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IJAT Vol.15 No.5 pp. 641-650
doi: 10.20965/ijat.2021.p0641
(2021)

Paper:

Multi-Layer Quality Inspection System Framework for Industry 4.0

Victor Azamfirei, Anna Granlund, and Yvonne Lagrosen

Mälardalen University
15 Hamngatan, Eskilstuna 632 20, Sweden

Corresponding author

Received:
February 23, 2021
Accepted:
April 26, 2021
Published:
September 5, 2021
Keywords:
quality inspection, Industry 4.0, cyber-physical systems, zero-defect manufacturing, CAD/CAM/CAE
Abstract

In the era of market globalisation, the quality of products has become a key factor for success in the manufacturing industry. The growing demand for customised products requires a corresponding adjustment of processes, leading to frequent and necessary changes in production control. Quality inspection has been historically used by the manufacturing industry to detect defects before customer delivery of the end product. However, traditional quality methods, such as quality inspection, suffer from large limitations in highly customised small batch production. Frameworks for quality inspection have been proposed in the current literature. Nevertheless, full exploitation of the Industry 4.0 context for quality inspection purpose remains an open field. Vice-versa, for quality inspection to be suitable for Industry 4.0, it needs to become fast, accurate, reliable, flexible, and holistic. This paper addresses these challenges by developing a multi-layer quality inspection framework built on previous research on quality inspection in the realm of Industry 4.0. In the proposed framework, the quality inspection system consists of (a) the work-piece to be inspected, (b) the measurement instrument, (c) the actuator that manipulates the measurement instrument and possibly the work-piece, (d) an intelligent control system, and (e) a cloud-connected database to the previous resources; that interact with each other in five different layers, i.e., resources, actions, and data in both the cyber and physical world. The framework is built on the assumption that data (used and collected) need to be validated, holistic and on-line, i.e., when needed, for the system to effectively decide upon conformity to surpass the presented challenges. Future research will focus on implementing and validating the proposed framework in an industrial case study.

