Failure analysis of CNC machines due to human errors: An integrated IT2F-MCDM-based FMEA approach

Abstract Computer numerical control (CNC) machining has become one of the salient transformative technologies due to its ability to manufacture precise components, scalable production, constant uptime, uniformity and repeatability. Despite the claim of minimum human intervention during CNC machining operation, operators’ involvement leading to CNC machine failures cannot be totally neglected. In this paper, a modified failure modes and effects analysis (FMEA)-based risk analysis approach is presented to identify the major human errors, their causes and effects during CNC machining operation based on the participation of a team of cross-functional experts. An integrated interval type-2 fuzzy sets (IT2FSs)-based multi-criteria decision making (MCDM) framework is subsequently developed for prioritizing the risks associated with each identified human error with respect to four pertinent risk factors, i.e. safety, economic consequences, occurrence and detection. However, due to lack of exact numerical values, the errors are linguistically assessed by the experts. To deal with subjective uncertainties, IT2FSs are chosen as a viable alternative due to their numerous superiorities over the traditional type-1 fuzzy sets (T1FSs). The IT2F-analytic hierarchy process (IT2F-AHP) is adopted to calculate weights of the risk factors, and IT2F-decision making trial and evaluation laboratory (IT2F-DEMATEL) is employed to model the causal interactions among different errors. For risk ranking, the traditional measurement of alternatives and ranking according to compromise solution (MARCOS) method is extended in IT2F-domain (IT2F-MARCOS). Finally, the computed risk ranking results are contrasted with other popular IT2FSs and T1FSs-based MCDM methods to validate feasibility of the proposed approach. This integrated framework comprising IT2F-AHP, IT2F-DEMATEL and IT2F-MARCOS is novel with respect to its development as well as application context and would surely be an interesting tool helping the entire decision making community.

[1]  Silvia Carpitella,et al.  A combined multi-criteria approach to support FMECA analyses: A real-world case , 2018, Reliab. Eng. Syst. Saf..

[2]  Adil Baykasoğlu,et al.  Comprehensive fuzzy FMEA model: a case study of ERP implementation risks , 2017, Operational Research.

[3]  Jindong Qin,et al.  Risk priorization for failure modes with extended MULTIMOORA method under interval type-2 fuzzy environment , 2019, J. Intell. Fuzzy Syst..

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

[5]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[6]  Cengiz Kahraman,et al.  Fuzzy analytic hierarchy process with interval type-2 fuzzy sets , 2014, Knowl. Based Syst..

[7]  Z. J. Yang,et al.  Reliability analysis of machining center based on the field data , 2013 .

[8]  Hu-Chen Liu.  Improved FMEA Methods for Proactive Healthcare Risk Analysis , 2019 .

[9]  Prasenjit Chatterjee,et al.  Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS) , 2020, Comput. Ind. Eng..

[10]  Feng Li,et al.  A novel approach to emergency risk assessment using FMEA with extended MULTIMOORA method under interval-valued Pythagorean fuzzy environment , 2020, Int. J. Intell. Comput. Cybern..

[11]  Lin Li,et al.  A novel risk assessment model based on failure mode and effect analysis and probabilistic linguistic ELECTRE II method , 2020, J. Intell. Fuzzy Syst..

[12]  J. Maiti,et al.  Granulized Z-VIKOR Model for Failure Mode and Effect Analysis , 2022, IEEE Transactions on Fuzzy Systems.

[13]  James J. H. Liou,et al.  A novel multiple-criteria decision-making-based FMEA model for risk assessment , 2018, Appl. Soft Comput..

[14]  John R. Wilson,et al.  HEDOMS—Human Error and Disturbance Occurrence in Manufacturing Systems: Toward the development of an analytical framework , 1999 .

[15]  Guofa Li,et al.  Advanced FMEA method based on interval 2-tuple linguistic variables and TOPSIS , 2019, Quality Engineering.

[16]  Ahmet Can Kutlu,et al.  Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP , 2012, Expert Syst. Appl..

[17]  Muhammet Gul,et al.  An FMEA-based TOPSIS approach under single valued neutrosophic sets for maritime risk evaluation: the case of ship navigation safety , 2020, Soft Computing.

[18]  Liliane Pintelon,et al.  A new hybrid MCDM approach for RPN evaluation for a medical device prototype , 2021, Qual. Reliab. Eng. Int..

