A novel failure mode and effect analysis model for machine tool risk analysis. (March 2019)

• Record Type: Journal Article
• Authors: Lo, Huai-Wei; Liou, James J.H.; Huang, Chun-Nen; Chuang, Yen-Ching
• Is Part Of: Reliability engineering & system safety. Volume 183(2019)
• Page Start: 173
• Page End: 183
• Publication Date: 2019-03
• Subjects: FMEA -- MCGDM -- R-BWM -- R-TOPSIS -- Machine tool -- Risk management; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.11.018
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: l We integrate several state-of-the-art methods to analyze system/product failures. l The proposed model combines the rough number, BWM, and modified TOPSIS techniques. l The variety of expert opinions can be effectively integrated with this method. l The proposed model is demonstrated using data provided by a machine tool company. l Comparisons are performed between results from the proposed model and past methods. Abstract: Increasing the reliability of machine tools and reducing possible risks during the manufacturing process is crucial for the future of industry. The failure mode and effects analysis (FMEA) method is reliant upon the experience of experts to determine the primary failure modes and detect the most critical factors for preventing risk. Clearly, an effective method capable of integrating the various different expert opinions is required. This study proposes a novel FMEA model based on multi-criteria group decision-making, which is developed by integrating a rough best–worst method, and modified rough technique for order preference by similarity to an ideal solution for ranking failure modes. The model can overcome some of the limitations of the conventional FMEA. It also includes the expected cost as a risk element to provide a more practical result. The effectiveness of the proposed model is demonstrated by conducting a case study involving a machine tool company. The results indicate that the proposed model can effectively assist managers in evaluating risk factors and identifying critical failure modes.