Parts design and process optimization

Book chapter

Hassanin, Hany, Bidare, Prveen, Zweiri, Yahya and Essa, Khamis 2021. Parts design and process optimization. in: Salunkhe, S., Hussein, H. and Davim, J. (ed.) Applications of Artificial Intelligence in Additive Manufacturing USA IGI Global. pp. 25-49
AuthorsHassanin, Hany, Bidare, Prveen, Zweiri, Yahya and Essa, Khamis
EditorsSalunkhe, S., Hussein, H. and Davim, J.
AbstractArtificial intelligence and additive manufacturing are primary drivers of Industry 4.0, which is reshaping the manufacturing industry. Based on the progressive layer-by-layer principle, additive manufacturing allows for the manufacturing of mechanical parts with a high degree of complexity. In this chapter, a deep learning neural network (DLNN) is introduced to rationalize the effect of cellular structure design factors as well as process variables on physical and mechanical properties utilizing laser powder bed fusion. The models developed were validated and utilized to create process maps. For both design and process optimization, the trained deep learning neural network model showed the highest accuracy. Deep learning neural networks were found to be an effective technique for predicting material properties from limited data sets, as per the findings.
KeywordsDeep learning; Additive manufacturing; Porosity; Powder bed fusion
Page range25-49
Year2021
Book titleApplications of Artificial Intelligence in Additive Manufacturing
PublisherIGI Global
Output statusPublished
Place of publicationUSA
ISBN9781799885160
ISSN2327-0411
2327-042X
Publication dates
PrintDec 2021
Publication process dates
AcceptedNov 2021
Deposited13 Dec 2021
Digital Object Identifier (DOI)https://doi.org/10.4018/978-1-7998-8516-0.ch002
Official URLhttps://www.igi-global.com/chapter/parts-design-and-process-optimization/294047
Related URLhttps://www.igi-global.com/book/applications-artificial-intelligence-additive-manufacturing/271276
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