Fuzzy inference system application for failure analyzing in automobile industry

Abstract

Purpose Nowadays, quality is one of the most important key success factors in the automobile industry. Improving the quality is based on optimizing the most important quality characteristics and usually launched by highly applied techniques such as failure mode and effect analysis (FMEA). According to the literature, however, traditional FMEA suffers from some limitations. Reviewing the literature, on one hand, shows that the fuzzy rule-base system, under the artificial intelligence category, is the most frequently applied method for solving the FMEA problems. On the other hand, the automobile industry, which highly takes advantages of traditional FMEA, has been deprived of benefits of fuzzy rule-based FMEA (fuzzy FMEA). Thus, the purpose of this paper is to apply fuzzy FMEA for quality improvement in the automobile industry. Design/methodology/approach Firstly, traditional FMEA has been implemented. Then by consulting with a six-member quality assurance team, fuzzy membership functions have been obtained for risk factors, i.e., occurrence (O), severity (S), and detection (D). The experts have also been consulted about constructing the fuzzy rule base. These evaluations have been performed to prioritize the most critical failure modes occurring during production of doors of a compact car, manufactured by a part-producing company in Iran. Findings Findings indicate that fuzzy FMEA not only solves problems of traditional FMEA, but also is highly in accordance with it, in terms of some priorities. According to results of fuzzy FMEA, failure modes E, pertaining to the sash of the rear right door, and H, related to the sash of the front the left door, have been ranked as the most and the least critical situations, respectively. The prioritized failures could be considered to facilitate future quality optimization. Practical implications This research provides quality engineers of the studied company with the chance of ranking their failure modes based on a fuzzy expert system. Originality/value This study utilizes the fuzzy logic approach to solve some major limitations of FMEA, an extensively applied method in the automobile industry.

A Novel Failure Mode and Effect Analysis Approach Integrating Probabilistic Linguistic Term Sets and Fuzzy Petri Nets

“…A modified FMEA model, which consists of fuzzification, fuzzy rule-based inference, and defuzzification, was suggested by Yu and Lee [44] for the risk assessment of urban regeneration projects. Kumru and Kumru [45] applied a fuzzy risk assessment method to improve the purchasing process of a public hospital, and Geramian et al [46] utilized the fuzzy rule-based FMEA to increase the reliability of door production in a compact car. Cheng and Lu [47] developed a risk ranking system for the complex pipe jacking construction project for water transmission.…”

Development of quality improvement matrix: An integrated tools for quality improvement

“…On the other hand, FMEA is a technique that provides information for risk management decisions. It is used to investigate the causes and effects of defects that are further detected in order to reduce the possibility of dislocation [2]. This tool incorporates technology and draws from one to the next in identifying a possible failure method of a process or product to be further developed to address its responses [9].…”

Automatic Generation of Programs for Data Tables with Batch Least Square Mamdani Inference Systems: Application in the AWG Table

“…Fuzzy inference systems have been spread in several areas since its very beginning in 1965, today there still appearing several applications because of its capability for mimic human reasoning using expert knowledge. There are several papers where fuzzy systems have been applied in the recent years, they have been used in image enhancement in [6], image compression analysis in [7], risk evaluation and periodization in industries in [8], estimating the length-weight relationship of fishes in [9], for failure analyzing in automobile industry in [10], in the assessment of power transformers in [11], among other several applications.…”