How to cite this paper
Saeheaw, T. (2023). Comparison of different supervised machine learning algorithms for bead geometry prediction in GMAW process.Engineering Solid Mechanics, 11(2), 175-190.
Refrences
Abbasi, M., & El Hanandeh, A. (2016). Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Management, 56, 13-22.
Apaydın, E. (2004). Introduction to Machine Learning (Adaptive Computation and Machine Learning).
Asante-Okyere, S., Shen, C., Yevenyo Ziggah, Y., Moses Rulegeya, M., & Zhu, X. (2018). Investigating the predictive performance of Gaussian process regression in evaluating reservoir porosity and permeability. Energies, 11(12), 3261.
Chandrasekaran, R. R., Benoit, M. J., Barrett, J. M., & Gerlich, A. P. (2019). Multi-variable statistical models for predicting bead geometry in gas metal arc welding. The International Journal of Advanced Manufacturing Technology, 105(1), 1573-1584.
Dong, H., Cong, M., Liu, Y., Zhang, Y., & Chen, H. (2016, June). Predicting characteristic performance for arc welding process. In 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) (pp. 7-12). IEEE.
Dong, H., Huff, S. A., Cong, M., & Zhang, Y. (2017, July). Backside weld bead shape modeling using support vector machine. In 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER) (pp. 277-282). IEEE.
Dutta, P., & Pratihar, D. K. (2007). Modeling of TIG welding process using conventional regression analysis and neural network-based approaches. Journal of Materials Processing Technology, 184(1-3), 56-68.
Fauzi, E. I., Samad, Z., Jamil, M. C., Nor, N. M., & Boon, G. P. (2018). Parametric modeling of metal inert gas (MIG) welding process using second-order regression model analysis. Journal of Advanced Manufacturing Technology (JAMT), 12(1 (2)), 367-382.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). Overview of supervised learning. In The elements of statistical learning (pp. 9-41). Springer, New York, NY.
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.
Kamble, A. G., & Rao, R. V. (2013). Experimental investigation on the effects of process parameters of GMAW and transient thermal analysis of AISI321 steel. Advances in Manufacturing, 1(4), 362-377.
Kim, J. W., & Na, S. J. (1995). A study on the effect of contract tube-to-workpiece distance on weld pool shape in gas metal arc welding. Welding Journal, 74(5).
Liang, R., Yu, R., Luo, Y., & Zhang, Y. (2019). Machine learning of weld joint penetration from weld pool surface using support vector regression. Journal of Manufacturing Processes, 41, 23-28.
Loh, W. Y. (2011). Classification and regression trees. Wiley interdisciplinary reviews: data mining and knowledge discovery, 1(1), 14-23.
Martinez, R. T., Bestard, G. A., & Alfaro, S. C. A. (2021). Two gas metal arc welding process dataset of arc parameters and input parameters. Data in Brief, 35, 106790.
Mendes-Moreira, J., Soares, C., Jorge, A. M., & Sousa, J. F. D. (2012). Ensemble approaches for regression: A survey. Acm computing surveys (csur), 45(1), 1-40.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.
Mur, R., Díaz, I., & Rodríguez, M. (2020). Comparative Study of Surrogate Modelling Techniques Applied to Three Different Chemical Processes. In Computer Aided Chemical Engineering (Vol. 48, pp. 145-150). Elsevier.
Richardson, R. R., Osborne, M. A., & Howey, D. A. (2017). Gaussian process regression for forecasting battery state of health. Journal of Power Sources, 357, 209-219.
Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524.
Tafarroj, M. M., & Kolahan, F. (2018). A comparative study on the performance of artificial neural networks and regression models in modeling the heat source model parameters in GTA welding. Fusion Engineering and Design, 131, 111-118.
Tham, G., Yaakub, M. Y., Abas, S. K., Manurung, Y. H., & Jalil, B. A. (2012). Predicting the gmaw 3f t- fillet geometry and its welding parameter. Procedia Engineering, 41, 1794-1799.
Tripepi, G., Jager, K. J., Dekker, F. W., & Zoccali, C. (2008). Linear and logistic regression analysis. Kidney international, 73(7), 806-810.
Wolpert, D. H. (1996). The lack of a priori distinctions between learning algorithms. Neural computation, 8(7), 1341-1390.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82.
Wu, S., Gao, H., Zhang, W., & Zhang, Y. M. (2017). Analytic weld pool model calibrated by measurements part 1: Principles. Weld. J, 96(6), 193s-202s.
