Tools and Process of Defect Detection in Automated Manufacturing Systems

Hussein N. Al-Jubori; Izzat Al-Darraji

doi:10.4108/eetsis.4000

Authors

Hussein N. Al-Jubori University of Baghdad
Izzat Al-Darraji University of Baghdad

DOI:

https://doi.org/10.4108/eetsis.4000

Keywords:

Automated Defect detection, Artificial Intelligence, Machine Vision, Sensors, Manufacturing Systems

Abstract

INTRODUCTION: A range of tools and technologies are at disposal for the purpose of defect detection. These include but are not limited to sensors, Statistical Process Control (SPC) software, Artificial Intelligence (AI) and machine learning (ML) algorithms, X-ray systems, ultrasound systems, and eddy current systems.

OBJECTIVES: The determination of the suitable instrument or combination of instruments is contingent upon the precise production procedure and the category of flaw being identified. In certain cases, defects may necessitate real-time monitoring and analysis through the use of sensors and SPC software, whereas more comprehensive analysis may be required for other defects through the utilization of X-ray or ultrasound systems.

METHODS: The utilization of AI and ML algorithms has gained significant traction in the realm of defect detection. This is attributed to their ability to process vast amounts of data and discern patterns that may have otherwise eluded detection. The aforementioned tools have the capability to anticipate potential flaws and implement pre-emptive measures to avert their occurrence.

RESULTS: The detection of defects in automated manufacturing systems is a continuous process that necessitates meticulous observation and examination to guarantee prompt and effective identification and resolution of defects. CONCLUSION: The utilization of suitable tools and technologies is imperative for manufacturers to guarantee optimal production quality and operational success.

References

Yirui Wu, Hao Cao, Guoqiang Yang, Tong Lu, and Shaohua Wan. 2022. Digital Twin of Intelligent Small Surface Defect Detection with Cyber-Manufacturing Systems. ACM Trans. Internet Technol. Just Accepted (November 2022). https://doi.org/10.1145/3571734.

AlBahar, Areej & Kim, Inyoung & Yue, Xiaowei. (2021). A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems.

Costa, M.J.R., Gouveia, R.M., Silva, F.J.G. et al. How to solve quality problems by advanced fully-automated manufacturing systems. Int J Adv Manuf Technol 94, 3041–3063 (2018). https://doi.org/10.1007/s00170-017-0158-8.

ao Zhang, Biyun Ding, Xin Zhao, Ganjun Liu, Zhibo Pang, LearningADD: Machine learning based acoustic defect detection in factory automation, Journal of Manufacturing Systems, Volume 60, 2021, Pages 48-58, ISSN 0278-6125, https://doi.org/10.1016/j.jmsy.2021.04.005.

E. Deutschl, C. Gasser, A. Niel and J. Werschonig, "Defect detection on rail surfaces by a vision based system," IEEE Intelligent Vehicles Symposium, 2004, Parma, Italy, 2004, pp. 507-511, doi: 10.1109/IVS.2004.1336435.

Roberto Milton Scheffel, Antônio Augusto Fröhlich & Marco Silvestri (2021) Automated fault detection for additive manufacturing using vibration sensors, International Journal of Computer Integrated Manufacturing, 34:5, 500-514, DOI: 10.1080/0951192X.2021.1901316

Masoumeh Aminzadeh, Thomas Kurfess, Automatic thresholding for defect detection by background histogram mode extents, Journal of Manufacturing Systems, Volume 37, Part 1, 2015, Pages 83-92, ISSN 0278-6125, https://doi.org/10.1016/j.jmsy.2015.09.004.

A. Al-Shayea, H. Kaid, A. Al-Ahmari, E. A. Nasr, A. K. Kamrani and H. A. Mahmoud, "Colored Resource-Oriented Petri Nets for Deadlock Control and Reliability Design of Automated Manufacturing Systems," in IEEE Access, vol. 9, pp. 125616-125627, 2021, doi: 10.1109/ACCESS.2021.3111575.

eongsu Lee, Young Chul Lee, Jeong Tae Kim, Fault detection based on one-class deep learning for manufacturing applications limited to an imbalanced database, Journal of Manufacturing Systems, Volume 57, 2020, Pages 357-366, ISSN 0278-6125, https://doi.org/10.1016/j.jmsy.2020.10.013.

F. Hardan and A. . R. J. Almusawi, “Developing an Automated Vision System for Maintaing Social Distancing to Cure the Pandemic”, alkej, vol. 18, no. 1, pp. 38–50, Mar. 2022.

ABBOOD, Wisam T.; ABDULLAH, Oday I.; KHALID, Enas A. A real-time automated sorting of robotic vision system based on the interactive design approach. International Journal on Interactive Design and Manufacturing (IJIDeM), 2020, 14: 201-209.‏

Abbood, W. T., Hussein, H. K., & Abdullah, O. I. (2019). Industrial tracking camera and product vision detection system. Journal of mechanical engineering research and developments, 42(4), 277-280.

Al-Karkhi, N. K., Abbood, W. T., Khalid, E. A., Jameel Al-Tamimi, A. N., Kudhair, A. A., & Abdullah, O. I. (2022). Intelligent Robotic Welding Based on a Computer Vision Technology Approach. Computers, 11(11), 155.

H. B. Matar, S. S. . Al-Zubaidi, and L. A. Al-Kindi, “Evaluation of the Main Causes of Diesel Engine Injector Failure using Fault Tree Analysis ”, alkej, vol. 17, no. 4, pp. 23–35, Dec. 2021.

Najah, F. F. Mustafa, and W. S. Hacham, “Effect of Environmental Factors on the Accuracy of a Quality Inspection System Based on Transfer Learning”, alkej, vol. 17, no. 2, pp. 1–7, Jun. 2021.

M. Q. Ibraheem, “Prediction of Cutting Force in Turning Process by Using Artificial Neural Network”, alkej, vol. 16, no. 2, pp. 34–46, Jun. 2020.

Al-Zubaidi, S., Ghani, J.A. and Haron, C.H.C., 2012. Application of artificial neural networks in prediction tool life of PVD coated carbide when end milling of TI6aL4v alloy. International Journal of Mechanics, 6(3), pp.179-186.