Data mining model of continuous casting cutting machinery

Donglin Wang; Feifei Xing; Xiangming Kong; Jun Ji

doi:10.1117/12.2686398

28 July 2023 Data mining model of continuous casting cutting machinery

Donglin Wang, Feifei Xing, Xiangming Kong, Jun Ji

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127563M (2023) https://doi.org/10.1117/12.2686398
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

Continuous casting and cutting machinery and equipment play an important role in industrial production. During continuous casting cutting, how to meet the actual operation requirements with the minimum loss and achieve the customer's target value as much as possible is the central element of cutting scheme design. This paper uses data mining model to guide the reuse of cutting equipment. Set the decision variable as the cutting length of each section of billet, Z as the objective function, combined with the basic requirements and normal requirements, and the specific model is as follows:Z1 = min Σ(l + |l2 − 8.5| + |l2 − 8.5|+. . . |lk − 8.5|) . st 8≤ li ≤9 ;l +l1 +l2 +...+lk =L ;4.8≤ l < 8.The above model is realized by matlab programming, and the cutting scheme is obtained by inputting different tail blank length values. On this basis, the cutting length and loss are adjusted manually according to the actual situation, and the optimal scheme is obtained. Here, the minimum loss section should be 4.8 meters, while the cutting frequency is 4 minutes/time, and the casting speed is 1 meter/minute, so the cutting frequency always meets the requirements of the scheme.

Citation Download Citation

Donglin Wang, Feifei Xing, Xiangming Kong, and Jun Ji "Data mining model of continuous casting cutting machinery", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127563M (28 July 2023); https://doi.org/10.1117/12.2686398

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