期刊
RAPID PROTOTYPING JOURNAL
卷 23, 期 6, 页码 983-997出版社
EMERALD GROUP PUBLISHING LTD
DOI: 10.1108/RPJ-03-2016-0041
关键词
Additive manufacturing; Selective laser melting; Design feature recommendation; Hybrid machine learning
资金
- SERC
- A*STAR Industrial Additive Manufacturing Programme
- SIMTech-NTU Joint Lab
- National Research Foundation, Singapore
- Singapore Centre for 3D Printing
Purpose - This paper aims to present a hybrid machine learning algorithm for additive manufacturing (AM) design feature recommendation during the conceptual design phase. Design/methodology/approach - In the proposed hybrid machine learning algorithm, hierarchical clustering is performed on coded AM design features and target components, resulting in a dendrogram. Existing industrial application examples are used to train a supervised classifier that determines the final sub-cluster within the dendrogram containing the recommended AM design features. Findings - Through a case study of designing additive manufactured R/C car components, the proposed hybrid machine learning method was proven useful in providing feasible conceptual design solutions for inexperienced designers by recommending appropriate AM design features. Originality/value - The proposed method helps inexperienced designers who are newly exposed to AM capabilities explore and utilize AM design knowledge computationally.
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