Mathematical Modelling of Shear Cutting Process of Grain Oriented Electrical Steels Using Regression Modelling

Yıl 2023, , 724 - 734, 25.08.2023

Nihat Celık , Alaaddin Toktaş

https://doi.org/10.16984/saufenbilder.1183741

Öz

This article proposes a regression model for the shear-cutting process of grain-oriented electrical steel magnetic cores of transformers made from different gages and magnetic properties of steels. In the experimental runs, 3 levels for thickness (230, 270, and 300 µm) and 4 levels for magnetic features of electrical steels are considered. Core steels are supplied as foils and slit to designed lengths in slitting machinery along the rolling direction of coils. The best magnetic features rely on the rolling direction of the coil and the transverse direction of the coil is subject to the shear-cutting process. The result of cutting operations, discontinuities, and degradations in magnetic properties may occur because of deterioration in crystallography and strain gradation on laminated sheets. Shear-cutting process factors have a strong influence on magnetic degradation even the magnitude of the no-load loss of the transformer core. In this study, the mathematical relation between shear cutting factors sheet thickness ST, counts of hits CH, and the response burr length BL is determined using regression modeling. For this purpose, the process parameters of GEORG TBA 400 cut-to-length machinery in use core production is studied. The calculated coefficient of determination is close to almost 1.00 i.e., R2 = 0.9896 which means the factors are sufficient to model the response, and the model is obtained with a good prediction performance. The aim of the present study is building up a useful process control tool for the machinery and raise a discussion alike process in industry.

Anahtar Kelimeler

Shearing, regression, burr, deformation, modelling

Kaynakça

• I. Wadi, R. Balendra, “Using neural networks to model the blanking process,” Journal of Materials Processing Technology, vol. 91, no. 1, pp. 52–65, Jun. 1999.
• P. Baudouin, M. de Wulf, L. Kestens, Y. Houbaert, “The effect of the guillotine clearance on the magnetic properties of electrical steels,” Journal of Magnetism and Magnetic Materials, vol. 256, no. 1–3, pp. 32–40, Jan. 2003.
• A. Peksoz, S. Erdem, N. Derebasi, “Mathematical model for cutting effect on magnetic flux distribution near the cut edge of non-oriented electrical steels,” Computational Materials Science, vol. 43, no. 4, pp. 1066–1068, Oct. 2008.
• E. S. Al-Momani, A. T. Mayyas, I. Rawabdeh, R. Alqudah, “Modeling blanking process using multiple regression analysis and artificial neural networks,” Journal of Materials Engineering and Performance, vol. 21, no. 8, 2012.
• N. A. K. Bashah, N. Muhamad, B. Md Deros, A. Zakaria, S. Ashari, A. Mobin, M. S. M. A. Lazat, “Multi-regression modelling for spring-back effect on automotive body in white stamped parts,” Materials and Design, vol. 46, pp. 175–190, Apr. 2013.
• A. D. Karaoglan, N. Celik, “A new painting process for vessel radiators of transformer: wet-on-wet,” Journal of Applied Statistics, vol. 43, no. 2, pp. 370–386, 2016.
• J. Park, M. Kil, J. Kim, B. Kang, “A Predictive Model of Flexibly-reconfigurable Roll Forming Process using Regression Analysis” Procedia Engineering, pp. 1266–1271, 2017.
• O. Cavusoglu, H. Gürün, “The relationship of burr height and blanking force with clearance in the blanking process of AA5754 aluminium alloy,” Transactions of Famena, vol. 41, no. 1, pp. 55–62, 2017.
• T. Y. Badgujar, V. P. Wani, “Stamping Process Parameter Optimization with Multiple Regression Analysis Approach,” in Materials Today: Proceedings, Elsevier Ltd, 2018, pp. 4498–4507.
• L. Bohdal, R. Patyk, K. Tandecka, S. Gontarz, D. Jackiewicz, “Influence of shear-slitting parameters on workpiece formation, cut edge quality and selected magnetic properties for grain-oriented silicon steel,” Journal of Manufacturing Process, vol. 56, pp. 1007–1026, Aug. 2020.
• T. Zhou, L. He, Z. Zou, F. Du, J. Wu, P. Tian, “Three-dimensional turning force prediction based on hybrid finite element and predictive machining theory considering edge radius and nose radius,” Journal of Manufacturing Process, vol. 58, pp. 1304–1317, 2020.
• S. Neseli, I. Asilturk, L. Celik, “Determining the optimum process parameter for grinding operations using robust process,” Journal of Mechanical Science and Technology, vol. 26, no. 11, pp. 3587–3595, 2012.
• Z. Patonai, R. Kicsiny, G. Géczi, “Multiple linear regressionbased model for the indoor temperature of mobile containers,” Heliyon, vol. 8, no. 12, Dec. 2022.
• M. Hanief, M. F. Wani, M. S. Charoo, “Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis,” Engineering Science and Technology, an International Journal, vol. 20, no. 3, pp. 1220–1226, 2017.
• H. W. Lee, W. T. Kwon, “Determination of the minute range for RSM to select the optimum cutting conditions during turning on CNC lathe,” Journal of Mechanical Science and Technology, vol. 24, no. 8, pp. 1637–1645, 2010.
• D. R. Patel, M. B. Kiran, V. Vakharia, “Modeling and prediction of surface roughness using multiple regressions: A noncontact approach,” Engineering Reports, vol. 2, no. 2, Feb. 2020.