Quality monitoring in manufacturing processes, particularly in welding, plays a pivotal role in ensuring product integrity and consistency. This paper explores the significance of data characteristics in welding quality monitoring, emphasizing the diverse types of data generated, their properties, and their implications for effective monitoring strategies. By analyzing various data sources, including sensor data, image data, and historical records, this paper aims to provide insights into the challenges and opportunities associated with leveraging these data types for enhancing welding process quality. Additionally, it discusses the role of advanced analytics techniques, such as machine learning and artificial intelligence, in harnessing the potential of these data for real-time monitoring and predictive maintenance. Through a comprehensive understanding of data characteristics, manufacturers can optimize their welding processes, minimize defects, and improve overall product quality.
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Journal of Biometrics & Biostatistics received 3496 citations as per Google Scholar report
