製程目標之數位孿生最佳化初探

Abstract

本研究以數位孿生為基礎結合模擬最佳化模型，利用實際系統所收集之資料建立一虛擬軋機。由於影響軋延製程因子過於複雜，無法以物理或數學模型所建構，因此本研究以機器學習模型模擬軋延製程之方式。運用模擬系統之結果，結合模擬最佳化模型計算最佳參數。 目前軋延製程之目標板形設定多由經驗法則所訂定，尚未有一系統性的方式設定。而且，目前製程之控制參數變動幅度較大，與最適控制參數差距變化大，使油壓輥作動幅度大。為能夠以數值方式解決上述問題，本研究提供一結構化的模型計算最佳目標設定參數。以虛擬軋機結合模擬最佳化之模型，模擬不同目標設定參數計算目標板形後，使得最佳控制參數與最適控制參數差距最小，以提升整體製程品質。 本研究將整體架構分為四個模組，首先，以一目標設定參數計算目標板形，模組一建立XGBoost模型預測在不同目標時其出口板形分位數，了解冷軋機在目標板形與出口板形之關係。模組二接續模組一之輸出，利用結合LASSO與Random Forest之模型，透過前一道次出口板形之分位數預測下一道次入口板形之分位數，精準地預測出口和入口關係。模組三接續模組二之輸出，以代表性入口板形和其他機台參數輸入至虛擬軋機，此虛擬軋機為XGBoost模型建立，可以精準地模擬軋延結果，結合模擬最佳化模型，以Cross-Entropy演算法計算在軋延限制下最佳之控制參數，使得出口板形貼近目標板形。模組四結合模組一至模組三，建立一模擬最佳化模型，以Cross-Entropy演算法最佳化目標設定參數，透過模組三所計算之最佳控制參數與最適之控制參數差距，不斷地調整目標設定參數，放入模組一繼續後續實驗，直到找到最佳目標設定參數。利用本研究之四個模組能夠最佳化控制方法，改善整體製程品質並實現智慧製造。

This research combines the concept of Digital Twin and Simulation Optimization. We build a virtual rolling mill system with data collected from an actual system. Due to the complexity of factors affecting the rolling process, it is too hard to simulate a virtual rolling machine by a physical or a mathematical model. Therefore, we establish a machine learning model to simulate the process of rolling mill in this research. The predicted results from virtual rolling machine are combined with the simulation optimization model to calculate the optimal control parameters. Currently, the target shape setting in the rolling process is mostly based on empirical rules, and there is no systematic way to set it. In addition, the difference between control parameters in the process and suitable control parameters is significant, leading to large fluctuations in the rolling process. To solve these problems above, we establish a structured model to calculate the optimal target setting. The study divides the overall structure into four modules. The first module calculates the target shape using target setting parameters. After simulating target shape, we build XGBoost model to predict exit shapezone quantile with different target shapes. The second module predicts the entry shapezone quantile based on the output of the first module using a model that combines LASSO and Random Forest model. The third module simulates the rolling process using a virtual rolling mill constructed by an XGBoost model and combines a simulation optimization model to calculate the optimal control parameters. The fourth module combines the three modules above to establish a simulation optimization model and optimize the target setting parameters using the Cross-Entropy algorithm until the optimal target setting parameters are found. By using these four modules, this study optimizes the target setting method, improves the process quality and realizes smart manufacturing.