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Towards comprehensive digital evaluation of low-carbon machining process planning

Published online by Cambridge University Press:  25 July 2022

Zhaoming Chen Open the ORCID record for Zhaoming Chen [Opens in a new window] ,
Jinsong Zou  and
Wei Wang
Zhaoming Chen*
Affiliation:
Chongqing University, Chongqing 400044, China Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China
Jinsong Zou
Affiliation:
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
Wei Wang
Affiliation:
College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China
*
Author for correspondence: Zhaoming Chen, E-mail: zhaomingc_sc@163.com
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Abstract

Low-carbon process planning is the basis for the implementation of low-carbon manufacturing technology. And it is of profound significance to improve process executability, reduce environmental pollution, decrease manufacturing cost, and improve product quality. In this paper, based on the perceptual data of parts machining process, considering the diversity of process planning schemes and factors affecting the green manufacturing, a multi-level evaluation criteria system is established from the aspects of processing time, manufacturing cost and processing quality, resource utilization, and environmental protection. An integrated evaluation method of low-carbon process planning schemes based on digital twins is constructed. Each index value is normalized by the polarized data processing method, its membership is determined by the fuzzy statistical method, and the combination weight of each index is determined by the hierarchical entropy weight method to realize the organic combination of theoretical analysis, practical experience, evaluation index, and process factors. The comprehensive evaluation of multi-process planning schemes is realized according to the improved fuzzy operation rules, and the best process planning solution is finally determined. Finally, taking the low-carbon process planning of an automobile part as an example, the feasibility and effectiveness of this method are verified by the evaluation of three alternative process planning schemes. The results show that the method adopted in this paper is more in line with the actual production and can provide enterprises with the optimal processing scheme with economic and environmental benefits, which may be helpful for more data-driven manufacturing process optimization in the future.

