Journal of Engineering Research

As industrial production moves towards intelligence, the need for quality control and optimization is urgent. This study employs deep learning technology to deeply analyze industrial process data. Leveraging its precise pattern recognition capabilities, it constructs models to monitor production anomalies, predict quality trends, and optimize processes. By extracting key features from massive datasets, it achieves early warnings of defective products, dynamically adjusts production parameters, effectively improves product quality, reduces costs, and lays a solid foundation for industrial upgrading, enhancing corporate market competitiveness.

Deep learning; Industrial process; Quality control; Process optimization; Intelligent production