20.500.12556/DKUM-96921 Machine learning for enhancing manufacturing quality control in ultrasonic nondestructive testing a wavelet neural network and genetic algorithm approach With the rapid development of the global manufacturing industry, an efficient and accurate quality control system has become key to enhancing competitiveness. Ultrasonic Nondestructive Testing (NDT), as an efficient means of quality inspection, plays a crucial role in improving manufacturing quality through the precision of its data analysis. This study aims to explore the application of ultrasonic NDT data in manufacturing quality control by integrating machine learning technologies, with a specific focus on the Wavelet Neural Network optimized by Genetic Algorithms (GA-WNN). This study achieved significant prediction and evaluation results by applying a GA-WNN to quality control in manufacturing. Compared to traditional Wavelet Neural Network (WNN) models, the GA-WNN more effectively identifies and predicts potential quality issues, especially in noisy data and complex production environments, demonstrating higher accuracy and stability. When predicting possible defect types in the manufacturing process, the GA-WNN showed a notable improvement in accuracy over other models. Additionally, in quality stability evaluation, GA-WNN was able to capture production fluctuations more accurately, providing more valuable results for decision-making. The methodologies and discoveries of this study offer new perspectives and tools for quality control in manufacturing and the analysis of ultrasonic NDT data, presenting broad application prospects. ultrasonic nondestructive testing NDT machine learning genetic algorithm GA Wavelet Neural Network WNN quality prediction quality stability assessment quality control optimization nevronske mreže optimizacija kontrole kvaliteta true false true Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering Angleški jezik Slovenski jezik https://apem-journal.org/ Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Članek v reviji 2026-02-02 15:00:45 2026-02-02 15:00:45 2026-02-03 03:14:35 0000-00-00 00:00:00 2024 0 0 str. 347-357 no. 3 Vol. 19 Sept. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-04-09 2024-09-29 2024-10-31 658.5 266961667 10.14743/apem2024.3.511 1854-6250 APEM19-3_347-357.pdf APEM19-3_347-357.pdf 1 4DCF7BE8218F775471FFE962032F4885 803475c99df276bfe0132d8bea796d20756cb58c783bf64b8bd032d7b7f9a4e6 0e52b197-0040-11f1-90fc-00155d581219 https://dk.um.si/Dokument.php?lang=slv&id=202989 https://apem-journal.org/Archives/2024/Abstract-APEM19-3_347-357.html 0 12b0e473-0040-11f1-90fc-00155d581219 https://dk.um.si/Dokument.php?lang=slv&id=202990 Fakulteta za strojništvo Advances in production engineering & management 0 0 0