用深度学习优化玻璃瓶成型参数，提升质量一致性。
Deep Learning-Based Control Optimization for Glass Bottle Forming
- 基于实测数据训练神经网络，预测参数调整对瓶坯的影响。
- 通过逆向机制自动找到最优机器设置，实现目标瓶坯特征。
- 在多条产线验证，可降低废品率、提高生产稳定性。
在玻璃瓶制造中，成型设备的精确控制对保证质量、减少缺陷至关重要。本文提出一种基于深度学习的控制算法，用于优化实际生产环境中的成型过程。利用来自多个活跃生产线的真实运行数据，神经网络可预测当前生产条件下参数变化对瓶坯特性的影响。通过专门设计的逆向机制，算法能识别出达成目标瓶坯特性的最优机器设置。在多条产线的历史数据集上进行实验，结果表明该方法表现良好，显示出提升工艺稳定性、减少浪费和改善产品一致性的潜力。研究凸显了深度学习在玻璃制造过程控制中的应用前景。
原文摘要 · Abstract (English)
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.
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