Digital twin-driven green material optimal selection and evolution in product iterative design

Abstract

In recent years, green concepts have been integrated into the product iterative design in the manufacturing field to address global competition and sustainability issues. However, previous efforts for green material optimal selection disregarded the interaction and fusion among physical entities, virtual models, and users, resulting in distortions and inaccuracies among user, physical entity, and virtual model such as inconsistency among the expected value, predicted simulation value, and actual performance value of evaluation indices. Therefore, this study proposes a digital twin-driven green material optimal selection and evolution method for product iterative design. Firstly, a novel framework is proposed. Subsequently, an analysis is carried out from six perspectives: the digital twin model construction for green material optimal selection, evolution mechanism of the digital twin model, multi-objective prediction and optimization, algorithm design, decision-making, and product function verification. Finally, taking the material selection of a shared bicycle frame as an example, the proposed method was verified by the prediction and iterative optimization of the carbon emission index.

Keywords

Product iterative design / Digital twin (DT) / Green material optimal selection / Evolution mechanism / Iterative optimization

References

[1.] Govindan K, Shankar KM, Kannan D. Sustainable material selection for construction industry—a hybrid multi criteria decision making approach. Renew Sustain Energy Rev, 2016, 55: 1274-1288.

[2.] Seyed AH, Saeed M, Mohsen AS. Social life cycle assessment for material selection: a case study of building materials. Int J Life Cycle Assess, 2014, 19: 620-645.

[3.] Fehim F, Kemal T. Materials selection for lighter wagon design with a weighted property index method. Mater Des, 2012, 37: 470-477.

[4.] Apple_Environmental_Progress_Report_2020. https://www.apple.com/euro/environment/pdf/a/generic/Apple_Environmental_Progress_Report_2020.pdf. Accessed 10 Feb 2022

[5.] Xiang F, Huang YY, Zhang Z, et al. A new mode of green manufacturing for product life cycle based on digital twin. Comput Integr Manuf Syst, 2019, 25(6): 1505-1514.

[6.] Peças P, Ribeiro I, Silva A, et al. Comprehensive approach for informed life cycle-based materials selection. Mater Des, 2013, 43: 220-232.

[7.] Venkata RR. A decision making methodology for material selection using an improved compromise ranking method. Mater Des, 2008, 29(10): 1949-1954.

[8.] Gong QS, Zhang H, Jiang ZG, et al. Nonempirical hybrid multi-attribute decision-making method for design for remanufacturing. Adv Manuf, 2019, 7: 423-437.

[9.] Tao F, Zhang H, Qi QL, et al. Theory and application of digital twin model construction. Comput Integr Manuf Syst, 2021, 27(1): 1-15.

[10.] Kosacka M, Kostrzewski M, Marczewska M, et al. How digital twin concept supports internal transport systems?—Literature review. Energies, 2021, 14: 4919.

[11.] van der Valk H, Haße H, Möller F et al (2020) A taxonomy of digital twins. In: The 26th Americas conference on information systems, 15–17 August, Salt Lake City

[12.] Tao F, Zhang CY, Zhang H, et al. Future gear exploration: digital twin gear. Comput Integr Manuf Syst, 2022, 28(1): 1-16.

[13.] Liu SM, Lu YQ, Li J, et al. Multi-scale evolution mechanism and knowledge construction of a digital twin mimic model. Robot Comput Integr Manuf, 2021, 71: 102123.

[14.] Li H, Tao F, Wang HQ, et al. Integrated development framework and key technology of complex product design and manufacturing based on digital twin. Comput Integr Manuf Syst, 2019, 25(6): 1320-1336.

[15.] Zhuang CB, Liu JH, Xiong H, et al. The connotation, system structure and development trend of product digital twin. Comput Integr Manuf Syst, 2017, 23(4): 753-768.

[16.] Yi Y, Yan YH, Liu XJ, et al. Digital twin-based smart assembly process design and application framework for complex products and its case study. J Manuf Syst, 2021, 58: 94-107.

[17.] van Raymon D, Bedir T, Cagatay C. Predictive maintenance using digital twins: a systematic literature review. Inf Softw Technol, 2022, 151: 107008.

[18.] Peter O, Jing ZT, Thomas W. Computational optimization for circular economy product design. J Clean Prod, 2022, 362: 132340.

[19.] Ji X, Gao Q, Wang H. A bilevel-optimization approach to determine product specifications during the early phases of product development: increase customer value and reduce design risks. Expert Syst Appl, 2022, 188: 116012.

[20.] Sandanayake M, Gunasekara C, Law D, et al. Sustainable criterion selection framework for green building materials—an optimization based study of fly-ash geopolymer concrete. Sustain Mater Technol, 2020, 25: e00178.

