Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks

Abstract

The use of machine learning algorithms to improve productivity and quality and to maximize efficiency in the steel industry has recently become a major trend. In this paper, we propose an algorithm that automates the setup in the cold-rolling process and maximizes productivity by predicting the roll forces and motor loads with multi-layer perceptron networks in addition to balancing the motor loads to increase production speed. The proposed method first constructs multilayer perceptron models with all available information from the components, the hot-rolling process, and the cold-rolling process. Then, the cold-rolling variables related to the normal part set-up are adjusted to balance the motor loads among the rolling stands. To validate the proposed method, we used a data set with 70,533 instances of 128 types of steels with 78 variables, extracted from the actual manufacturing process. The proposed method was found to be superior to the physical prediction model currently used for setups with regard to the prediction accuracy, motor load balancing, and production speed.

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

“…Carneiro [33], Wang [34] 2021-2023 Prediction of steel properties Zou [35], Feng [36], Liu [37], Wang [38], Qian [39] 2021-2023 Prediction of molten steel composition Wang [40] 2022 Energy efficiency Lee [41] 2021 Motor equipment load Huang [42], Yu [43] 2022-2023 Modeling and prediction of inventory change Zhou [44], Esche [45], Zhu [46], Li [47], Bouaswaig [48],…”

Application of Machine Learning Methods for the Prediction of Roll Force and Torque during Plate Rolling of Micro-alloyed Steel

“…The experimental results show that the prediction accuracy is lower than that of the traditional model. Lee et al [10] discussed the roll force and motor load prediction methods for the cold-rolling process based on multi-layer preceptor model. The prediction errors decreased in the range if 1-2% compared to error rates of 3% for the conventional physical model.…”

“…Rolling is a complex and nonlinear process, thus, mathematical models cannot capture the effect of various process variables and describe the complete hot rolling phenomenon. Nowadays, Artificial Neural Network (ANN) models are extensively used to compensate for the deficiency of the above models and to improve the prediction accuracy [7][8][9][10][11][12].…”

“…These data are extensively used for the prediction of mechanical properties [21][22]24]. Machine learning has also been applied for the prediction of roll force during the cold rolling process [10]. As far as the hot rolling process is concerned, machine learning models are augmented with physical models due to its complex nature [9].…”

Methods of Condition Monitoring and Fault Detection for Electrical Machines

“…This technique is widely applicable to condition monitoring of machine parameters [26]. Network algorithms can cover classification [27], prediction [28], and feature extraction [29]. In addition, artificial neural networks can be a part of supervised, unsupervised, or reinforcement learning [30].…”