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Jin, Y.; Gao, C. 3D Printing Intelligent Factory. Encyclopedia. Available online: https://encyclopedia.pub/entry/52851 (accessed on 24 June 2024).
Jin Y, Gao C. 3D Printing Intelligent Factory. Encyclopedia. Available at: https://encyclopedia.pub/entry/52851. Accessed June 24, 2024.
Jin, Yuran, Cheng Gao. "3D Printing Intelligent Factory" Encyclopedia, https://encyclopedia.pub/entry/52851 (accessed June 24, 2024).
Jin, Y., & Gao, C. (2023, December 18). 3D Printing Intelligent Factory. In Encyclopedia. https://encyclopedia.pub/entry/52851
Jin, Yuran and Cheng Gao. "3D Printing Intelligent Factory." Encyclopedia. Web. 18 December, 2023.
3D Printing Intelligent Factory
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Considering the advantages of 3D printing, intelligent factories and distributed manufacturing, the 3D printing distributed intelligent factory has begun to rise in recent years. However, because the supply chain network of this kind of factory is very complex, coupled with the impact of customized scheduling and environmental constraints on the enterprise, the 3D printing distributed intelligent factory is facing the great challenge of realizing green supply chain networks and optimizing production scheduling at the same time, and thus a theoretical gap appears. 

3D printing intelligent factory

1. Introduction

With the increasing personalized demand of customers and the shortening of the product life cycle, more and more manufacturing enterprises and government departments are beginning to pay attention to personalized production and flexible production [1][2][3][4]. The intelligent factory is an important option to solve these problems. The Industrial 4.0 announced by Germany points out that a new supply chain system can be developed to meet individual needs using the intelligent factory with advanced information and communication technologies, such as the Internet of Things, cloud computing, big data and 3D printing technology [5][6][7][8]. In this context, many manufacturing enterprises around the world have carried out the practice of building intelligent factories, such as the Electronic Works Amberg in Germany, Harley-Davidson in the United States, and the Foshan Haier Drum Washing Machine Factory and the Shangpin Home in China [9][10][11][12]. By connecting the devices in the intelligent factory through the IoT, enterprises can monitor and share information in the cloud system, adjust the process at any time, quickly respond to the individual needs of customers and improve the flexibility of production [13][14][15]. In recent years, 3D printing technology has been further integrated with the intelligent factory [16][17], for it makes product customization more possible and flexible [18][19][20]. In addition, considering that the production of products can be transferred through the distributed 3D printing smart factory, which can avoid the impact of capacity constraints on the production process and achieve high operational efficiency, a distributed 3D printing intelligent factory is beginning to appear on the stage of history.
Although the distributed 3D printing intelligent factory is a new kind of enterprise form, it still depends on the development of the supply chain. Production planning, capacity decision making and outsourcing are all long-term problems, and changes in decision making and demand have a great impact on costs [5][21][22][23][24]. Supply chain operation and control problems still need to be solved after decision making, such as short-term planning and production decisions, so scholars need to design supply chain networks to achieve personalized production in order to reduce costs [25][26][27]. On the other hand, with the increasingly serious environmental problems in the world, the construction of a green supply chain system is an important means to promote national development [28][29][30], and reducing carbon emissions has become a factor that must be considered in supply chain design [31][32][33].

2. 3D Printing Intelligent Factory

Aiming at the research on the hybrid optimization of green supply chain networks and scheduling of the distributed 3D printing intelligent factory, scholars only began to pay attention to the research of intelligent factories in recent years. The issues related to 3D printing technology, distributed production, green supply chain and production scheduling have been more in-depth, but research on the distributed 3D printing intelligent factory is relatively scarce.
As for related research on the 3D printing intelligent factory, most scholars study 3D printing technology; for example, Park et al. [34] proved that 3D printing technology can make it possible to produce products with high degrees of freedom and complex shapes, such as manufacturing ceramic cores with high mechanical properties, etc. Zhu and Zhu [35] analyzed that 3D printing technology brings more possibilities to the development of clinical medicine such as surgical medical models and implantable bionic devices. Ma et al. [36] thought that 3D printing technology would have an impact on the traditional supply chain architecture. Xing et al. [37] proposed a 3D printing cloud manufacturing platform, which showed that 3D printing can be monitored remotely and in real time through the IoT so as to realize intelligent production. Deon et al. [38] reviewed the results of 3D printing drug characterization, which provided a solid foundation for the pharmaceutical industry. There are also many scholars who study intelligent factories; for example, Ivanov et al. [39] proposed a short-term dynamic supply chain model under the environment of the intelligent factory industry for the first time. Afrin et al. [40] designed a multi-objective optimization model to solve the problem of robot work assignment in the intelligent factory. With the proposal of “Made in China 2025”, Chinese scholars Tang et al. [41] and Yu [42] put forward the construction framework and scheme of the intelligent factory according to the construction background and current situation of the intelligent factory so as to improve its intelligence. Gong et al. [43] established a model to enable flexible intelligent factories to plan labor and investment decisions reasonably. There is also a small amount of research on the 3D printing intelligent factory. For example, scholars Chung, Kim and Lee [5] pointed out the importance of 3D printing and IoT in intelligent factories and proposed a dynamic supply chain model and production operation plan.
