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.
Highlights
This study provides a novel procedure that predicts the roll forces and motor loads in the cold-rolling process based on neural networks and balances the motor loads and maximizes the production speed
If we consider the entire process of the proposed method, the time complexity of the test phase becomes O( M(W 0 + b)i ) where W 0 is the total number of parameters in the roll force and motor load prediction models, b is the maximum number of iterations in the balancing procedure, and i is the maximum number of repetitions of the entire proposed method
We compared the results of the roll force and motor load prediction models to those of the conventional physical models currently used by a steel manufacturer in South
Summary
Predictive machine learning models have been applied to several industrial problems, such as error detection and diagnosis [29,30,31], quality manufacturing [32,33,34], process monitoring [4,35], and manufacturing automation [36,37] These machine learning models, which include regression methods, decision trees, support vector machines, and artificial. The models suffered from lack of data, and artificial instances were created with finite element methods to train the neural networks These studies demonstrate that machine learning techniques, such as neural network models, can be applied to various industrial fields to optimize the process efficiency and improve the quality of products
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