Advances in Production Engineering & Management
Volume 19 | Number 3 | September 2024 | pp 347–357
https://doi.org/10.14743/apem2024.3.511
Machine learning for enhancing manufacturing quality control in ultrasonic nondestructive testing: A Wavelet Neural Network and Genetic Algorithm approach
Song, W.T.; Huo, L.
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A B S T R A C T
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.
A R T I C L E I N F O
Keywords • Ultrasonic nondestructive testing (NDT); Machine learning; Genetic Algorithm (GA); Wavelet Neural Network (WNN); Quality prediction; Quality stability assessment; Quality control optimization
Corresponding author • Huo, L.
Article history • Received 9 April 2024, Revised 22 September 2024, Accepted 29 September 2024
Published on-line • 31 October 2024
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