With machine learning and AI, large sensor datasets that had been difficult to interpret becomes clear, as well as the correlative relationships between irregularities, failures, work life and quality. By applying discrimination logic to muliple production lines, efficiency can imporve in multiple processes.
Irregularity and failure detection
Sensors attached to equipment can collect data from stable operations to deteriorating performance and failure, allowing machine learning to discover shifts in operational patterns. Trained models can monitor real-time data, alert before adverse events, or allow feedback control to prevent unexpected downtime.
Work life prediction
Machine learning and AI allows replacement of process tools, consumable supplies, and product parts, at optimal usage intervals, by monitoring the correlation between the life cycle and the use of state.
Analyzing the relationship between production parameters and quality results can lead to the discovery of correlations and indicators of quality deterioration, providing information to manufacturing for quality improvement.
Using AI for quality control and preventive maintenance, including anomaly detection and product life span, can optimize production lines overall. AI can even provide wide-range of support in design and prototyping stages, by picking up knowledge from large amounts of past data, as well as reduce simulation time for searching optimal parameters.