Defect prediction model for wrapping machines assembly
Defect prediction model for wrapping machines assembly
Índice
Purpose – Development of a defect prediction model for the assembly of wrapping machines.
Design/methodology/approach – The assembly process of wrapping machines is firstly decomposed into several steps, called workstations, each one potentially critical in generating defects. According to previous studies, two assembly complexity factors related to the process and the design are evaluated. Experimental defect rates in each workstation are collected and a bivariate prediction model is developed.
Findings – Defects occurring in low-volume production, such as those of wrapping machines, may be predicted by exploiting the complexity based on the process and the design of the assembly.
Research limitations/implications – Although the defect prediction model is designed for the assembly of wrapping machines, the research approach can provide a framework for future investigation on other low-volume productions of similar electromechanical and mechanical products.
Practical implications – The defect prediction model is a powerful tool for quantitatively estimating defects of newly developed wrapping machines and supporting decisions for assembly quality- oriented design and optimisation.
Originality/value – The proposed model is one of the first attempts to predict defects in low-volume production, where the limited historical data available and the inadequacy of traditional statistical approaches make the quality control extremely challenging.
See paper
Aft, L.S. (2000), Work Measurement and Methods Improvement, John Wiley & Sons, Hoboken, NJ, USA.
Alkan, B. (2019), “An experimental investigation on the relationship between perceived assembly complexity and product design complexity”, International Journal on Interactive Design and Manufacturing (IJIDeM), Vol. 13 No. 3, pp. 1145–1157.
Antani, K.R. (2014), A Study of the Effects of Manufacturing Complexity on Product Quality in Mixed-Model Automotive Assembly, PhD dissertation, Mechanical Engineering Department, Clemson University.
Bates, D.M. and Watts, D.G. (1988), Nonlinear Regression Analysis and Its Applications., John Wiley & Sons, Inc., Hoboken, NJ, USA.
Ben-Arieh, D. (1994), “A methodology for analysis of assembly operations’ difficulty”, International Journal of Production Research, Taylor & Francis, Vol. 32 No. 8, pp. 1879–1895.
Boothroyd, G. and Alting, L. (1992), “Design for assembly and disassembly”, CIRP Annals, Elsevier, Vol. 41 No. 2, pp. 625–636.
Devore, J.L. (2011), Probability and Statistics for Engineering and the Sciences, Cengage learning, Boston, USA.
Dong, Q. and Saaty, T.L. (2014), “An analytic hierarchy process model of group consensus”, Journal of Systems Science and Systems Engineering, Vol. 23 No. 3, pp. 362–374.
Falck, A.-C., Örtengren, R., Rosenqvist, M. and Söderberg, R. (2017), “Proactive assessment of basic complexity in manual assembly: development of a tool to predict and control operator-induced quality errors”, International Journal of Production Research, Vol. 55 No. 15, pp. 4248–4260.
Franceschini, F., Galetto, M., Genta, G. and Maisano, D.A. (2018), “Selection of quality-inspection procedures for short-run productions”, The International Journal of Advanced Manufacturing Technology, Vol. 99 No. 9–12, pp. 2537–2547.
Galetto, M., Genta, G., Maculotti, G. and Verna, E. (2020), “Defect Probability Estimation for Hardness-Optimised Parts by Selective Laser Melting”, International Journal of Precision Engineering and Manufacturing, DOI: 10.1007/s12541-020-00381-1.
Galetto, M., Verna, E. and Genta, G. (2020), “Accurate estimation of prediction models for operator- induced defects in assembly manufacturing processes”, Quality Engineering, DOI: 10.1080/08982112.2019.1700274.
Galetto, M., Verna, E., Genta, G. and Franceschini, F. (2020), “Uncertainty evaluation in the prediction of defects and costs for quality inspection planning in low-volume productions”, The International Journal of Advanced Manufacturing Technology, Vol. 108 No. 11, pp. 3793–3805.
Genta, G., Galetto, M. and Franceschini, F. (2018), “Product complexity and design of inspection strategies for assembly manufacturing processes”, International Journal of Production Research, Vol. 56 No. 11, pp. 4056–4066.