For research on distributed production, Srai et al. [44] pointed out that distributed manufacturing brings changes to manufacturing enterprises, which promotes the development of digitalization and infrastructure. Ding and Jiang [45] studied a variety of distributed production control mechanisms to realize a personalized production system. Ji and Jin [46] built an optimization model of distributed production networks for 3D printing manufacturing enterprises. Yin [47] designed the optimal production scheduling scheme to solve the tire production scheduling in distributed factories. Gong et al. [48] studied the distributed production scheduling problem between different workshops and factories for the first time. Scholars Zhang [49] and Wang et al. [50] innovated an algorithm for solving the distributed production scheduling problem in order to make the workshop flexible. Xin et al. [51] proposed a GSS method based on distributed production scheduling to schedule jobs to reduce the load on critical equipment. Lu et al. [52] designed an iterative greedy algorithm to achieve the goal of the shortest completion time and minimum energy consumption in a distributed flow shop.
For research on green supply chain networks and scheduling, most scholars study green supply chain networks. For example, scholars Ramudhin et al. [53] introduced the model formula of carbon–market sensitivity–green supply chain network design, which provides decision makers with the ability to understand the tradeoff between total logistics costs and reducing the impact of greenhouse gases. Wang et al. [54] studied a supply chain network design problem considering environmental factors, which can be used as an effective tool for green supply chain strategic planning. Elhedhli and Merrick [55] designed a supply chain network considering carbon emissions, which shows that the cost of carbon emissions will change the optimal configuration of the supply chain. Coskun et al. [56] studied the design of a green supply chain network based on consumers’ green expectations. There are also many scholars who study production scheduling, such as scholars Koç et al. [57], who designed a facility and vehicle scheduling model between production and shipment, which reasonably allocates the utilization of inbound and outbound vehicles. Paithankar and Chatterjee [58] used a maximum flow algorithm and genetic algorithm to solve the problem of effective resource management and maximum cash flow. Xu [59] pointed out that for flexible production, an efficient scheduling scheme is very important in order to improve the production efficiency and customer satisfaction of the manufacturing workshop. Shao et al. [60] showed that more and more enterprises pay attention to the production scheduling problem of distributed factories and verified the effectiveness of NMA algorithm. Zhang et al. [61] designed an advanced planning and scheduling system for mass flexible manufacturing. There is also a small amount of research on green supply chain networks and scheduling. For example, scholars Tanimizu and Amano [62] proposed a new comprehensive scheduling method for production and transportation problems based on the green supply chain network model to reduce carbon dioxide emissions. Scholar Sinaki et al. [63] proposed a multi-objective programming model, which is used to integrate production scheduling and environmentally sustainable supply chain networks to obtain optimal satisfaction.
To sum up, scholars’ research on the 3D printing intelligent factory is on the rise, and the related topics of intelligent factories were deeply studied from the perspective of the application of 3D printing technology, the business model of intelligent factories and the technological application of intelligent factories. In addition, there are a few pieces of research on the hybrid optimization of green supply chain networks and production scheduling, which mainly focus on the design of green supply chain networks and production scheduling optimization.

References

  1. Wang, M.; Pang, S.C.; Yu, S.H.; Qiao, S.B.; Zhai, X.; Yue, H. An Optimal Production Scheme for Reconfigurable Cloud Manufacturing Service System. IEEE Trans. Ind. Inform. 2022, 18, 9037–9046.
  2. Chen, B.T.; Zhang, Y.; Xia, X.H.; Martinez-Garcia, M.; Jombo, G. Knowledge Sharing Enabled Multirobot Collaboration for Preventive Maintenance in Mixed Model Assembly. IEEE Trans. Ind. Inform. 2022, 18, 8098–8107.
  3. He, Y.A.; Xia, M.H. Research on mass personalization production model based on the “Industry 4.0”. Manuf. Autom. 2021, 43, 25–29. (In Chinese)
  4. Ye, X.N. Small orders which can also be produced, more marketable individuation, flexible manufacturing, smart made in China. Policy Res. Explor. 2021, 674, 34–35. (In Chinese)
  5. Chung, B.D.; Kim, S.I.; Lee, J.S. Dynamic Supply Chain Design and Operations Plan for Connected Smart Factories with Additive Manufacturing. Appl. Sci. 2018, 8, 583.
  6. Bisogni, C.; Hao, F.; Loia, V.; Narducci, F. Drowsiness Detection in the Era of Industry 4.0: Are We Ready? IEEE Trans. Ind. Inform. 2022, 18, 9083–9091.
  7. Fang, T. Research on the Application of Internet of things Technology in Industrial Automation. Papermak. Equip. Mater. 2022, 51, 44–46. (In Chinese)
  8. Darwish, L.R.; El-Wakad, M.T.; Farag, M.M. Towards sustainable industry 4.0: A green real-time IIoT multitask scheduling architecture for distributed 3D printing services. J. Manuf. Syst. 2021, 61, 196–209.
  9. Wang, Z. Siemens Digital enabling Sustainable Future. Smart Rail Transit 2022, 59, 48. (In Chinese)
  10. Liu, J. The Bankruptcy Crisis Solution of Harley Davidson. Master’s Thesis, Xinjiang University of Finance & Economics, Urumqi, China, 2018. (In Chinese).
  11. Wang, Y. Haier Foshan Visual Interconnection Factory: The Leader of the Transformation of Home Appliance Manufacturing Industry in the Internet era. Guangdong Sci. Technol. 2015, 24, 32–35. (In Chinese)
  12. Zhu, Y.Y. Research on the Communication Strategy of the Precision Marketing of Shangpin Home under big data. Mod. Bus. 2022, 627, 9–11. (In Chinese)
  13. Hoang, T.M.; Dinh-Van, S.; Barn, B.; Trestian, R.; Nguyen, H.X. RIS-Aided Smart Manufacturing: Information Transmission and Machine Health Monitoring. IEEE Internet Things J. 2022, 9, 22930–22943.
  14. Song, S.; Chang, K.; Yun, K.; Jun, C.D.; Baek, J.G. Defect Synthesis Using Latent Mapping Adversarial Network for Automated Visual Inspection. Electronics 2022, 11, 13.
  15. Tang, T.; Teng, L.; Wu, J.; Chen, M. Realizing Digitalization in an all-round way is the only way to Intelligent Manufacturing-- an interpretation of the Road to Intelligent Manufacturing: Digital Factory. China Mech. Eng. 2018, 29, 366–377. (In Chinese)
  16. Mehrpouya, M.; Dehghanghadikolaei, A.; Fotovvati, B.; Vosooghnia, A.; Emamian, S.S.; Gisario, A. The potential of additive manufacturing in the smart factory industrial 4.0: A review. Appl. Sci. 2019, 9, 3865.
  17. Park, S. Development of innovative strategies for the Korean manufacturing industry by use of the Connected Smart Factory (CSF). Procedia Comput. Sci. 2016, 91, 744–750.
  18. Mai, J.; Zhang, L.; Tao, F.; Ren, L. Customized production based on distributed 3D printing services in cloud manufacturing. Int. J. Adv. Manuf. Technol. 2016, 84, 71–83.
  19. Yang, H.; Leow, W.R.; Chen, X. 3D printing of flexible electronic devices. Small Methods 2018, 2, 1700259.
  20. Wu, Y.; Zeng, Y.; Chen, Y.; Li, C.; Qiu, R.; Liu, W. Photocurable 3D printing of high toughness and self-healing hydrogels for customized wearable flexible sensors. Adv. Funct. Mater. 2021, 31, 2107202.
  21. Xiang, L. A large-scale equilibrium model of energy emergency production: Embedding social choice rules into Nash Q-learning automatically achieving consensus of urgent recovery behaviors. Energy 2022, 259, 14.
  22. Zhu, M.X.; Zhou, X.J. Lifecycle maintenance scheduling of station with multiple categories of informatione. Comput. Ind. Eng. 2022, 172, 11.
  23. Dey, B.K.; Park, J.; Seok, H. Carbon-emission and waste reduction of a manufacturing-remanufacturing system using green technology and autonomated inspection. Rairo-Oper. Res. 2022, 56, 2801–2831.
