With the development of manufacturing industry, considerable attention has been paid to the issue of environmental problems caused by the manufacturing process. One of the most important methods to solve this kind of problem is the low-carbon design of the products before manufacturing process. Therefore, a new part structure low-carbon design method is proposed in this paper. At first, the layered structure model based on part features is constructed in order to divide the product into part features, and as a result, the carbon footprint is calculated for each part feature. Then, the part features are clustered into several classes looking forward to find the higher carbon emissions part features as initial features for low-carbon design. After that, the multi-agent interactive reinforcement learning method is performed on the topological path of multiple initial parts using the Q-learning algorithm in Markov environment, so as to meet the design requirements of low-carbon design. Finally, a 6400 kN injection molding machine moving template design was taken as an example to verify the effectiveness of the proposed low-carbon design method.
HerrmannITHauschildMZ. Effects of globalisation on carbon footprints of products. CIRP Ann Manuf Technol2009; 58: 13–16.
2.
WiedmannTMinxJ. A definition of carbon footprint. J Roy Soc Med2009; 92: 193–195.
3.
ChangDSYehLTLiuW. Incorporating the carbon footprint to measure industry context and energy consumption effect on environmental performance of business operations. Clean Technol Environ Policy2015; 17: 359–371.
4.
JoyceTOkrasinskiTASchaefferW. Estimating the carbon footprint of telecommunications products: A heuristic approach. J Mech Des2010; 132: 094502–094502.
5.
Yang QZ and Song B. Improving the carbon footprint of secondary nickel recovered from industrial wastes. In: IEEE conference on industrial electronics and applications, 2010, pp.83–88. New York: IEEE.
6.
AccorsiRBortoliniMGamberiMet al.Multi-objective warehouse building design to optimize the cycle time, total cost, and carbon footprint. Int J Adv Manuf Technol2017; 92: 839–854.
7.
ZhangCLiWJiangPet al.Experimental investigation and multi-objective optimization approach for low-carbon milling operation of aluminum. Arch Proc IMechE, Part C: J Mechanical Engineering Science1989–1996, pp. 203–210.
8.
XiaYHLiuJYHuangZWet al.Carbon emission impact on the operation of virtual power plant with combined heat and power system. Front Inform Technol Electron Eng2016; 17: 479–488.
9.
MayyasAQattawiAOmarMet al.Design for sustainability in automotive industry: A comprehensive review. Renew Sustain Energy Rev2012; 16: 1845–1862.
10.
Dattilo CA, Zanchi L, Pero FD, et al. Sustainable design: An integrated approach for lightweighting components in the automotive sector. In: International conference on sustainable design and manufacturing. Cham: Springer, 2017, pp.291–302.
11.
Alkadi NM. Product design for energy reduction in concurrent engineering: An inverted pyramid approach. PhD Thesis, West Virginia University, USA, 2007.
12.
Zanchi L, Delogu M, Ierides M, et al. Life cycle assessment and life cycle costing as supporting tools for EVs lightweight design. In: Sustainable design and manufacturing 2016. New York: Springer International Publishing, 2016.
13.
GuinéeJBHuppesGHeijungsR. Developing an LCA guide for decision support. Environ Manage Health2001; 12: 301–311.
14.
MoriYHuppesGHaesHAUDet al.Component manufacturing analysis. Int J Life Cycle Assess2000; 5: 327–334.
15.
HeBTangWWangJet al.Low-carbon conceptual design based on product life cycle assessment. Int J Adv Manuf Technol2015; 81: 863–874.
16.
BallPDEvansSLeversAet al.Zero carbon manufacturing facility - Towards integrating material, energy, and waste process flows. Proc IMechE, Part B: J Engineering Manufacture2009; 223: 1085–1096.
17.
ZhangYF. Research on low-carbon architectural development based on green life cycle. Appl Mech Mater2014; 443: 263–267.
18.
HertwichEGGibonTBoumanEAet al.Integrated life-cycle assessment of electricity-supply scenarios confirms global environmental benefit of low-carbon technologies. Proc Natl Acad Sci USA2015; 112: 6277–6277.
19.
MaYCaiLWangJ. Life cycle carbon emissions research of hybrid-power gas engine heat pump. J Refrig2013; 34: 52–58.
20.
BoPWet al.Carbon footprint - a catalyst for LCA?J Ind Ecol2011; 12: 3–6.
21.
AlkadiNEGopalakrishnanBChaudhariSA. Product design for energy reduction in concurrent engineering: An inverted pyramid approach. Int J Ind Syst Eng2013; 15: 90–117.
22.
SongJSLeeKM. Development of a low-carbon product design system based on embedded GHG emissions. Resour Conserv Recycl2010; 54: 547–556.
23.
SunLFet al.Carbon emission hierarchical model of complex equipment for low-carbon design. Comput Integr Manuf Syst2012; 18: 2381–2390.
24.
ZhaoSSLIZB. Formalization reasoning method for assembly sequences based on polychromatic sets. Comput Integr Manuf Syst2008; 14: 1579–1578.
Zhang X, Lesser V and Wagner T. Integrative negotiation in complex organizational agent systems. In: IEEE/WIC international conference on intelligent agent technology. Washington DC: IEEE Computer Society, 2003, pp. 140–146.
27.
Uhrmacher A and Gugler K. Distributed, parallel simulation of multiple, deliberative agents. In: Proceedings of 14th workshop on parallel and distributed simulation, 2000, pp.101–108. IEEE.
28.
ChenXFuBHeYet al.Timesharing-tracking framework for decentralized reinforcement learning in fully cooperative multi-agent system. J Autom Sin2015; 1: 127–133.
29.
Nabuurs GJ, Masera O, Andrasko K, et al. Climate change 2007: Mitigation. contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Climate Change 2007: The Physical Science Basis, 2007, pp.159–254.
