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Multicriteria decision analysis framework for part orientation analysis in additive manufacturing
Ransikarbum Kasin,Pitakaso Rapeepan,Kim Namhun,Ma Jungmok 한국CDE학회 2021 Journal of computational design and engineering Vol.8 No.4
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.
오영광,Moise Busogi,Kasin Ransikarbum,신동민,권대일,김남훈 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.12
The quality monitoring and control (QMC) has been an essential process in the manufacturing industries. With the advancements in data analytics, machine-learning based QMC has become popular in various manufacturing industries. At the same time, the cost effectiveness (CE) of the QMC is perceived as a main decision criterion that explicitly accounts for inspection efforts and has a direct relationship with the QMC capability. In this paper, the cost-effective support vector machine (CESVM)-based automated QMC system (QMCS) is proposed. Unlike existing models, the proposed CESVM explicitly incorporates inspection-related expenses and error types in the SVM algorithm. The proposed automated QMCS is verified and validated using an automotive door-trim manufacturing process. Next, we perform a design of experiment to assess the sensitivity analysis of the proposed framework. The proposed model is found to be effective and could be viewed as an alternative or complementary tool for the traditional quality inspection system.