  24. Florido-Benítez, L.; Aldeanueva Fernández, I. Fusing International Business and Marketing: A Bibliometric Study. Adm. Sci. 2022, 12, 159.
  25. Savoji, H.; Mousavi, S.M.; Antucheviciene, J.; Pavlovskis, M. A Robust Possibilistic Bi-Objective Mixed Integer Model for Green Biofuel Supply Chain Design under Uncertain Conditions. Sustainability 2022, 14, 16.
  26. Liu, Y.; Ren, J.Z.; Dong, L.; Jin, Y.Z.; Man, Y. Urban sludge to value-added products for promoting the development of circular economy: Supply network design and optimization. Resour. Conserv. Recycl. 2022, 182, 14.
  27. Moghaddas, Z.; Tosarkani, B.M.; Yousefi, S. A Developed Data Envelopment Analysis Model for Efficient Sustainable Supply Chain Network Design. Sustainability 2022, 14, 23.
  28. Wu, S.F.; Mao, J. A brief Analysis of the problems and Countermeasures of the current Green supply chain. China Collect. Econ. 2021, 684, 103–104. (In Chinese)
  29. Mao, Y.Q. Research on the construction of green manufacturing system in industrial parks in the new era. China Qual. Certif. 2021, 180, 61–63. (In Chinese)
  30. Cao, J.; Qian, Y.H.; Deng, Q.W.; Hou, R. Establish the green supply chain management and evaluation system and facilitate the development of China’s circular economy. World Environ. 2022, 197, 45–47. (In Chinese)
  31. Kao, H.Y.; Lan, Z.H.; Huang, C.H.; Huang, C.H. A two-stage bi-level decision approach for green supply chain design and cloud virtual machine placement. Int. J. Ind. Eng.-Theory Appl. Pract. 2021, 28, 529–540.
  32. Samuel, C.N.; Venkatadri, U.; Diallo, C.; Khatab, A. Robust closed-loop supply chain design with presorting, return quality and carbon emission considerations. J. Clean. Prod. 2020, 247, 15.
  33. Liu, H.; Yang, C.; Yang, J. Trade-Offs between Cost and Carbon Emissions in Logistics Network Design. Ind. Eng. Manag. 2013, 18, 61–66+73. (In Chinese)
  34. Park, H.Y.; Kim, E.H.; Cho, G.H.; Jung, Y.G.; Zhang, J. Process development of fabricating ceramic core using 3D printing technique. Mater. Chem. Phys. 2019, 231, 382–387.
  35. Zhu, K.F.; Zhu, L. Application Analysis of 3D Printing Technology in the Field of Medical Devices. Wisdom China 2022, 71, 72–73. (In Chinese)
  36. Ma, S.; Cai, X.T.; Songlin, C. Decision Optimization for 3D Printing-Driven Supply Chain. Forum Sci. Technol. China 2022, 311, 148–155. (In Chinese)
  37. Xing, J.J.; Zhang, F.Z.; Zhou, M.L.; Li, M.; Ye, Q. Construction and key Technology Analysis of 3D Printing Cloud platform based on Internet of things. Inf. Technol. Informatiz. 2022, 269, 17–20. (In Chinese)
  38. Deon, M.; Santos, J.; Andrade, D.F.; Beck, R.C.R. A critical review of traditional and advanced characterisation tools to drive formulators towards the rational development of 3D printed oral dosage forms. Int. J. Pharm. 2022, 628, 22.
  39. Ivanov, D.; Dolgui, A.; Sokolov, B.; Werner, F.; Ivanova, M. A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. Int. J. Prod. Res. 2016, 54, 386–402.
  40. Afrin, M.; Jin, J.; Rahman, A.; Tian, Y.-C.; Kulkarni, A. Multi-objective resource allocation for Edge Cloud based robotic workflow in smart factory. Future Gener. Comput. Syst. 2019, 97, 119–130.
  41. Tang, Y.; Chang, J.; Yue, M.M.; Gao, X. Intelligent Factory Technology Framework Based on Big Data. Telecom Power Technol. 2019, 36, 183–185. (In Chinese)
  42. Yu, M.L. The methods and Strategies for the realization of Smart Factory in the New era. Eng. Technol. Res. 2020, 5, 255–256. (In Chinese)
  43. Gong, Q.G.; Chen, G.H.; Zhang, W.; Wang, H. The role of humans in flexible smart factories. Int. J. Prod. Econ. 2022, 254, 12.
