A hybrid machine learning approach for additive manufacturing design feature recommendation

Abstract

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.