Modeling batch annealing process using data mining techniques for cold rolled steel sheets

Saraee, MH; Moghimi, M; Bagheri, A

Abstract

The annealing process is one of the important operations in production of cold rolled steel sheets, which significantly influences the final product quality of cold rolling mills. In this process, cold rolled coils are heated slowly to a desired temperature and then cooled. Modelling of annealing process (prediction of heating and cooling time and trend prediction of coil core temperature) is a very sophisticated and expensive work. Modelling of annealing process can be done by using of thermal models. In this paper, Modelling of steel annealing process is proposed by using data mining techniques. The main advantages of modelling with data mining techniques are: high speed in data processing, acceptable accuracy in obtained results and simplicity in using of this method. In this paper, after comparison of results of some data mining techniques, feed forward back propagation neural network is applied for annealing process modelling. A good correlation between results of this method and results of thermal models has been obtained.

Citation

Saraee, M., Moghimi, M., & Bagheri, A. (2011, August). Modeling batch annealing process using data mining techniques for cold rolled steel sheets. Presented at The 17th Annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Diego

• Presentation Conference Type: Other
• Conference Name: The 17th Annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• Conference Location: San Diego
• Start Date: Aug 21, 2011
• End Date: Aug 24, 2011
• Publication Date: Aug 21, 2011
• Deposit Date: Oct 28, 2011
• Publicly Available Date: Apr 5, 2016
• Book Title: Proceedings of the First International Workshop on Data Mining for Service and Maintenance - KDD4Service '11
• DOI: https://doi.org/10.1145/2018673.2018677
• Publisher URL: http://dx.doi.org/10.1145/2018673.2018677
• Additional Information: Event Type : Workshop