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|Title:||Optimisation of additive manufacturing solutions for customised car parts||Authors:||Han, Eunseo||Keywords:||Engineering::Manufacturing::Product design
|Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||Han, E. (2022). Optimisation of additive manufacturing solutions for customised car parts. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157418||Project:||B139||Abstract:||With the rising trend of customization in the automobile industry, manufacturers are seeking to increase customers’ involvement in product design and manufacturing. Additive Manufacturing (AM) technology is a cost-effective method to produce highly personalized unique designs and is being adopted by many manufacturers for its high design flexibility. In this report, the challenge of identifying the optimal process, material, and print parameters to print a specific customized part using AM is addressed. Genetic Algorithm (GA) is adapted to search for optimal solutions with the objectives to minimize build time and cost and, to maximize structural strength. The following AM processes are considered: Fused Deposition Modelling (FDM), Material Jetting (MJ), Directed Energy Deposition (DED), Electron Beam Melting (EBM), Selective Laser Sintering (SLS), and Stereolithography (SLA). Fused Deposition Modelling (FDM) printing parameters’ effect on the mechanical properties of Polylactic acid (PLA) specimens was studied through ASTM D638 tensile test procedure and integrated into the GA. A case study of customized steering wheel is used to demonstrate the GA optimization process. The possibility of incorporating finite element method simulations in the optimization problem is also explored. This report gives an insight into how optimization algorithms can allow manufacturers to shorten lead time and unit cost by making more informed decisions for AM.||URI:||https://hdl.handle.net/10356/157418||Schools:||School of Mechanical and Aerospace Engineering||Organisations:||Hyundai Motors Group Innovation Center Singapore||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Student Reports (FYP/IA/PA/PI)|
Updated on Dec 10, 2023
Updated on Dec 10, 2023
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