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.

Citation

Yao, X., Moon, S.K. and Bi, G. (2017), "A hybrid machine learning approach for additive manufacturing design feature recommendation", Rapid Prototyping Journal, Vol. 23 No. 6, pp. 983-997. https://doi.org/10.1108/RPJ-03-2016-0041