A decision support system for additive manufacturing process selection using a hybrid multiple criteria decision-making method. Issue 9 (30th October 2018)

• Record Type: Journal Article
• Authors: Wang, Yuanbin; Zhong, Ray Y.; Xu, Xun
• Is Part Of: Rapid prototyping journal. Volume 24:Issue 9(2018)
• Page Start: 1544
• Page End: 1553
• Publication Date: 2018-10-30
• Subjects: Additive manufacturing -- Multi-criteria decision making -- Decision-support systems -- Process selection; Engineering design -- Periodicals; 620.004205
• DOI: 10.1108/RPJ-01-2018-0002
• Languages: English
• ISSNs: 1355-2546

Abstract

Abstract : Purpose: Additive manufacturing (AM) has been increasingly used in various applications in recent years. However, it is still challenge when it comes to selecting a suitable AM process. This is because the outcome may vary due to not only different materials and printers but also different parameters and post-processes. This paper aims to develop an efficient method to help users understand trade-offs and make right decisions. Design/methodology/approach: A hybrid method is proposed to help users select appropriate options from a large-scale and discrete option space in an interactive way. First, the design-by-shopping approach is applied to allow users exploring and refining the option space. The analytical hierarchical process method is then used to capture customers' preferences. After analyzing the results of different normalization methods, a modified Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach is proposed to rank solutions and provide suggestions. Findings: The usefulness of proposed method is illustrated in a case study. The results show that it can help customers understand performance distributions and find most suitable options accurately. The ranking of the modified TOPSIS method is more reasonable. Originality/value: Due to the complexity of AM technologies, the process selection is considered at the parameter level. A new system framework is proposed for decision support. The TOPSIS method is modified to achieve a stable performance.