Cite this article as:
Victor Azamfirei, Anna Granlund, and Yvonne Lagrosen, “Multi-Layer Quality Inspection System Framework for Industry 4.0,” Int. J. Automation Technol., Vol.15, No.5, pp. 641-650, 2021.
Data files:
References
  1. [1] D. Imkamp, J. Berthold, M. Heizmann, K. Kniel, M. Peterek, R. Schmitt, J. Seidler, and K. D. Sommer, “Challenges and trends in manufacturing measurement technology – the “Industrie 4.0” concept,” Technisches Messen, Vol.83, No.7-8, pp. 417-429, 2016.
  2. [2] K. S. Wang, “Towards zero-defect manufacturing (ZDM)-a data mining approach,” Advances in Manufacturing, Vol.1, No.1, pp. 62-74, 2013.
  3. [3] M. Babu, P. Franciosa, and D. Ceglarek, “Spatio-Temporal Adaptive Sampling for effective coverage measurement planning during quality inspection of free form surfaces using robotic 3D optical scanner,” J. of Manufacturing Systems, Vol.53, pp. 93-108, 2019.
  4. [4] F. M. Gryna and J. M. Juran (Eds.), “Juran’s quality control handbook,” McGraw-Hill, 4th edition, 1988.
  5. [5] M. Uekita and Y. Takaya, “On-machine dimensional measurement of large parts by compensating for volumetric errors of machine tools,” Precision Engineering, Vol.43, pp. 200-210, 2016.
  6. [6] M. Colledani, D. Coupek, A. Verl, J. Aichele, and A. Yemane, “A cyber-physical system for quality-oriented assembly of automotive electric motors,” CIRP J. of Manufacturing Science and Technology, Vol.20, pp. 12-22, 2018.
  7. [7] G. May and D. Kiritsis, “Zero Defect Manufacturing Strategies and Platform for Smart Factories of Industry 4.0,” Proc. of the 4th Int. Conf. on the Industry 4.0 Model for Advanced Manufacturing, pp. 142-152, 2019.
  8. [8] S. Phuyal, D. Bista, and R. Bista, “Challenges, Opportunities and Future Directions of Smart Manufacturing: A State of Art Review,” Sustainable Futures, Vol.2, 100023, 2020.
  9. [9] M. A. Akhloufi and B. Verney, “Fusion framework for 3D inspection and thermal NDT,” SAE Technical Papers, 7, 2013.
  10. [10] M. Papananias, T. E. McLeay, M. Mahfouf, and V. Kadirkamanathan, “A Bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing,” Computers in Industry, Vol.105, pp. 35-47, 2019.
  11. [11] V. K. Pathak, A. K. Singh, M. Sivadasan, and N. K. Singh, “Framework for Automated GD&T Inspection Using 3D Scanner,” J. of The Institution of Engineers (India): Series C, Vol.99, No.2, pp. 197-205, 2018.
  12. [12] O. Anokhin and R. Anderl, “Towards design for cyber-physical inspection,” Procedia CIRP, Vol.84, pp. 400-405, 2019.
  13. [13] P. Cicconi and R. Raffaeli, “An Industry 4.0 Framework for the Quality Inspection in Gearboxes Production,” Proc. of CAD’19, pp. 97-100, 2019.
  14. [14] D. Evangelista, M. Antonelli, A. Pretto, C. Eitzinger, M. Moro, C. Ferrari, and E. Menegatti, “SPIRIT – A Software Framework for the Efficient Setup of Industrial Inspection Robots,” Proc. of the 2020 IEEE Int. Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2020, pp. 622-626, 2020.
  15. [15] J. Schmitt, J. Bönig, T. Borggräfe, G. Beitinger, and J. Deuse, “Predictive model-based quality inspection using Machine Learning and Edge Cloud Computing,” Advanced Engineering Informatics, Vol.45, 101101, 2020.
  16. [16] Y. Takaya, “In-Process and On-Machine Measurement of Machining Accuracy for Process and Product Quality Management: A Review,” Int. J. Automation Technol., Vol.8, No.1, pp. 4-19, 2013.
  17. [17] Y. Li, C. Liu, J. X. Gao, and W. Shen, “An integrated feature-based dynamic control system for on-line machining, inspection and monitoring,” Integrated Computer-Aided Engineering, Vol.22, No.2, pp. 187-200, 2015.
  18. [18] Chrysler Group LLC, Ford Motor Company, and General Motors Corporation, “Measurement system analysis (MSA),” AIAG, 4th edition, 2010.
  19. [19] ISO, “ISO 2859-1:1999(en) Sampling procedures for inspection by attributes – Part 1: Sampling schemes indexed by acceptance quality limit (AQL) for lot-by-lot inspection,” Technical Report, 1999.
  20. [20] ISO, “ISO 2859-2:2020(en) Sampling procedures for inspection by attributes – Part 2: Sampling plans indexed by limiting quality (LQ) for isolated lot inspection,” Technical Report, 2020.
  21. [21] J. M. Juran, “Management of inspection and quality control,” Harper & Brothers, 1945.
