Artificial intelligence (AI) and machine learning (ML) are playing an increasingly critical role in smart manufacturing by optimizing production processes and enhancing product quality. Key applications in smart manufacturing include AI/ML-based process control, yield diagnostics, prediction, and anomaly detection.
Process control enables real-time adjustments, monitoring, and control of production processes, reducing variability and material waste. ML techniques used in process control include deep learning, neural networks, and predictive modeling. These techniques have been shown to improve the quality of the final product while reducing material waste and production costs. Accurate yield diagnostics are necessary for effective process control, and various ML techniques such as data visualization and decision trees are commonly used in addition to standard statistical modeling. These techniques have been used to identify and address root causes of yield loss in various manufacturing processes, leading to improved product quality and reduced costs.
Prediction techniques, such as virtual metrology, estimate quality parameters without direct measurement, reducing production costs mostly based on supervised learning. On the other hand, anomaly detection includes unsupervised learning, outlier detection, and clustering to allow manufacturers to detect and respond to unexpected events in the manufacturing process, minimizing downtime and reducing defects. These techniques have been used to identify and diagnose anomalies in various manufacturing processes, leading to reduced downtime and improved product quality.
In conclusion, AI/ML-based applications in smart manufacturing offer tremendous potential for revolutionizing production processes and enhancing product quality. During the talk, we will provide further details on the ML techniques used in these applications and how they improve production efficiency, enhance product quality, and reduce costs in smart manufacturing.