Design for automated inspection in remanufacturing: a discrete event simulation for process improvement

Abstract Remanufacturing is a process of restoring end-of-life (Eol) products to “as new” condition with a matching warranty. It offers significant economic benefits to remanufacturers as well as providing enormous environmental and social benefits. However, achieving remanufacturing is complex and uncertain due to varying product return quantity and quality. These uncertainties call for proper assessment of returned Eol products to extract the most value from them. The evaluation involves inspecting the Eol products on arrival for remanufacturing, thereby making the most appropriate Eol decisions. These decisions are made by expert personnel by visually assess these products manually or using semi-automated techniques. However, the accuracy of these decisions is subjective, time-consuming, and prone to missing defects. The need to develop processes and tools for efficient, reliable, and repeatable inspection in remanufacturing becomes inevitable. Automated inspection becomes handy to improve the assessment of these defects as well as saving time. This paper proposes design-for-inspection as a tool to enhance remanufacturing. A discrete event simulation of this model for the torque converter remanufacturing process is presented and highlights the benefits of automating the remanufacturing inspection process. It further suggests the design improvements to obtain an enhanced automated inspection.

[1]  Jing Lin,et al.  A comprehensive review on convolutional neural network in machine fault diagnosis , 2020, Neurocomputing.

[2]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[3]  Jörg Krüger,et al.  Vision-based Identification Service for Remanufacturing Sorting , 2018 .

[4]  Erik Sundin,et al.  Remanufacturing challenges and possible lean improvements , 2018 .

[5]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[6]  W. Ijomah,et al.  Deep Learning Based Vision Inspection System for Remanufacturing Application , 2020 .

[7]  Stephen J. Childe,et al.  A business process model of inspection in remanufacturing , 2013 .

[8]  Keith Worden,et al.  A Gaussian mixture model for automated corrosion detection in remanufacturing , 2018 .

[9]  Duncan McFarlane,et al.  Value of RFID in remanufacturing , 2007 .

[10]  Rolf Steinhilper,et al.  Modular Simulation Model for Remanufacturing Operations , 2017 .

[11]  James F. C. Windmill,et al.  Design for remanufacture: a literature review and future research needs , 2011 .

[12]  Vishal Fegade,et al.  Design for Remanufacturing: Methods and their Approaches , 2015 .

[13]  Martin Charter,et al.  Remanufacturing and product design , 2008 .

[14]  Todd Rudberg,et al.  AFP Automated Inspection System Performance and Expectations , 2017 .

[15]  Amit P. Sheth,et al.  Machine learning for Internet of Things data analysis: A survey , 2017, Digit. Commun. Networks.

[16]  Anil Mital,et al.  Manual, Hybrid and Automated Inspection Literature and Current Research , 1993 .

[17]  Jonathan Corney,et al.  Improving the efficiency of remanufacture through enhanced pre-processing inspection – a comprehensive study of over 2000 engines at Caterpillar remanufacturing, U.K. , 2019, Production Planning & Control.

[18]  Hong-Chao Zhang,et al.  The State-of-the-Art Surveys for Application of Metal Magnetic Memory Testing in Remanufacturing , 2011 .

[19]  Sami Kara,et al.  Design, management and control of demanufacturing and remanufacturing systems , 2017 .

[20]  Surendra M. Gupta,et al.  Buffer allocation plan for a remanufacturing cell , 2005, Comput. Ind. Eng..

[21]  Nidhal Rezg,et al.  New dynamic heuristic for the optimization of opportunities to use new and remanufactured spare part in stochastic degradation context , 2017, J. Intell. Manuf..

[22]  Roger Mohr,et al.  Optimal camera placement for accurate reconstruction , 2002, Pattern Recognit..

[23]  Ali Fuat Guneri,et al.  The use of Arena simulation programming for decision making in a workshop study , 2008, Comput. Appl. Eng. Educ..

[24]  Yanbin Du,et al.  An integrated method for evaluating the remanufacturability of used machine tool , 2012 .

[25]  Mohd Yazid Abu,et al.  Classification of crankshaft remanufacturing using Mahalanobis-Taguchi system , 2016 .

[26]  Anthony Gachagan,et al.  Achieving remanufacturing inspection using deep learning , 2020 .

[27]  William Ion,et al.  Low cost three-dimensional virtual model construction for remanufacturing industry , 2017, Journal of Remanufacturing.

[28]  Emelia Sari,et al.  Pattern Recognition on Remanufacturing Automotive Component as Support Decision Making Using Mahalanobis-taguchi System☆ , 2015 .

[29]  Muris Lage Junior,et al.  Production planning and control for remanufacturing: exploring characteristics and difficulties with case studies , 2016 .

[30]  Mourad Oubrich,et al.  The optimization of Reverse Logistics activities: A Literature Review and Future Directions , 2018, 2018 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD).

[31]  Anthony J. Mulholland,et al.  Design-for-testing for improved remanufacturability , 2017 .

[32]  Sara Ridley,et al.  A novel pre-processing inspection methodology to enhance productivity in automotive product remanufacture: an industry-based research of 2196 engines , 2015 .