Part segregation based on particle swarm optimisation for assembly design in additive manufacturing
Issue Date
2019-05-05Subjects
assembly-based designadditive manufacturing
layered manufacturing
mixed-integer non-linear optimisation
particle swarm optimisation
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Show full item recordAbstract
Minimising total production time in the additive or layered manufacturing is a critical concern, and in this respect, the idea of balancing assembly time and build time is rapidly gaining research attention. The proposed work intends to provide benefit in terms of reduced lead time to customers in a collaborative environment with simultaneous part printing. This paper formulates a mixed-integer non-linear programming (MINLP) model to evaluate the near optimal threshold area and support material allocation while segregating parts for a single material additive manufacturing set-up. The resulting time minimisation model is finitely bounded with respect to support material volume, total production time and total assembly cost constraints. A novel swarm intelligence-based part segregation procedure is proposed to determine the number of part assemblies and part division scheme that adheres to cross-sectional shape, cross-sectional area, and height restrictions. The proposed approach is illustrated and evaluated for objects with regular as well as free-form surfaces using two different hypothetically simulated real size 3D models. Results indicate that the proposed approach is able to reduce the total amount of manufacturing time in comparison with single part build time for all the tested cases.Citation
Maiyar L M , Singh S, Prabhu V, Tiwari M K (2019) 'Part segregation based on particle swarm optimisation for assembly design in additive manufacturing', International Journal of Computer Integrated Manufacturing, 32 (7), pp.705-722.Publisher
Taylor and Francis Ltd.Additional Links
https://www.tandfonline.com/doi/abs/10.1080/0951192X.2019.1610577Type
ArticleLanguage
enISSN
0951-192XEISSN
1362-3052ae974a485f413a2113503eed53cd6c53
10.1080/0951192X.2019.1610577