Fuzzy logic-based FMEA robust design: a quantitative approach for robustness against groupthink in group/team decision-making

Group/team decision-making is an integral part of almost all failure mode and effects analysis (FMEA) projects. A dysfunctional aspect of this decision-making fashion in fuzzy FMEA is that group/team members’ designs for membership functions and IF-THEN rules may be overshadowed by a member’s design. This problem is caused by groupthink, a pitfall known by the Organisational Behaviour science. This study aims to develop a fuzzy FMEA approach which is robust to the problem. We applied the Taguchi’s robust parameter design and investigated the effects of various control parameters namely Defuzzification, Aggregation, And and Implication operators for the fuzzy inference system (FIS). Our experiments illustrate that the control parameters, in the above-mentioned order, have the most effect on the signal-to-noise ratio (SNR). These factors’ optimal setting consists of the Centroid, Sum, Minimum and Minimum levels, respectively.

[1]  Ahmed A. D. Sarhan,et al.  Increasing the productivity of the wire-cut electrical discharge machine associated with sustainable production , 2015 .

[2]  Taho Yang,et al.  Improving the reliability of electronic paper display using FMEA and Taguchi methods: A case study , 2014, Microelectron. Reliab..

[3]  Chung-Neng Huang,et al.  Integration of Taguchi's method and multiple-input, multiple-output ANFIS inverse model for the optimal design of a water-cooled condenser , 2016 .

[4]  Nan Liu,et al.  Risk evaluation approaches in failure mode and effects analysis: A literature review , 2013, Expert Syst. Appl..

[5]  Antonino Staiano,et al.  A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference , 2015, Expert Syst. Appl..

[6]  J. B. Bowles,et al.  Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis , 1995 .

[7]  Nalinee Chanamool,et al.  Fuzzy FMEA application to improve decision-making process in an emergency department , 2016, Appl. Soft Comput..

[8]  Mohammad Reza Mehregan,et al.  Fuzzy inference system application for failure analyzing in automobile industry , 2017 .

[9]  Om Prakash Yadav,et al.  A framework for capturing and analyzing the failures due to system/component interactions , 2008, Qual. Reliab. Eng. Int..

[10]  Mika Liukkonen,et al.  Robust parameter design for the micro-BGA stencil printing process using a fuzzy logic-based Taguchi method , 2016, Appl. Soft Comput..

[11]  Loon Ching Tang,et al.  Fuzzy assessment of FMEA for engine systems , 2002, Reliab. Eng. Syst. Saf..

[12]  R. Gnanadass,et al.  Optimization of process parameters through fuzzy logic and genetic algorithm - A case study in a process industry , 2015, Appl. Soft Comput..

[13]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[14]  Anne Johannes Jan Braaksma,et al.  A quantitative method for Failure Mode and Effects Analysis , 2012 .

[15]  Chee-Cheng Chen.  A developed autonomous preventive maintenance programme using RCA and FMEA , 2013 .

[16]  Kazem Abhary,et al.  Multi-objective optimisation of dynamic scheduling in robotic flexible assembly cells via fuzzy-based Taguchi approach , 2016, Comput. Ind. Eng..

[17]  Hu-Chen Liu,et al.  A Novel Approach for FMEA: Combination of Interval 2‐Tuple Linguistic Variables and Gray Relational Analysis , 2015, Qual. Reliab. Eng. Int..

[18]  Kazem Abhary,et al.  Simulation modelling and analysis of scheduling in robotic flexible assembly cells using Taguchi method , 2014 .

[19]  Jian-Bo Yang,et al.  Development of a fuzzy FMEA based product design system , 2008 .

[20]  Y. D. Hwang,et al.  AN IMPROVED TAGUCHI'S METHOD IN DESIGN OF EXPERIMENTS FOR MILLING CFRP COMPOSITE , 1997 .

[21]  Fiorenzo Franceschini,et al.  A new approach for evaluation of risk priorities of failure modes in FMEA , 2001 .

[22]  M. V. Glinow,et al.  Organizational Behavior: Emerging Knowledge and Practice for the Real World , 2010 .

[23]  Antonella Petrillo,et al.  Multi-criteria risk analysis to improve safety in manufacturing systems , 2012 .

[24]  Jiju Antony,et al.  Optimization of multiple responses using a fuzzy-rule based inference system , 2002 .

[25]  Tsu-Ming Yeh,et al.  Fuzzy-based risk priority number in FMEA for semiconductor wafer processes , 2014 .

[26]  Jie Ren,et al.  A fuzzy expert system , 1989 .

[27]  Chin Jeng Feng,et al.  Approach to prediction of laser cutting quality by employing fuzzy expert system , 2011, Expert Syst. Appl..