Genta, G., Galetto, M. and Franceschini, F. (2020), “Inspection procedures in manufacturing processes: recent studies and research perspectives”, International Journal of Production Research, DOI: 10.1080/00207543.2020.1766713.
Hinckley, C.M. (1994), A Global Conformance Quality Model. A New Strategic Tool for Minimising Defects Caused by Variation, Error, and Complexity, PhD dissertation, Mechanical Engineering Department, Stanford University.
Hinckley, C.M. and Barkan, P. (1995), A Conceptual Design Methodology for Enhanced Conformance Quality, Sandia National Labs., Livermore, CA (United States).
Krugh, M., Antani, K., Mears, L. and Schulte, J. (2016), “Prediction of Defect Propensity for the Manual Assembly of Automotive Electrical Connectors”, Procedia Manufacturing, Vol. 5, pp. 144– 157.
Saaty, T.L. (1980), The Analytic Hierarchy Process, McGraw-Hill, New York.
Shibata, H. (2002), Global Assembly Quality Methodology: A New Methodology for Evaluating Assembly Complexities in Globally Distributed Manufacturing, PhD dissertation, Mechanical Engineering Department, Stanford University.
Shibata, H., Cheldelin, B. and Ishii, K. (2003), “Assembly quality methodology: A new method for evaluating assembly complexity in globally distributed manufacturing”, in ASME 2003 International Mechanical Engineering Congress and Exposition in Washington, DC, USA, November 15–21, 2003, The American Society of Mechanical Engineers, pp. 335–344.
Shin, D., Wysk, R.A. and Rothrock, L. (2006), “An investigation of a human material handler on part flow in automated manufacturing systems”, IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans, Vol. 36 No. 1, pp. 123–135.
Sinha, K. (2014), Structural Complexity and Its Implications for Design of Cyber-Physical Systems, PhD dissertation, Engineering Systems Division, Massachusetts Institute of Technology.
Spiess, A.N. and Neumeyer, N. (2010), “An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: A Monte Carlo approach”, BMC Pharmacology, Vol. 10, pp. 1–11.
Su, Q., Liu, L. and Whitney, D.E. (2010), “A systematic study of the prediction model for operator- induced assembly defects based on assembly complexity factors”, IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans, Vol. 40 No. 1, pp. 107–120.
Verna, E., Genta, G., Galetto, M. and Franceschini, F. (2020a), “Defect prediction models to improve assembly processes in low-volume productions”, forthcoming in 8th CIRP Conference of Assembly Technology and Systems in Athens, Greece, 29 September-1 October, 2020, Procedia CIRP.
Verna, E., Genta, G., Galetto, M. and Franceschini, F. (2020b), “Planning offline inspection strategies in low-volume manufacturing processes”, Quality Engineering, DOI: 10.1080/08982112.2020.1739309.
Verna, E., Genta, G., Galetto, M. and Franceschini, F. (2020c), “Inspection planning by defect prediction models and inspection strategy maps for low-volume productions”, submitted to The International Journal of Advanced Manufacturing Technology.
Verna, E., Genta, G., Galetto, M. and Franceschini, F. (2020d), “Product assembly and defect prediction: a novel model based on the structural complexity paradigm”, submitted to International Journal of Production Research.
Wei, C.C., Chien, C.F. and Wang, M.J.J. (2005), “An AHP-based approach to ERP system selection”, International Journal of Production Economics, Vol. 96 No. 1, pp. 47–62.
Yamagiwa, Y. (1988), “An assembly ease evaluation method for product designers: DAC”, Techno Japan, Vol. 21 No. 12, pp. 26–29.
Zhang, F. and Luk, T. (2007), “A Data Mining Algorithm for Monitoring PCB Assembly Quality”, IEEE Transactions on Electronics Packaging Manufacturing, Vol. 30 No. 4, pp. 299–305.
Defect prediction, Assembly, Low-volume production, Wrapping machines