[19]  Bin Shuai,et al.  Failure mode and effect analysis using regret theory and PROMETHEE under linguistic neutrosophic context , 2020 .

[20]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[21]  Muhammet Gul,et al.  A modified failure modes and effects analysis using interval-valued spherical fuzzy extension of TOPSIS method: case study in a marble manufacturing facility , 2021, Soft Computing.

[22]  E. Celik,et al.  A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method , 2021, Complex & Intelligent Systems.

[23]  Adil Baykasolu,et al.  Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS , 2017, Expert Syst. Appl..

[24]  Afshin Jamshidi,et al.  Using Fuzzy Cost‐Based FMEA, GRA and Profitability Theory for Minimizing Failures at a Healthcare Diagnosis Service , 2015, Qual. Reliab. Eng. Int..

[25]  Darko Bozanic,et al.  Multi-criteria FUCOM – Fuzzy MABAC model for the selection of location for construction of single-span bailey bridge , 2019, Decision Making: Applications in Management and Engineering.

[26]  Gwo-Hshiung Tzeng,et al.  A hybrid MCDM-based FMEA model for identification of critical failure modes in manufacturing , 2020, Soft Computing.

[27]  Muhammad Akram,et al.  Risk evaluation in failure modes and effects analysis: hybrid TOPSIS and ELECTRE I solutions with Pythagorean fuzzy information , 2020, Neural Computing and Applications.

[28]  Hu-Chen Liu,et al.  Failure mode and effect analysis using multi-criteria decision making methods: A systematic literature review , 2019, Comput. Ind. Eng..

[29]  Zhichao Wang,et al.  An extended MULTIMOORA method based on OWGA operator and Choquet integral for risk prioritization identification of failure modes , 2020, Eng. Appl. Artif. Intell..

[30]  Muhammet Gul,et al.  A manufacturing failure mode and effect analysis based on fuzzy and probabilistic risk analysis , 2020, Appl. Soft Comput..

[31]  Priyank Srivastava,et al.  Qualitative analysis of CNG dispensing system using fuzzy FMEA–GRA integrated approach , 2018, Int. J. Syst. Assur. Eng. Manag..

[32]  Integration of failure mode, effects, and criticality analysis with multi-criteria decision-making in engineering applications: Part I – Manufacturing industry , 2021 .

[33]  Jose Arturo Garza-Reyes,et al.  Decision-making for risk evaluation: integration of prospect theory with failure modes and effects analysis (FMEA) , 2020 .

[34]  Jerry M. Mendel,et al.  Interval Type-2 Fuzzy Logic Systems Made Simple , 2006, IEEE Transactions on Fuzzy Systems.

[35]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[36]  Muhammad Akram,et al.  Digraph and matrix approach for risk evaluations under Pythagorean fuzzy information , 2021, Expert Syst. Appl..

[37]  Ta-Chun Wen,et al.  Soft failure mode and effects analysis using the OWG operator and hesitant fuzzy linguistic term sets , 2018, J. Intell. Fuzzy Syst..

[38]  Soumava Boral,et al.  A hybrid decision making framework for modified failure mode effects and criticality analysis , 2019, International Journal of Quality & Reliability Management.

[39]  W. Neumann,et al.  Ageing and human-system errors in manufacturing: a scoping review , 2020, Int. J. Prod. Res..

[40]  J. K. Jha,et al.  Evaluation of risks in foodgrains supply chain using failure mode effect analysis and fuzzy VIKOR , 2020 .

[41]  Kristoffer K. McKee,et al.  An Integrated Approach for Fuzzy Failure Mode and Effect Analysis Using Fuzzy AHP and Fuzzy MARCOS , 2020, 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[42]  Soumava Boral,et al.  A novel hybrid multi-criteria group decision making approach for failure mode and effect analysis: An essential requirement for sustainable manufacturing , 2020, Sustainable Production and Consumption.

[43]  Dilbagh Panchal,et al.  A STRUCTURED FRAMEWORK FOR RELIABILITY AND RISK EVALUATION IN THE MILK PROCESS INDUSTRY UNDER FUZZY ENVIRONMENT , 2021, Facta Universitatis, Series: Mechanical Engineering.

[44]  Jindong Qin,et al.  Failure mode and effects analysis (FMEA) for risk assessment based on interval type-2 fuzzy evidential reasoning method , 2020, Appl. Soft Comput..