OPTIMIZING THE SCAR PROCESS THROUGH NONCONVENTIONAL METHODS: APPLYING VIBRODIAGNOSTICS AND GENETIC ALGORITHMS FOR QUALITY IMPROVEMENT IN THE AUTOMOTIVE CHAIN
Keywords:
SCAR, vibrodiagnostics,, genetic algorithms, optimization, supply chain
Abstract
This paper explores the application of unconventional methods to optimize the Supplier Corrective Action Request (SCAR) process. The study proposes an innovative framework that integrates vibrodiagnostics for the early detection of defects at the source and genetic algorithms for optimizing supplier process parameters. Through case study analysis, the paper demonstrates how these technologies can identify root causes of non-conformities that remain undetected by traditional methods, leading to more precise corrective actions and durable quality improvements. The results indicate a significant reduction in the recurrence rate of defects and an increase in the overall efficiency of the supply chain.
References
1. Baron, P., Kočiško, M., Hlavatá, S., & Franas, E. (2022). Vibrodiagnostics as a predictive maintenance tool in the operation of turbo generators of a small hydropower plant. Advances in Mechanical Engineering, 14(5). https://doi.org/10.1177/16878132221101023
2. Crosno, J. L., Dahlstrøm, R., & Manolis, C. (2015). Comply or defy? An empirical investigation of change requests in buyer-supplier relationships. Journal of Business & Industrial Marketing, 30(5), 688-699. https://doi.org/10.1108/jbim-09-2013-0198
3. DiMase, D., Collier, Z. A., Carlson, J., Gray, R., & Linkov, I. (2016). Traceability and risk analysis strategies for addressing counterfeit electronics in supply chains for complex systems. Risk Analysis, 36(10), 1834-1843. https://doi.org/10.1111/risa.12536
4. Grasso, M., Laguzza, V., Semeraro, Q., & Colosimo, B. M. (2016). In-process monitoring of selective laser melting: Spatial detection of defects via image data analysis. Journal of Manufacturing Science and Engineering, 139(5). https://doi.org/10.1115/1.4034715
5. Hanchevici, A., & Guţă, S. (2015). Parameters optimization of PID controllers for leather manufacturing process by using genetic algorithms. Leather and Footwear Journal, 15(4), 229-238. https://doi.org/10.24264/lfj.15.4.2
6. Huo, X. (2023). Development of a real-time printed circuit board (PCB) visual inspection system using you only look once (YOLO) and fuzzy logic algorithms. Journal of Intelligent & Fuzzy Systems, 45(3), 4139-4145. https://doi.org/10.3233/jifs-223773
7. Huo, Z., Zhang, Y., Francq, P., Shu, L., & Huang, J. (2017). Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures. IEEE Access, 5, 24063-24076. https://doi.org/10.1109/access.2017.2661967
8. Kim, J., Zhang, Z., Won, D., Yoon, S. W., & Jin, Y. (2024). A pick-and-place process control based on the bootstrapping method for quality enhancement in surface mount technology. The International Journal of Advanced Manufacturing Technology, 133(1-2), 745-763. https://doi.org/10.1007/s00170-024-13767-6
9. Kumar, A., & Kumar, S. (2020). Evolutionary computation for solving search-based data analytics problems. Artificial Intelligence Review, 54(4), 2761-2787. https://doi.org/10.1007/s10462-020-09882-x
10. Li, T., Li, Y., Yanling, Q., & Zeng, S. (2016). Optimization for manufacturing process based on timed Petri net and genetic algorithm. Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology. https://doi.org/10.2991/icmmct-16.2016.126
11. Li, Y., Liu, Y., Zhang, H., Xiao, J., Zhao, Y., & Wei-zhou, G. (2010). Study of emergency resource distribution model based on genetic algorithm. The 2010 IEEE International Conference on Information and Automation, 142-147. https://doi.org/10.1109/icinfa.2010.5512352
12. Otieno, S., Kotak, N., & Parmar, C. (2023). Defect detection and classification on hot-rolled steel using machine learning and deep learning algorithms: A comparative analysis. Research Square. https://doi.org/10.21203/rs.3.rs-3281494/v1
