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Computational intelligence-based process optimization for tandem cold rolling
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DOI：10.1081/AMP-200053535.png)
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摘要
En 中文
The requirements for the manufacturers of steel have been increased in many respects in recent years. Harsh competition among manufacturers demands a continuous reduction of production costs and improvement of product quality. The work presented herein describes maximizing rolling mill throughput and minimizing processing costs and crop losses by computational intelligence-based process modeling and optimization. In this article, an intelligent searching mechanism is introduced to optimize the rolling schedule by assessing rolling constraints and the combined cost function of tension, shape, and power distribution. The optimization results have been compared with current rolling practices based on empirical models. It is shown that the proposed model can significantly reduce the power distribution cost, maximize the safe level of strip tension, and obtain good strip shape. The proposed model is generic for complex engineering problem optimization, and is capable of multiple-objective problem solving.
Keyword:
computational intelligence
constraints
cost function
engineering problem
genetic algorithms
heuristic optimization
intelligent system
manufacturing process
materials processing
multiple-objective problem solving
process modeling
process optimization
rolling schedule
shape
tandem cold rolling
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