[21.] Tian GD, Zhang HH, Feng YX, et al. Green decoration materials selection under interior environment characteristics: a grey-correlation based hybrid MCDM method. Renew Sustain Energy Rev, 2018, 81(1): 682-692.

[22.] Zhang HH, Wu YY, Wang K, et al. Materials selection of 3D-printed continuous carbon fiber reinforced composites considering multiple criteria. Mater Des, 2020, 196: 109140.

[23.] Zhou CC, Yin GF, Hu XB. Multi-objective optimization of material selection for sustainable products: artificial neural networks and genetic algorithm approach. Mater Des, 2009, 30(4): 1209-1215.

[24.] Tao F, Bi LN, Zuo Y, et al. A hybrid group leader algorithm for green material selection with energy consideration in product design. CIRP Ann, 2016, 65(1): 9-12.

[25.] Zhang XJ, Chen KZ, Feng XA. Material selection using an improved genetic algorithm for material design of components made of a multiphase material. Mater Des, 2008, 29(5): 972-981.

[26.] Xiang F, Zhang Z, Zuo Y, et al. Digital twin driven green material optimal-selection towards sustainable manufacturing. Proc CIRP, 2019, 81: 1290-1294.

[27.] Zhang H, Liu Q, Chen X, et al. A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access, 2017, 5: 26901-26911.

[28.] Tao F, Zhang M. Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access, 2017, 5: 20418-20427.

[29.] Tao F, Qi QL, Liu A, et al. Data-driven smart manufacturing. J Manuf Syst, 2018, 48: 157-169.

[30.] Tao F, Cheng Y, Chen JF, et al. Theories and technologies for cyber- physical fusion in digital twin shop-floor. Comput Integr Manuf Syst, 2017, 23(8): 1603-1611.

[31.] Tao F, Zhang M, Liu YS, et al. Digital twin driven prognostics and health management for complex equipment. CIRP Ann, 2018, 67(1): 169-172.

[32.] Qi QL, Tao F. Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison. IEEE Access, 2018, 6: 3585-3593.

[33.] Guo JP, Zhao N, Sun L, et al. Modular based flexible digital twin for factory design. J Ambient Intell Humaniz Comput, 2018, 10: 1189-1200.

[34.] He B, Bai KJ. Digital twin-based sustainable intelligent manufacturing: a review. Adv Manuf, 2020, 9: 1-21.

[35.] Lennart YL. Materials selection and design for development of sustainable products. Mater Des, 2007, 28(2): 466-479.

[36.] Lu JF, Xia LY, Bai O, et al. Study on the whole life cycle of product digital twin under intelligent manufacturing. Process Autom Instrum, 2021, 42(3): 1-7.

[37.] Li Y, Liu Q. Service-oriented research on multi-pass milling parameters optimization for green and high efficiency. J Mech Eng, 2015, 51(11): 89-98.

[38.] Deng ZH, Fu YH, Wan LL, et al. Multi objective optimization of milling process parameters for green high-performance manufacturing. China Mech Eng, 2017, 28(19): 2365-2372.

[39.] Wang RH, Jie PF, Wang DL, et al. Optimization of insulation thickness of double buried district heating pipes using the Eco-indicator method. J Build Eng, 2021, 41: 102393.

[40.] Wang KS. Towards zero-defect manufacturing (ZDM)—a data mining approach. Adv Manuf, 2013, 1: 62-74.

[41.] Tao F, Zhang CY, Qi QL, et al. The maturity model of digital twin. Comput Integr Manuf Syst, 2022, 28(5): 1267-1281.

[42.] Chen DD, Lin YC, Chen XM. A strategy to control microstructures of a Ni-based superalloy during hot forging based on particle swarm optimization algorithm. Adv Manuf, 2019, 7: 238-247.

[43.] Agathocle AK, Nikolaos AF. Balancing multiple criteria in formulation of weighted, single-objective genetic algorithm optimization for CNC machining problems. Adv Manuf, 2016, 4: 178-188.

[44.] Rityuj SP, Raj KS, Srinivasu GS. Novel design and composition optimization of self-lubricating functionally graded cemented tungsten carbide cutting tool material for dry machining. Adv Manuf, 2020, 9: 34-46.

[45.] MatWeb. https://matweb.com/. Accessed 7 Apr 2022

[46.] MetalChina. https://www.metalchina.com/. Accessed 7 Apr 2022

[47.] Liu JF, Wen XJ, Zhou HG et al (2022) Digital twin-enabled machining process modeling. Adv Eng Inform 54:1–13

[48.] Wang L, Luo ZD, Tang HT (2023) A novel model for dynamic manufacturing service collaboration on industrial internet. IEEE Trans Ind Inform. https://doi.org/10.1109/TII.2023.3252408

Funding

National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809(52005025); Fundamental Research Funds for the Central Universities http://dx.doi.org/10.13039/501100012226(51705379)