  44. Srai, J.S.; Kumar, M.; Graham, G.; Phillips, W.; Tooze, J.; Ford, S.; Beecher, P.; Raj, B.; Gregory, M.; Tiwari, M.K.; et al. Distributed manufacturing: Scope, challenges and opportunities. Int. J. Prod. Res. 2016, 54, 6917–6935.
  45. Ding, K.; Jiang, P. Incorporating social sensors, cyber-physical system nodes, and smart products for personalized production in a social manufacturing environment. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2018, 232, 2323–2338.
  46. Ji, S.F.; Jin, Y.R. Manufacturing Enterprise Distributed Production Network Optimization Based on 3D Printing Technology. Ind. Eng. Manag. 2018, 23, 15–23. (In Chinese)
  47. Yin, J.F. Design and Implementation of Scheduling System for Tire Production on Distributed Factories. Master’s Thesis, Jiangsu University, Zhenjiang, China, 2019.
  48. Gong, G.; Chiong, R.; Deng, Q.; Luo, Q. A memetic algorithm for multi-objective distributed production scheduling: Minimizing the makespan and total energy consumption. J. Intell. Manuf. 2020, 31, 1443–1466.
  49. Zhang, M. Research on Distributed Production Scheduling Algorithm Based on Swarm Intelligence and Machine Learning. Henan Sci. Technol. 2020, 717, 52–53. (In Chinese)
  50. Wang, S.C.; Tang, D.B.; Zhu, H.H.; Nie, Q.W.; Pan, J.F.; Yang, L. Improved Particle Swarm Optimization Algorithm for Distributed Multi-plants Production Scheduling. Mach. Build. Autom. 2021, 50, 9–13. (In Chinese)
  51. Xin, Y.; Wang, L.T.; Dong, Y.H.; Qian, J.B. A Gibbs sampling based coordination scheduling method with fixed I/O constraint. Comput. Ind. Eng. 2020, 142, 9.
  52. Lu, C.; Liu, Q.; Zhang, B.; Yin, L. A Pareto-based hybrid iterated greedy algorithm for energy-efficient scheduling of distributed hybrid flowshop. Expert Syst. Appl. 2022, 204, 17.
  53. Ramudhin, A.; Chaabane, A.; Kharoune, M.; Paquet, M. Carbon Market Sensitive Green Supply Chain Network Design. In Proceedings of the 2008 IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, 8–11 December 2008; pp. 1093–1097.
  54. Wang, F.; Lai, X.; Shi, N. A multi-objective optimization for green supply chain network design. Decis. Support Syst. 2011, 51, 262–269.
  55. Elhedhli, S.; Merrick, R. Green supply chain network design to reduce carbon emissions. Transp. Res. Part D Transp. Environ. 2012, 17, 370–379.
  56. Coskun, S.; Ozgur, L.; Polat, O.; Gungor, A. A model proposal for green supply chain network design based on consumer segmentation. J. Clean. Prod. 2016, 110, 149–157.
  57. Koç, U.; Toptal, A.; Sabuncuoglu, I. Coordination of inbound and outbound transportation schedules with the production schedule. Comput. Ind. Eng. 2017, 103, 178–192.
  58. Paithankar, A.; Chatterjee, S. Open pit mine production schedule optimization using a hybrid of maximum-flow and genetic algorithms. Appl. Soft Comput. J. 2019, 81, 105507.
  59. Xu, J.H. Research and Application of Flexible Manufacturing Cell Production Scheduling Optimization. Master’s Thesis, Nanchang University, Nanchang, China, 2020.
  60. Shao, W.S.; Shao, Z.S.; Pi, D.C. A network memetic algorithm for energy and labor-aware distributed heterogeneous hybrid flow shop scheduling problem. Swarm Evol. Comput. 2022, 75, 20.
  61. Zhang, Z.Y.; He, S.; Zhao, C.C.; Li, F.; Che, J.W. Research on advanced planning and scheduling strategy of photovoltaic industry under mass flexible manufacturing. Manuf. Autom. 2022, 44, 19–21. (In Chinese)
  62. Tanimizu, Y.; Amano, K. Integrated Production and Transportation Scheduling for Multi-objective Green Supply Chain Network Design. Procedia CIRP 2016, 57, 152–157.
  63. Sinaki, R.Y.; Sadeghi, A.; Suer, G.; Çelikbilek, C. A Weighted Multi-Objective Mathematical Model for Cell Scheduling and Environmentally Sustainable Supply Chain Network. Procedia Manuf. 2019, 39, 1559–1566.
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