Keywords

Combined weightcomprehensive evaluationdigital twinlow-carbon manufacturingprocess planning
Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e21
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Ball, PD, Evans, S, Levers, A and Ellison, D (2009) Zero carbon manufacturing facility – towards integrating material, energy, and waste process flows. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 223, 1085–1096.CrossRefGoogle Scholar
Cheng, HQ, Cao, HJ, Li, HC and Luo, Y (2013) Decision-making model of mechanical components based on carbon benefit and its application. Computer Integrated Manufacturing Systems 19, 2018–2025.Google Scholar
Das, A (2020) Multivariate statistical monitoring strategy for an automotive manufacturing part facility. Materials Today: Proceedings 27, 2914–2917.Google Scholar
Debroy, T, Zhang, W, Turner, J and Babu, SS (2016) Building digital twins of 3D printing machines. Scripta Materialia 135, 119–124.CrossRefGoogle Scholar
Gao, X, Mou, W and Peng, Y (2016) An intelligent process planning method based on feature-based history machining data for aircraft structural parts. Procedia CIRP 56, 585–589.CrossRefGoogle Scholar
Grieves, MW (2005) Product lifecycle management: the new paradigm for enterprises. International Journal of Product Development 2, 1–8.CrossRefGoogle Scholar
Grieves, MW (2011) Virtually Perfect: Driving Innovative and Lean Products Through Product Lifecycle Management. Cocoa Beach, FL, USA: Space Coast Press.Google Scholar
Gutowski, TG (2007) The carbon and energy intensity of manufacturing. In 40th CIRP International Manufacturing Systems Seminar at Liverpool University, Liverpool, UK, May 30–June 1.Google Scholar
Jin, Y, Du, J and He, Y (2017) Optimization of process planning for reducing material consumption in additive manufacturing. Journal of Manufacturing Systems 44, 65–78.CrossRefGoogle Scholar
Kholopov, VA, Antonov, SV, Kurnasov, EV and Kashirskaya, EN (2019) Digital twins in manufacturing. Russian Engineering Research 39, 1014–1020.CrossRefGoogle Scholar
Kumar, S (2019) Knowledge-based expert system in manufacturing planning: state-of-the-art review. International Journal of Production Research 57, 4766–4790.CrossRefGoogle Scholar
Li, CB, Cui, LG, Liu, F and Li, PY (2013) Carbon emissions quantitative method of machining system based on generalized boundary. Computer Integrated Manufacturing Systems 19, 2229–2236.Google Scholar
Li, C, Rong, M, Chang, Z, Zhang, D and Ying, X (2015) Ying decision-making of process route considering process planning experience and manufacturing stability. Journal of Computer-Aided Design & Computer Graphics 12, 2384–2392.Google Scholar
Lian, K, Zhang, C, Shao, X and Liang, G (2012) Optimization of process planning with various flexibilities using an imperialist competitive algorithm. International Journal of Advanced Manufacturing Technology 59, 815–828.CrossRefGoogle Scholar
Liu, C, Liu, SG, Xie, RJ and Ma, HC (2014) Integrated optimization model of process route and tolerance design. Journal of Machine Design 10, 40–44.Google Scholar
Mayyas, AT, Qattawi, A, Mayyas, AR and Omar, MA (2012) Life cycle assessment-based selection for a sustainable lightweight body-in-white design. Energy 39, 412–425.CrossRefGoogle Scholar
Meier, H and Shi, XQ (2011) CO2 emission assessment: a perspective on low-carbon manufacturing. Advanced Materials Research 356–360, 1781–1785.CrossRefGoogle Scholar
Munoz, AA and Sheng, P (1995) An analytical approach for determining the environmental impact of machining processes. Journal of Materials Processing Technology 53, 736–758.CrossRefGoogle Scholar
Mv, A, Sm, B, Bg, C, Pv, A and Bp, A (2019) Integrating simulation and optimization for process planning and scheduling problems. Computer-Aided Chemical Engineering 46, 1441–1446.Google Scholar
Narita, H, Kawamura, H, Norihisa, T, Chen, L, Fujimoto, H and Hasebe, T (2006) Development of prediction system for environmental burden for machine tool operation. JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing 49, 1188–1195.Google Scholar
Pai, Z and Kendrik, Y (2020) Product family design and optimization: a digital twin-enhanced approach. Procedia CIRP 93, 246–250.Google Scholar
Pakkar, SM (2016) Multiple attribute grey relational analysis using DEA and AHP. Complex & Intelligent Systems 2, 243–250.CrossRefGoogle Scholar
Pakkar, MS (2017) Fuzzy multi-attribute grey relational analysis using DEA and AHP. Proceedings of the Eleventh International Conference on Management Science and Engineering Management. Cham: Springer, pp. 695–707.Google Scholar
Pei, WA and Ming, LB (2021) A digital twin-based big data virtual and real fusion learning reference framework supported by industrial internet towards smart manufacturing-science direct. Journal of Manufacturing Systems 58, 16–32.Google Scholar
Rafiei, FM, Manzari, SM and Bostanian, S (2011) Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence. Expert Systems with Applications 38, 10210–10217.CrossRefGoogle Scholar
Research Group for Research on New Mode and Business Model of Manufacturing Led by New-Generation Artificial Intelligence Technology (2018) Research on new mode and business model of manufacturing led by new-generation artificial intelligence technology. Strategic Study of CAE 20, 66–72.Google Scholar
Saravanan, A, Jerald, J and Rani, A (2020) An explicit methodology for manufacturing cost-tolerance modeling and optimization using the neural network integrated with the genetic algorithm. Artificial Intelligence for Engineering Design Analysis and Manufacturing 34, 1–14.CrossRefGoogle Scholar
Schnoes, F and Zaeh, MF (2019) Model-based planning of machining operations for industrial robots. Procedia CIRP 82, 497–502.CrossRefGoogle Scholar
Scipioni, A, Manzardo, A, Mazzi, A and Mastrobuono, M (2012) Monitoring the carbon footprint of products: a methodological proposal. Journal of Cleaner Production 36, 94–101.CrossRefGoogle Scholar
Shin, SJ, Woo, J and Rachuri, S (2017) Energy efficiency of milling machining: component modeling and online optimization of cutting parameters. Journal of Cleaner Production 161, 12–29.CrossRefGoogle Scholar
Sun, Q and Zhang, WM (2011) Carbon footprint based multilevel hierarchical production process control. China Mechanical Engineering 22, 1035–1038.Google Scholar
Sungsu, C, Lkhagvadorj, B and Aziz, N (2017) A decision tree approach for identifying defective products in the manufacturing process. International Journal of Contents 13, 57–65.Google Scholar
Vidal, LA, Marle, F and Bocquet, JC (2011) Measuring project complexity using the analytic hierarchy process. International Journal of Project Management 29, 718–727.CrossRefGoogle Scholar
Wagner, R, Schleich, B, Haefner, B, Kuhnle, A and Lanza, G (2019) Challenges and potentials of digital twins and industry 4.0 in product design and production for high performance products. Procedia CIRP 84, 88–93.CrossRefGoogle Scholar
Yan, J, Feng, C and Li, L (2014) Sustainability assessment of machining process based on extension theory and entropy weight approach. International Journal of Advanced Manufacturing Technology 71, 1419–1431.CrossRefGoogle Scholar
Yazdani, MA, Benyoucef, L, Khezri, A and Siadat, A (2020) Multi-objective process and production planning integration in reconfigurable manufacturing environment: augmented ε-constraint based approach. The 13th International Conference on Modeling, Optimization and Simulation-MOSIM 20, 12–14 November.Google Scholar
Yi, Q, Li, C, Zhang, XL, Liu, F and Tang, Y (2015) An optimization model of machining process route for low carbon manufacturing. International Journal of Advanced Manufacturing Technology 80, 1181–1196.CrossRefGoogle Scholar
Yin, R, Cao, H and Li, H (2014) A process planning method for reduced carbon emissions. International Journal of Computer Integrated Manufacturing 27, 1175–1186.CrossRefGoogle Scholar
Zhang, XF, Zhang, SY and Hu, Z (2012) Identification of connection units with high GHG emissions for low-carbon product structure design. Journal of Cleaner Production 27, 118–125.CrossRefGoogle Scholar
Zhang, H, Liu, Q, Chen, X, Zhang, D and Leng, J (2017) A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access 5, 26901–26911.CrossRefGoogle Scholar
Zheng, Y and Wang, Y (2012) Optimization of process selection and sequencing based on genetic algorithm. China Mechanical Engineering 23, 59–65.Google Scholar
Zheng, P, Wang, H, Sang, Z, Zhong R, Y, Liu, Y and Liu, C (2018) Smart manufacturing systems for industry 4.0: conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering 13, 137–150.CrossRefGoogle Scholar
Zheng, H, Yang, S, Lou, S, Gao, Y and Feng, Y (2021) Knowledge-based integrated product design framework towards sustainable low-carbon manufacturing. Advanced Engineering Informatics 48, 101258.CrossRefGoogle Scholar
Zhu, H and Li, J (2018) Research on three-dimensional digital process planning based on MBD. Kybernetes 47, 816–830.CrossRefGoogle Scholar
Zoran, M and Milica, P (2017) Application of modified multi-objective particle swarm optimization algorithm for flexible process planning problem. International Journal of Computer Integrated Manufacturing 30, 271–291.Google Scholar