Transfer Learning

Definition

Transfer Learning (TL) is a machine learning technique that reuses knowledge learned from one task to improve performance on a related task. By leveraging models pre-trained on large-scale datasets, it enables rapid adaptation to new tasks with minimal data.

Manufacturing Applications

Defect Inspection Model Development

• Pre-trained Model Utilization: Build defect inspection systems using vision models pre-trained on ImageNet
• Addressing Data Scarcity: Implement effective inspection systems even in early production stages with limited defect samples
• New Product Line Deployment: Rapidly adapt models trained on existing product lines to new products

Predictive Maintenance

• Cross-Equipment Knowledge Transfer: Apply failure prediction models from Plant A equipment to similar equipment in Plant B
• Sensor Data Leverage: Learn general time-series patterns, then specialize in detecting anomalies for specific equipment

Process Optimization

• Existing Process Knowledge: Apply optimal parameter learning models from similar processes to explore new process conditions

Key Points

Benefits for Manufacturing Implementation:

• Minimize labeled training data requirements (70-90% reduction)
• Shorten model development time (months → weeks)
• Enable AI application even for low-volume products

Practical Example: In semiconductor wafer inspection, using ImageNet pre-trained models can achieve over 95% detection accuracy with fewer than 1,000 defect images.