Additive manufacturing industrial adaptability analysis using fuzzy Bayesian Network. (May 2021) - Jing, Liting; Tan, Bowen; Jiang, Shaofei; Ma, Junfeng
Highlights: The fuzzy BN is firstly proposed to analyze the causal relationship among multiple features in AM. The BN model is constructed for AM adaptiveness and 23 relevant influence factors are involved. The proposed fuzzy BN is used as a decision support tool for assessing the risk and uncertainty of AM technology application. Abstract: Additive manufacturing (AM) technology, also known as 3D printing, has attracted extensive attention in industries due to its significant merits in complex geometry customization and supply chain cost reduction. Although plenty of researches has been conducted on the aspects of process design, control, and prototyping, the study of adapting AM in industry is still not fully investigated. To mitigate the application risk and improve the feasibility of using AM in industry, this study proposes a holistic framework based on the fuzzy Bayesian Network to explore the adaptability of AM. The six categories, total twenty-three potential impact factors summarized from existing literature and industrial experience have been integrated into the analysis; the perception of industries was obtained based on fuzzy linguistic description; then the fuzzy Bayesian Network-based model was developed to quantitatively evaluate the AM adaptability. The jet engine blade case study was used to demonstrate the applicability of the proposed approach. The results showed that the fuzzy Bayesian Network is able to provide the robust and reliable results of adaptability analysis, and provided a valuable recommendation for manufacturers to consider AM technology in the aircraft industry.