Deep Learning-Based Control Optimization for Glass Bottle Forming

TL;DR

This paper introduces a deep learning control algorithm that optimizes glass bottle forming processes in real-time, improving quality and reducing waste by predicting and adjusting machine parameters based on operational data.

Contribution

It presents a novel deep learning-based control method with an inversion mechanism for real-time process optimization in glass manufacturing.

Findings

Improved process stability and product quality.

Reduced waste and defects in production.

Effective prediction of machine parameter effects.

Abstract

In glass bottle manufacturing, precise control of forming machines is critical for ensuring quality and minimizing defects. This study presents a deep learning-based control algorithm designed to optimize the forming process in real production environments. Using real operational data from active manufacturing plants, our neural network predicts the effects of parameter changes based on the current production setup. Through a specifically designed inversion mechanism, the algorithm identifies the optimal machine settings required to achieve the desired glass gob characteristics. Experimental results on historical datasets from multiple production lines show that the proposed method yields promising outcomes, suggesting potential for enhanced process stability, reduced waste, and improved product consistency. These results highlight the potential of deep learning to process control in glass manufacturing.