13. Zhou, L., Chen, Z., & Chen, S. (2015). An effective detailed operation scheduling in MES based on hybrid genetic algorithm. Journal of Intelligent Manufacturing, 29(1), 135-153. https://doi.org/10.1007/s10845-015-1097-6
14. T&T Online. (2015). Traductoarele pentru vibrodiagnoza transmisiilor mecanice (I). TTonline.ro., de la https://ttonline.ro/articole/traductoarele-pentru-vibrodiagnoza-transmisiilor-mecanice-i
2. Crosno, J. L., Dahlstrøm, R., & Manolis, C. (2015). Comply or defy? An empirical investigation of change requests in buyer-supplier relationships. Journal of Business & Industrial Marketing, 30(5), 688-699. https://doi.org/10.1108/jbim-09-2013-0198
3. DiMase, D., Collier, Z. A., Carlson, J., Gray, R., & Linkov, I. (2016). Traceability and risk analysis strategies for addressing counterfeit electronics in supply chains for complex systems. Risk Analysis, 36(10), 1834-1843. https://doi.org/10.1111/risa.12536
4. Grasso, M., Laguzza, V., Semeraro, Q., & Colosimo, B. M. (2016). In-process monitoring of selective laser melting: Spatial detection of defects via image data analysis. Journal of Manufacturing Science and Engineering, 139(5). https://doi.org/10.1115/1.4034715
5. Hanchevici, A., & Guţă, S. (2015). Parameters optimization of PID controllers for leather manufacturing process by using genetic algorithms. Leather and Footwear Journal, 15(4), 229-238. https://doi.org/10.24264/lfj.15.4.2
6. Huo, X. (2023). Development of a real-time printed circuit board (PCB) visual inspection system using you only look once (YOLO) and fuzzy logic algorithms. Journal of Intelligent & Fuzzy Systems, 45(3), 4139-4145. https://doi.org/10.3233/jifs-223773
7. Huo, Z., Zhang, Y., Francq, P., Shu, L., & Huang, J. (2017). Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures. IEEE Access, 5, 24063-24076. https://doi.org/10.1109/access.2017.2661967
8. Kim, J., Zhang, Z., Won, D., Yoon, S. W., & Jin, Y. (2024). A pick-and-place process control based on the bootstrapping method for quality enhancement in surface mount technology. The International Journal of Advanced Manufacturing Technology, 133(1-2), 745-763. https://doi.org/10.1007/s00170-024-13767-6
9. Kumar, A., & Kumar, S. (2020). Evolutionary computation for solving search-based data analytics problems. Artificial Intelligence Review, 54(4), 2761-2787. https://doi.org/10.1007/s10462-020-09882-x
10. Li, T., Li, Y., Yanling, Q., & Zeng, S. (2016). Optimization for manufacturing process based on timed Petri net and genetic algorithm. Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology. https://doi.org/10.2991/icmmct-16.2016.126
11. Li, Y., Liu, Y., Zhang, H., Xiao, J., Zhao, Y., & Wei-zhou, G. (2010). Study of emergency resource distribution model based on genetic algorithm. The 2010 IEEE International Conference on Information and Automation, 142-147. https://doi.org/10.1109/icinfa.2010.5512352
12. Otieno, S., Kotak, N., & Parmar, C. (2023). Defect detection and classification on hot-rolled steel using machine learning and deep learning algorithms: A comparative analysis. Research Square. https://doi.org/10.21203/rs.3.rs-3281494/v1
13. Zhou, L., Chen, Z., & Chen, S. (2015). An effective detailed operation scheduling in MES based on hybrid genetic algorithm. Journal of Intelligent Manufacturing, 29(1), 135-153. https://doi.org/10.1007/s10845-015-1097-6
14. T&T Online. (2015). Traductoarele pentru vibrodiagnoza transmisiilor mecanice (I). TTonline.ro., de la https://ttonline.ro/articole/traductoarele-pentru-vibrodiagnoza-transmisiilor-mecanice-i
Published
2025-12-31
How to Cite
Covaci, C., Dragomir, M., Dragomir, D., & Titu, M. (2025). OPTIMIZING THE SCAR PROCESS THROUGH NONCONVENTIONAL METHODS: APPLYING VIBRODIAGNOSTICS AND GENETIC ALGORITHMS FOR QUALITY IMPROVEMENT IN THE AUTOMOTIVE CHAIN. Nonconventional Technologies Review, 29(4). Retrieved from http://www.revtn.ro/index.php/revtn/article/view/566
Section
Articles