  22. [22] T. Pfeifer, “Production metrology,” Walter de Gruyter GmbH & Co KG, 2015.
  23. [23] C. Karlsson (Ed.), “Research Methods for Operations Management,” Routledge, 2016.
  24. [24] ISO, “ISO 17020 Conformity assessment – Requirements for the operation of various types of bodies performing inspection,” Technical Report, 2012.
  25. [25] L. Pendrill, “Man as a Measurement Instrument,” NCSLI Measure, Vol.9, No.4, pp. 24-35, 2014.
  26. [26] M. Germani, F. Mandorli, M. Mengoni, and R. Raffaeli, “CAD-based environment to bridge the gap between product design and tolerance control,” Precision Engineering, Vol.34, No.1, pp. 7-15, 2010.
  27. [27] A. E. Jaramillo, P. Boulanger, and F. Prieto, “On-line 3-D system for the inspection of deformable parts,” Int. J. Adv. Manuf. Technol., pp. 1053-1063, 2011.
  28. [28] J. Molleda, J. L. Carús Candás, R. Usamentiaga, D. F. García, J. C. Granda, and J. L. Rendueles, “A fast and robust decision support system for in-line quality assessment of resistance seam welds in the steelmaking industry,” Computers in Industry, Vol.63, No.3, pp. 222-230, 2012.
  29. [29] K. Wang and Q. Yu, “Product quality inspection combining with structure light system, data mining and RFID technology,” IFIP Advances in Information and Communication Technology, Vol.411, pp. 205-220, 2013.
  30. [30] L. Stroppa, P. Castellini, and N. Paone, “Self-Optimizing Robot Vision for Online Quality Control,” Experimental Techniques, Vol.40, No.3, pp. 1051-1064, 2015.
  31. [31] E. Kiraci, P. Franciosa, G. A. Turley, A. Olifent, A. Attridge, and M. A. Williams, “Moving towards in-line metrology: evaluation of a Laser Radar system for in-line dimensional inspection for automotive assembly systems,” Int. J. of Advanced Manufacturing Technology, Vol.91, No.1-4, pp. 69-78, 2017.
  32. [32] R. Söderberg, K. Wärmefjord, J. S. Carlson, and L. Lindkvist, “Toward a Digital Twin for real-time geometry assurance in individualized production,” CIRP Annals – Manufacturing Technology, Vol.66, No.1, pp. 137-140, 2017.
  33. [33] V. Majstorovic, S. Stojadinovic, S. Zivkovic, D. Djurdjanovic, Z. Jakovljevic, and N. Gligorijevic, “Cyber-Physical Manufacturing Metrology Model (CPM3) for Sculptured Surfaces – Turbine Blade Application,” Procedia CIRP, Vol.63, pp. 658-663, 2017.
  34. [34] W. Gao, H. Haitjema, F. Z. Fang, R. K. Leach, C. F. Cheung, E. Savio, and J. M. Linares, “On-machine and in-process surface metrology for precision manufacturing,” CIRP Annals, Vol.68, Issue 2, pp. 843-866, 2019.
  35. [35] W. P. Syam, K. Rybalcenko, A. Gaio, J. Crabtree, and R. K. Leach, “Methodology for the development of in-line optical surface measuring instruments with a case study for additive surface finishing,” Optics and Lasers in Engineering, Vol.121, pp. 271-288, 2019.
  36. [36] N. D. M. Phan, Y. Quinsat, S. Lavernhe, and C. Lartigue, “Scanner path planning with the control of overlap for part inspection with an industrial robot,” Int. J. of Advanced Manufacturing Technology, Vol.98, No.1-4, pp. 629-643, 2018.
  37. [37] F. Xu, F. Yang, X. Wu, Q. Guo, and C. Zhao, “Application and experiments of 5g technology powered industrial internet,” Proc. of the 2019 IEEE Int. Conf. on Communications Workshops, ICC Workshops, pp. 1-6, 2019.
  38. [38] X. Zhang, Y. Cheng, L. Hu, S. Wei, and F. Wu, “Machine Vision On-line Detection System: Applications and Standardization Requirements,” Proc. of 2020 IEEE Int. Conf. on Artificial Intelligence and Computer Applications (ICAICA 2020), pp. 1200-1204, 2020.
  39. [39] H. Ding, R. X. Gao, A. J. Isaksson, R. G. Landers, T. Parisini, and Y. Yuan, “State of AI-Based Monitoring in Smart Manufacturing and Introduction to Focused Section,” IEEE/ASME Trans. on Mechatronics, Vol.25, No.5, pp. 2143-2154, 2020.
  40. [40] Y. Li, C. Liu, X. Hao, J. X. Gao, and P. G. Maropoulos, “Responsive fixture design using dynamic product inspection and monitoring technologies for the precision machining of large-scale aerospace parts,” CIRP Annals – Manufacturing Technology, Vol.64, No.1, pp. 173-176, 2015.

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Last updated on Sep. 19, 2021