Yang, L. J., Bibby, M. J., & Chandel, R. S. (1993). Linear regression equations for modeling the submerged-arc welding process. Journal of Materials Processing Technology, 39(1-2), 33-42.
Apaydın, E. (2004). Introduction to Machine Learning (Adaptive Computation and Machine Learning).
Asante-Okyere, S., Shen, C., Yevenyo Ziggah, Y., Moses Rulegeya, M., & Zhu, X. (2018). Investigating the predictive performance of Gaussian process regression in evaluating reservoir porosity and permeability. Energies, 11(12), 3261.
Chandrasekaran, R. R., Benoit, M. J., Barrett, J. M., & Gerlich, A. P. (2019). Multi-variable statistical models for predicting bead geometry in gas metal arc welding. The International Journal of Advanced Manufacturing Technology, 105(1), 1573-1584.
Dong, H., Cong, M., Liu, Y., Zhang, Y., & Chen, H. (2016, June). Predicting characteristic performance for arc welding process. In 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) (pp. 7-12). IEEE.
Dong, H., Huff, S. A., Cong, M., & Zhang, Y. (2017, July). Backside weld bead shape modeling using support vector machine. In 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER) (pp. 277-282). IEEE.
Dutta, P., & Pratihar, D. K. (2007). Modeling of TIG welding process using conventional regression analysis and neural network-based approaches. Journal of Materials Processing Technology, 184(1-3), 56-68.
Fauzi, E. I., Samad, Z., Jamil, M. C., Nor, N. M., & Boon, G. P. (2018). Parametric modeling of metal inert gas (MIG) welding process using second-order regression model analysis. Journal of Advanced Manufacturing Technology (JAMT), 12(1 (2)), 367-382.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). Overview of supervised learning. In The elements of statistical learning (pp. 9-41). Springer, New York, NY.
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.
Kamble, A. G., & Rao, R. V. (2013). Experimental investigation on the effects of process parameters of GMAW and transient thermal analysis of AISI321 steel. Advances in Manufacturing, 1(4), 362-377.
Kim, J. W., & Na, S. J. (1995). A study on the effect of contract tube-to-workpiece distance on weld pool shape in gas metal arc welding. Welding Journal, 74(5).
Liang, R., Yu, R., Luo, Y., & Zhang, Y. (2019). Machine learning of weld joint penetration from weld pool surface using support vector regression. Journal of Manufacturing Processes, 41, 23-28.
Loh, W. Y. (2011). Classification and regression trees. Wiley interdisciplinary reviews: data mining and knowledge discovery, 1(1), 14-23.
Martinez, R. T., Bestard, G. A., & Alfaro, S. C. A. (2021). Two gas metal arc welding process dataset of arc parameters and input parameters. Data in Brief, 35, 106790.
Mendes-Moreira, J., Soares, C., Jorge, A. M., & Sousa, J. F. D. (2012). Ensemble approaches for regression: A survey. Acm computing surveys (csur), 45(1), 1-40.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.
Mur, R., Díaz, I., & Rodríguez, M. (2020). Comparative Study of Surrogate Modelling Techniques Applied to Three Different Chemical Processes. In Computer Aided Chemical Engineering (Vol. 48, pp. 145-150). Elsevier.
Richardson, R. R., Osborne, M. A., & Howey, D. A. (2017). Gaussian process regression for forecasting battery state of health. Journal of Power Sources, 357, 209-219.
Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524.
Tafarroj, M. M., & Kolahan, F. (2018). A comparative study on the performance of artificial neural networks and regression models in modeling the heat source model parameters in GTA welding. Fusion Engineering and Design, 131, 111-118.
Tham, G., Yaakub, M. Y., Abas, S. K., Manurung, Y. H., & Jalil, B. A. (2012). Predicting the gmaw 3f t- fillet geometry and its welding parameter. Procedia Engineering, 41, 1794-1799.
Tripepi, G., Jager, K. J., Dekker, F. W., & Zoccali, C. (2008). Linear and logistic regression analysis. Kidney international, 73(7), 806-810.
Wolpert, D. H. (1996). The lack of a priori distinctions between learning algorithms. Neural computation, 8(7), 1341-1390.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82.
Wu, S., Gao, H., Zhang, W., & Zhang, Y. M. (2017). Analytic weld pool model calibrated by measurements part 1: Principles. Weld. J, 96(6), 193s-202s.
Yang, L. J., Bibby, M. J., & Chandel, R. S. (1993). Linear regression equations for modeling the submerged-arc welding process. Journal of Materials Processing Technology, 39(1-2), 33-42.