Progress and Research Status in Machine Learning for Defect Detection in Laser Cladding Coatings
The rapid development of artificial intelligence technology has led to significant changes and opportunities across various sectors.Machine learning,an important branch of artificial intelligence,can discover laws and patterns from data to make predictions and decisions.Furthermore,it has been widely used in the field of laser cladding in recent years.Laser cladding technology has emerged as a transformative method with numerous advantages,positioning it as a key player in various industrial applications.Its advantages,including high fusion efficiency,optimal material utilization,robust bonding,and extensive design flexibility,render it indispensable for repairing complex surface defects in metal parts.The occurrence of defects during the cladding process can significantly affect the quality and performance of the cladding layer.Ensuring the reliability and repeatability of cladding quality remains a significant challenge in the field of laser cladding technology.In this study,the application of machine learning algorithms in the field of laser cladding defect assessment is explored.A comprehensive and in-depth analysis of common defects and their formation mechanisms in the laser cladding process is provided.The acoustic,optical,and thermal signals generated during the cladding process are summarized,and the corresponding relationships between these signals and the cladding defects are described.Commonly used methods,sensors,and signal characteristics for monitoring the laser cladding process are summarized.Additionally,the classification and features of machine learning algorithms are organized and their use in signal processing is reviewed during the laser cladding process.The classification and characteristics of machine learning algorithms and their applications in laser cladding signal processing are summarized.Machine learning algorithms have been employed in detecting defects in laser cladding,typically by constructing datasets from features extracted from collected signals,the cladding process,and defect characteristics.These algorithms are used to establish relationships between the signals,defects,and the process.However,most current studies on laser cladding monitoring focus on a single pass or a small area of the cladding layer.The use of such small datasets can lead to model overfitting,thereby reducing the accuracy of defect detection.Nevertheless,the application of these algorithms facilitates the introduction of a dynamic feedback control mechanism that optimizes the cladding process and effectively mitigates defects.The convergence of laser cladding and machine learning has emerged as a vibrant area of research,tackling crucial issues and expanding the limits of quality assurance and process optimization.Researchers,both domestically and internationally,have examined pores,cracks,and other defects at various scales through experiments and simulations.However,the mechanisms behind these defects and their impact on the quality of cladding are not yet fully understood.There is a need for more comprehensive methods to study the laser cladding process.Developing a quantitative evaluation system that links the laser cladding process,signal data,and defect quality is a critical challenge in ensuring the reliability of laser cladding quality.Currently,various sensors,including acoustic,optical,and thermal types,are utilized to monitor the laser cladding process.These sensors aid in examining the relationship between the process signals,defects,and quality.However,the limitations in sensor accuracy and the efficiency of defect feature extraction pose challenges in establishing a precise process-signal-defect relationship.The predominant machine learning algorithms used in current research are supervised learning algorithms.However,unsupervised and semi-supervised learning algorithms,which require less data labeling,are drawing attention in the fields of laser melting and cladding process monitoring,demonstrating significant potential.This review emphasizes the current research hotspots and directions for applying machine learning methods in laser cladding.
万方数据
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