Building Up Mathematical Modeling Using Spot Welding Parameters and Prediction Weld Nugget by Minitab

Yıl 2021, Cilt: 5 Sayı: 2, 71 - 79, 20.06.2021

İlhan Çekiç Kadir Çavdar

https://doi.org/10.26701/ems.837829

Öz

In serial production, problems are constantly encountered in the selection of welding parameters due to the excess of welding parameters and variations. In order to compensate for these variations, mostly high energy flux is used. In this study, an approach developed in order to estimate weld nugget diameter in determining the welding parameters for sheets with a thickness of 0.6-3 mm is introduced. Minitab statistical program was used to create experimental data and mathematical operations. First of all, 7 source parameters were selected and experimental design (DOE) was carried out for 64 experiments using the ½ partition factorial method in Minitab software. With the experiments, real weld nugget diameters were obtained. These results were transferred to the Minitab software and the mathematical model of the system was established. Weld nugget diameter estimation procedures were carried out using the experimental design (DOE) data. Test and prediction data were transferred to Minitab software, regression graph was drawn and R-Sq and R-Sq (adj) values were calculated. In addition, samples were created with randomly selected data for verification and comparison was made by transferring them to Minitab. According to the results of this study, remarkable accuracy rates have been achieved in the weld nugget diameter estimation with Minitab.

Anahtar Kelimeler

Resistance spot welding, weld nugget prediction, design of experiment, regression analysis, Minitab

Kaynakça

• Yue, X. K., Tong, G. Q., Chen, F. , Ma, X.L., Gao, X. P. (2016). Optimal welding parameters for small-scale resistance spot welding with response surface methodology. Institute of Materials, Minerals and Mining, 22(2):1-7. Doi: 10.1080/13621718.2016.1204799.
• Boersch, I., Füssel, U., Gresch, C., Großmann, C., Hoffmann, B.(2018). A non-destructive method to predict the welding spot diameter by monitoring process parameters. The International Journal of Advanced Manufacturing Technology, 99:1085–1099. Doi: 10.1007/s00170-016-9847-y.
• Kim, K.Y., Ahmed, F.(2018). Semantic weldability prediction with RSW quality dataset and knowledge construction. Advanced Engineering Informatics, 38:41-53. Doi: org/10.1016/j.aei.2018.05.006.
• Sheikhi, M., Valaee, T.M., Fattah-Alhosseini, A., Usefifar, G.R. (2017). Thermal modeling of resistance spot welding and prediction of weld microstructure. Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science, 48(11):5415-5423. Doi: 10.1007/s11661-017-4314-4.
• Duric, A., Klobčar, D., Milčić, D., Marković, B. (2019). Parameter optimisation and failure load prediction of resistance spot welding of aluminium alloy 57547. Materials Science and Engineering, 659: 012042. Doi: 10.1088/1757-899X/659/1/012042.
• Satonaka, S., Kaieda, K., Okamoto, S. (2004). Prediction of tensile-shear strength of spot welds based on fracture modes. Welding in the World: The International Journal of Materials Joining, 48(5-6):39-45. Doi: org/10.1007/BF03266430.
• Hayat, F. (2011). The effects of the welding current on heat input, nugget geometry, and the mechanical and fractural properties of resistance spot welding on Mg/Al dissimilar materials. Materials and Design, 32(4):2476-2484. Doi: org/10.1016/j.matdes.2010.11.015
• Kim, T., Park, H., Rhee, S. (2005). Optimization of welding parameters for resistance spot welding of TRIP steel with response surface methodology. Science and Technology of Welding and Joining, 43(21):4643-4657. Doi: 10.1080/00207540500137365.
• Hiba, K.H., Israa, R.S., Iman, A.Z. (2019). Prediction of spot welding parameters using fuzzy logic controlling. Eastern-European Journal of Enterprise Technologies, 5 (2):101. Doi: 10.15587/1729-4061.2019.172642.
• Pereda, M., Santos, J.I., Galán, J.M., Martín, Ó. (2015). Direct quality prediction in resistance spot welding process: Sensitivity, specificity and predictive accuracy comparative analysis. Science and Technology of Welding and Joining, 20(8):679-685. Doi: org/10.1179/1362171815Y.0000000052.
• Kemda, B. V. F., Barka, N., Jahazi, M., Osmani, D. (2015). Modeling of phase transformation kinetics in resistance spot welding and investigation of effect of post weld heat treatment on weld microstructure. Metals and Materials International. Doi: org/10.1007/s12540-019-00486-x.
• Manladan, S.M. , Yusof, F., Ramesh, S., Fadzil, M., Luo, Z., Ao, S. (2017). A review on resistance spot welding of aluminum alloys. Int J Adv Manuf Technol, 90, 605–634 Doi:10.1007/s00170-016-9225-9
• Url, <https://support.minitab.com/en-us/minitab/18/help-and-how-to/modeling statistics/regression/how-to/fit-regression-model/methods-and-formulas/stepwise/> (Access date: 02.10.2020).
• Url, <https://support.minitab.com/en-us/minitab/18/help-and-how-to/modeling-statistics/using-fitted-models/how-to/predict/methods-and-formulas/methods-and-formulas/> (Access date: 5.11.2020).
• Url, <https://support.minitab.com/en-us/minitab/18/help-and-how-to/modeling statistics/regression/how-to/fitted-line-plot/before-you-start/overview/> (Acces date:15.11.2020).