Automated System For Manufacturing Defects Detection


EOI: 10.11242/viva-tech.01.06.017

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Citation

Mr. Keyur Dattani, Mr. Mandar Koli, Mr. Mangesh Kini, Mr.Prathamesh Dongre, "Automated System For Manufacturing Defects Detection", VIVA-IJRI Volume 1, Issue 6, Article 17, pp. 1-5, 2023. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

The Artificial intelligence (AI) is being increasingly used for quality inspection in manufacturing.Quality inspection is a critical process in manufacturing, as it ensures that products meet customer specifications and requirements. AI can be used for quality inspection in several ways, including visual inspection, material inspection, and dimensional inspection. Visual inspection is the most common type of quality inspection, and AI can be used to automatically identify defects in products. Material inspection is used to identify defects in materials used in products, and AI can be used to automatically identify defects in materials. Dimensional inspection is used to identify defects in products that do not meet dimensional specifications. AI can be used to automatically identify defects in products that do not meet dimensional specifications.A quality inspection project using a visual inspection system may involve analyzing images to identify defects and classifying objects. The system may be used to inspect products during manufacturing or to check for damage during shipping. So, we are going to develop a project that does quality inspection using visual inspection of the product that are formed by casting process there by reducing the use of manpower, increasing the efficiency of the inspection, and speeding up the quality inspection process.

Keywords

Artificial intelligence, Dimensional inspection, Manufacturing, Quality inspection, Visual inspection.

References

  1. Kuric, I.; Klarák, J.; Bulej, V.;Sága, M.; Kandera, M.; Hajduˇcík, A.;Tucki, K.,2022, ''Approach to AutomatedVisual Inspectionof Objects Based on Artificial Intelligence'' , Appl. Sci.,4(3), pp 58-64
  2. Benbarrad, T.; Salhaoui, M.Kenitar, S.B.; Arioua, M.,2021, "Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning", J. Sens. Actuator Netw., 3(2),pp 33-39
  3. Liqun, W.; Jiansheng, W.; Dingjin,2020, "Research on Vehicle Parts Defect Detection Based on Deep Learning",J. Phys. Conf.Ser.4(6),pp 29-30 Wang, J.; Fu, P.; Gao, R.X.,2019, "Machine vision intelligence for product defect inspection based on deep learning and Hough transform", J. Manuf,5(2), pp 51–60
  4. Sahoo, S.K.; Sharma, M.M.; Choudhury, B.B., 2019, "A Dynamic Bottle Inspection Structure. In Computational Intelligence in Data Mining", Springer: Singapore,7(4), pp. 873–884
  5. K. Maksim, B. Kirill, Z. Eduard, G. Nikita, B. Aleksandr, L. Arina, S. Vladislav, M. Daniil, and K. Nikolay, 2019 “Classification of wafer maps defect based on deep learning methods with small amount of data,” IEEE,3(2), pp. 1–5
  6. Jiang, J.L.; Wong, W.K.,2018, "Fundamentals of common computer vision techniques for textile quality control. In Applications of Computer Vision in Fashion and Textiles" ,Woodhead Publishing,8(5),pp. 3–15.
  7. Max K. Ferguson, Ak Ronay, Kincho. H. Law ,2018, "Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning",Smart and sustainable manufacturing systems, 3(2), pp 10-17.
  8. Galan, Orta, P., Kurfess, T., & Ahuett Garza, H., 2018, "Surface defect identification and measurement for metal castings by vision system", Manufacturing Letters 15(10), pp 5–8.
  9. F. Riaz, K Kamal, T Zafar, Q Rizwan, 2017,"An inspection approach for casting defects detection using image segmentation," International Conference on Mechanical, System and Control Engineering (ICMSC),11(8),pp. 101-105.
  10. Sandeep Singh, Lakhwindar Pal Singh & Gurudatt Sahni, 2016, "A REVIEW ON OPTIMIZATION OF GATING SYSTEM IN METAL CASTING", IMPACT: International Journal of Research in Engineering & Technology (IMPACT: IJRET) ISSN,4(6),pp 21-26.
  11. Pastor-López, I., Santos, I., Santamaría-Ibirika, A., Salazar, M., De-La-Peña-Sordo, J., & Bringas, P. G.,2012, "Machine-learning-based surface defect detection and categorisation in high-precision foundry", Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications.24(6),pp 28-29.
  12. H. Tang and Z. Liu,2007, “Research of illumination’s self-adjusting function for on-line checking the surface of spray-paint part", Opto electronic technology, 27(2), pp.135-138.
  13. Domingo Mery, Thomas Jaeger and Dieter Filbert, 2002, "A Review of Methods for Automated Recognition of Casting Defects",6(4),pp 1-12.
  14. Wilson, P.E., Dell, L. D., & Anderson, G. F., 1993, “Root Cause Analysis: A Tool for Total Quality Management”,Milwaukee: ASQC Quality Press,2(3)pp 111 120.