Multicriteria decision analysis framework for part orientation analysis in additive manufacturing. Issue 4 (8th July 2021)

• Record Type: Journal Article
• Authors: Ransikarbum, Kasin; Pitakaso, Rapeepan; Kim, Namhun; Ma, Jungmok
• Is Part Of: Journal of computational design and engineering. Volume 8:Issue 4(2021)
• Page Start: 1141
• Page End: 1157
• Publication Date: 2021-07-08
• Subjects: multicriteria decision making -- data envelopment analysis (DEA) -- analytic hierarchy process -- linear normalization -- orientation selection -- additive manufacturing; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1093/jcde/qwab037
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: Additive manufacturing (AM) or three-dimensional printing (3DP) refers to producing objects from digital information layer by layer. Despite recent advancements in AM, process planning in AM has not received much attention compared to subtractive manufacturing. One of the critical process planning issues in AM is deciding part orientation. In this research, the integrative framework of multicriteria decision making for part orientation analysis in AM is investigated. Initially, quantitative data are assessed using the data envelopment analysis (DEA) technique without preferences from a decision maker. In contrast, a decision maker's preferences are qualitatively analysed using the analytic hierarchy process (AHP) technique. Then, the proposed framework combining explicit data as in DEA, implicit preference as in AHP, and linear normalization (LN) technique is used, which reflects both preference and objective data in supporting decision making for 3DP part orientation. Two particular AM technologies, namely Fused Deposition Modelling and Selective Laser Sintering, are used as a case study to illustrate the proposed algorithm, which is further verified with experts to improve process planning for AM. Graphical Abstract: