Investigation of production planning for environmental sustainability improvement in polymer LPBF

Wang, Han (2024) Investigation of production planning for environmental sustainability improvement in polymer LPBF. PhD thesis, University of Nottingham.

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Additive Manufacturing (AM), also known as 3D printing, refers to a family of manufacturing technologies that use a layer-by-layer approach to converting digital models into physical components. The adoption of AM has offered significant sustainability benefits such as improved resource efficiency, extended product life, and reconfigured value chains. However, despite these prospective benefits, the full potential of the sustainable aspects of AM has not been explored, due to a lack of knowledge regarding environmental sustainability improvement in AM.

This thesis documents work on investigating the environmental sustainability improvement in polymer Laser Powder Bed Fusion (LPBF) from a production planning perspective. Three studies were performed to understand how to improve the environmental sustainability of AM: modelling, optimisation, and network effects investigation.

The modelling study revealed environmental sustainability elements in polymer LPBF and their share in the environmental impacts of polymer LPBF. To do this, a layer-based environmental sustainability model was established. In this model, the build time, energy consumption, embedded energy, material consumption, and risk of build failure were considered. It was shown that embedded energy dominated the total energy consumption (approximately 40 to 60%). Meanwhile, the energy relevant to risk of build failure contributed to approximately one third of expected total energy consumption at full capacity utilization.

The study of optimisation demonstrated that integrated optimisation plays a significant role in improving energy efficiency during the additive process. In this study, an exploratory simulation was used to investigate integrated optimisation through the system (or computational tool) development. Building on this, a new framework of integrated optimisation was established. Build volume packing and scheduling were jointly optimized. Specifically, a bottom-left heuristic, capacity aggregation algorithm and exhaustive search were used to support integrated optimisation. Specific energy consumption was regarded as the optimisation objective. It was found that integrated optimisation approach had a significant effect on improving energy efficiency of polymer LPBF at higher demand profiles. The developed system allowed a lower specific energy consumption during the additive process than the results in extant literature.

The study of network effects revealed the extraordinary potential for environmental sustainability improvement in polymer LPBF by investigating the environmental network effects in the AM platform. Environmental network effects reflect the mutual impact regarding quantity and benefits (i.e., energy efficiency and lead time) between customers and machine operators (or manufacturers) in AM platform. Specifically, machine operators are assumed to care about energy efficiency (i.e., specific energy consumption) and customers are assumed to concern lead time (i.e., schedule attainment). Another computational tool was developed to support this investigation. A build volume-based capacity aggregation algorithm was developed in this system. Specific energy consumption and schedule attainment were considered as the metrics to uncover environmental network effects in the AM platform. It was shown that there were indirect network effects embedded in the AM platform. These powerful effects are likely to help manufacturers improve energy efficiency and help customers reduce waiting time. Based on integrated optimisation, using network effects in the AM platform shows greater performance in improving the environmental sustainability of AM.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Baumers, Martin
Ashcroft, Ian
Keywords: Additive manufacturing, 3D printing, Environmental sustainability, Sustainable manufacturing, Production planning, Optimisation
Subjects: T Technology > TS Manufactures
Faculties/Schools: UK Campuses > Faculty of Engineering
Item ID: 77107
Depositing User: Wang, Han
Date Deposited: 12 Apr 2024 09:40
Last Modified: 12 Apr 2024 09:40

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