Feature based building orientation optimization for additive manufacturing. Issue 2 (21st March 2016)

• Record Type: Journal Article
• Authors: Zhang, Yicha; Bernard, Alain; Gupta, Ravi Kumar; Harik, Ramy
• Is Part Of: Rapid prototyping journal. Volume 22:Issue 2(2016)
• Page Start: 358
• Page End: 376
• Publication Date: 2016-03-21
• Subjects: Multi-Attribute decision making -- Additive manufacturing -- AM feature -- AM production knowledge -- Building orientation; Engineering design -- Periodicals; 620.004205
• DOI: 10.1108/RPJ-03-2014-0037
• Languages: English
• ISSNs: 1355-2546

Abstract

Abstract : Purpose: The purpose of this paper is to present research work based on the authors' conceptual framework reported in the VRAP Conference 2013. It is related with an efficient method to obtain an optimal part build orientation for additive manufacturing (AM) by using AM features with associated AM production knowledge and multi-attribute decision-making (MADM). The paper also emphasizes the importance of AM feature and the implied AM knowledge in AM process planning. Design/methodology/approach: To solve the orientation problem in AM, two sub-tasks, the generation of a set of alternative orientations and the identification of an optimal one within the generated list, should be accomplished. In this paper, AM feature is defined and associated with AM production knowledge to be used for generating a set of alternative orientations. Key attributes for the decision-making of the orientation problem are then identified and used to represent those generated orientations. Finally, an integrated MADM model is adopted to find out the optimal orientation among the generated alternative orientations. Findings: The proposed method to find out an optimal part build orientation for those parts with simple or medium complex geometric shapes is reasonable and efficient. It also has the potential to deal with more complex parts with cellular or porous structures in a short time by using high-performance computers. Research limitations/implications: The proposed method is a proof-of-concept. There is a need to investigate AM feature types and the association with related AM production knowledge further so as to suite the context of orientating parts with more complex geometric features. There are also research opportunities for developing more advanced algorithms to recognize AM features and generate alternative orientations and refine alternative orientations. Originality/value: AM feature is defined and introduced to the orientation problem in AM for generating the alternative orientations. It is also used as one of the key attributes for decision-making so as to help express production requirements on specific geometric features of a desired part.