure[J]. Precision Engineering，2022，73：296-312.
[10] BHAVSAR S N，ARAVINDAN S，RAO P V，et al. Investigating material removal rate and surface roughness using multi-objective optimization for focused ion beam (FIB) micro-milling of cemented carbide[J]. Precision Engineering，2015，40：131-138.
[11] DU C，HO C L，KAMINSKI J，et al. Prediction of product roughness，profile，and roundness using machine learning techniques for a hard turning process[J]. Advances in Manufacturing，2021，9(2)：206-215.
[12] 娄维达，秦国华，王华敏，等. 基于复化Cotes积分和神经网络的稳定铣削工艺参数多目标优化方法[J]. 机械工程学报，2023，59(17)：279-290. LOU Weida，QIN Guohua，WANG Huamin，et al. Multi-objective optimization method of stable milling process parameters based on BP neural network and composite Cotes integration[J]. Journal of Mechanical Engineering，2023，59(17)：279-290.
[13] STARK A，FERM K，HANSON R，et al. Hybrid digital manufacturing：Capturing the value of digitalization[J]. Journal of Operations Management，2023，69(6)：890-910.
[14] 张党，赵永宣，王振军，等. 数据-知识混合驱动的离散制造系统智能控制体系构架研究[J]. 机械工程学报，2024，60(6)：1-10，20. ZHANG Dang，ZHAO Yongxuan，WANG Zhenjun，et al. Research on data-knowledge driven intelligent control architecture for discrete manufacturing system[J]. Journal of Mechanical Engineering，2024，60(6)：1-10，20.
[15] CHU Wangwei，LI Yingguang，LIU Changqing，et al. A manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation for aircraft structural parts[J]. International Journal of Production Research，2014，52(11)：3239-3258.
[16] 刘达新，王科，刘振宇，等. 基于数据融合与知识推理的机器人装配单元数字孪生建模方法研究[J]. 机械工程学报，2024，60(5)：36-50. LIU Daxin，WANG Ke，LIU Zhenyu，et al. Research on digital twin modeling method for robotic assembly cell based on data fusion and knowledge reasoning[J]. Journal of Mechanical Engineering，2024，60(5)：36-50.
[17] DUAN Jingjing，DUAN Yang. Toward a framework of extracting typical machining process routines based on knowledge representation learning[J]. Advanced Engineering Informatics，2024，60：102431.
[18] HUA Yiwei，WANG Ru，WANG Zuoxu，et al. Knowledge graph with deep reinforcement learning for intelligent generation of machining process design[J]. Journal of Engineering Design，2025，36(11)：2072-2106.
[19] YANG Chao，YUAN Zheng，TU Xinyi，et al. Ontology-based knowledge representation of industrial production workflow[J]. Advanced Engineering Informatics，2023，58：102185.
[20] HASHMI K，GRAHAM I D，MILLS B. Data selection for turning carbon steel using a fuzzy logic approach[J]. Journal of Materials Processing Technology，2003，135(1)：44-58.
[21] TORRES-TREVIÑO L M，ESCAMILLA-SALAZAR I G，GONZÁLEZ-ORTÍZ B，et al. An expert system for setting parameters in machining processes[J]. Expert Systems with Applications，2013，40(17)：6877-6884.
[22] HUANG Hongzhong，LI Yonghua，XUE L H，et al. A comprehensive evaluation model for assessments of grinding machining quality[J]. Key Engineering Materials，2005，291-292：157-162
[23] SUN Qun，ZHANG Weimin，LI Pengzhong，et al. A comprehensive evaluation approach for machining operation based on fuzzy AHP [C]// 20144th International Conference on Mechanical Science and Technology，Sanya，Hainan Island，China，2014，472：1079.
[24] MA Yumin，LI Shengyi，LU Xiaoyu，et al. GAN based data analysis and mining for smart shop floor scheduling[C]// 2021 IEEE 17th International Conference on Automation Science and Engineering，Lyon，France，2021，651–656.
[25] SHI Peng，TONG Xiaomeng，CAI Maolin，et al. A novel 2.5D machining feature recognition method based on ray blanking algorithm[J]. Journal of Intelligent Manufacturing，2024，35(4)：1585–1605.
[26] 刘强，李忠群. 数控铣削加工过程仿真与优化：建模、算法与工程应用[M]. 北京：航空工业出版社，2011. LIU Qiang，LI Zhongqun. Simulation and optimization of CNC milling process：Modeling，algorithms，and engineering applications[M]. Beijing：Aviation Industry Press，2011.
[27] SHI Peng，TONG Xiaomeng，QU Hongquan，et al. Rapid generation of contour parallel toolpaths for 2.5D closed cavity based on accurate discrete medial axis transform[J]. Precision Engineering，2025，92：231-252.