Ahmed, M. M. Z., Touileb, K., El-Sayed Seleman, M. M., Albaijan, I., & Habba, M. I. A. (2022). Bobbin tool friction stir welding of aluminum: parameters optimization using taguchi experimental design. Materials, 15(8), 2771. https://doi.org/10.3390/ma15082771
Ali, S., Hassan, M., Kim, J. Y., Farid, M. I., Sanaullah, M., & Mufti, H. (2022). FF-PCA-LDA: intelligent feature fusion based PCA-LDA classification system for plant leaf diseases. Applied Sciences, 12(7), 3514. https://doi.org/10.3390/app12073514
Anand, K., Barik, B. K., Tamilmannan, K., & Sathiya, P. (2015). Artificial neural network modeling studies to predict the friction welding process parameters of Incoloy 800H joints. Engineering Science and Technology, an International Journal, 18(3), 394–407.
Antony, J. (2000). Multi‐response optimization in industrial experiments using Taguchi’s quality loss function and principal component analysis. Quality and Reliability Engineering International, 16(1), 3–8.
Babanova, S., Artyushkova, K., Ulyanova, Y., Singhal, S., & Atanassov, P. (2014). Design of experiments and principal component analysis as approaches for enhancing performance of gas-diffusional air-breathing bilirubin oxidase cathode. Journal of Power Sources, 245, 389–397. https://doi.org/10.1016/j.jpowsour.2013.06.031
Baraya, M., El-Asfoury, M. S., Fadel, O. O., & Abass, A. (2024). Experimental Analyses and Predictive Modelling of Ultrasonic Welding Parameters for Enhancing Smart Textile Fabrication. Sensors, 24(5), 1488. https://doi.org/10.3390/s24051488
Boriwal, L., Sarviya, R. M., & Mahapatra, M. M. (2021). Process analysis and regression modelling of resistance spot welded joints of austenitic stainless steel 304L and low carbon steel sheets by using surface response methodology. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 235(1), 24–33. https://doi.org/10.1177/0954408920940888
Breitenbach, J., Dauser, T., Illenberger, H., Traub, M., & Buettner, R. (2021). A Systematic Literature Review on Machine Learning Approaches for Quality Monitoring and Control Systems for Welding Processes. 2021 IEEE International Conference on Big Data (Big Data), 2019–2025.
Camarena-Martinez, R., Baeza-Serrato, R., & Lizarraga-Morales, R. A. (2023). Optimization of Welding Process of Geomembranes in Biodigesters Using Design of Factorial Experiments. Energies, 16(18), 6583. https://doi.org/10.3390/en16186583
Choudhary, D. K. C. D., Pandey, R. M., Prakash, C., & Kumar, V. (2024). Mathematical Modeling of Shape relationship including% Dilution and Total Bead Volume in SAW Process by using Two Level Half Factorial Technique. Journal of Manufacturing Engineering, 19(4), 108–125. https://doi.org/10.37255/jme.v19i4pp108-125
Das, S., AnanthaKrishna, K. V, Rajkumar, J. V, Kumar, B., Arora, U. K., & Tewari, R. (2025). A Study on the Effects of Welding Parameters on Weld Bead Geometry in TIG Welding Process of Zircaloy Fuel Pins Using Response Surface Methodology. Transactions of the Indian Institute of Metals, 78(2), 40. https://doi.org/10.1007/s12666-024-03484-9
Guo, H., Lin, L., Lv, Y., Liu, J., & Tong, C. (2021). Machine Learning for Determining Key Parameters in Welding Process of Underground Engineering Equipment. 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), 33–41. https://doi.org/10.1109/SDPC52933.2021.9563365
Haribalaji, V., Boopathi, S., & Asif, M. M. (2022). Optimization of friction stir welding process to join dissimilar AA2014 and AA7075 aluminum alloys. Materials Today: Proceedings, 50, 2227–2234. https://doi.org/10.1016/j.matpr.2021.09.499
Hayati, R., Munawar, A. A., Lukitaningsih, E., Earlia, N., Karma, T., & Idroes, R. (2024). Combination of PCA with LDA and SVM classifiers: A model for determining the geographical origin of coconut in the coastal plantation, Aceh Province, Indonesia. Case Studies in Chemical and Environmental Engineering, 9, 100552. https://doi.org/10.1016/j.cscee.2023.100552
He, Y., Yang, K., Wang, X., Huang, H., & Chen, J. (2022). Quality prediction and parameter optimisation of resistance spot welding using machine learning. Applied Sciences, 12(19), 9625. https://doi.org/10.3390/app12199625
Jagtap, K. R., Rojekar, M. S., Dravid, S. V, & Deshpande, A. R. (2017). Effect of welding parameters on tensile & yield strength of is 2062 grade steel using design of experiment approach. Materials Today: Proceedings, 4(8), 7875–7883. https://doi.org/10.1016/j.matpr.2017.07.123
Khoshnevisan, L., Hourfar, F., Alhameli, F., & Elkamel, A. (2021). Combining design of experiments, machine learning, and principal component analysis for predicting energy consumption and product quality of a natural gas processing plant. International Journal of Energy Research, 45(4), 5974–5987. https://doi.org/10.1002/er.6217
Kumar, R. R., Singh, A., Kumar, A., Ansu, A. kumar, Kumar, A., Kumar, S., Kumar, D., Goyal, A., Oza, A. D., & Singh, D. (2023). Enhancement of friction stir welding characteristics of alloy AA6061 by design of experiment methodology. International Journal on Interactive Design and Manufacturing (IJIDeM), 17(5), 2659–2671. https://doi.org/10.1007/s12008-022-01106-6
Kumar, S., Gaur, V., & Wu, C. (2022). Machine learning for intelligent welding and manufacturing systems: research progress and perspective review. The International Journal of Advanced Manufacturing Technology, 123(11), 3737–3765. https://doi.org/10.1007/s00170-022-10403-z
Kuo, C.-C., Tsai, Q.-Z., Li, D.-Y., Lin, Y.-X., & Chen, W.-X. (2022). Optimization of Ultrasonic Welding Process Parameters to Enhance Weld Strength of 3C Power Cases Using a Design of Experiments Approach. Polymers, 14(12), 2388. https://doi.org/10.3390/polym14122388
Lee, W., Jung, S., Noh, H.-S., & Park, M. (2010). Reliable feature extraction using linear discriminant analysis. SCIS & ISIS SCIS & ISIS 2010, 1233–1236.
Li, J., Zhao, B., & Zhang, H. (2009). Face recognition based on PCA and LDA combination feature extraction. 2009 First International Conference on Information Science and Engineering, 1240–1243. https://doi.org/10.1109/ICISE.2009.581
Madrid, J., Lorin, S., Söderberg, R., Hammersberg, P., Wärmefjord, K., & Lööf, J. (2019). A virtual design of experiments method to evaluate the effect of design and welding parameters on weld quality in aerospace applications. Aerospace, 6(6), 74. https://doi.org/10.3390/aerospace6060074
Mahadevan, R., Jagan, A., Pavithran, L., Shrivastava, A., & Selvaraj, S. K. (2021). Intelligent welding by using machine learning techniques. Materials Today: Proceedings, 46, 7402–7410. https://doi.org/10.1016/j.matpr.2020.12.1149
Massey Jr, F. J. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253), 68–78.
Meng, Y., Rajagopal, M., Kuntumalla, G., Toro, R., Zhao, H., Chang, H. C., Sundar, S., Salapaka, S., Miljkovic, N., & Ferreira, P. (2020). Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology. Mathematical Biosciences and Engineering, 17(6). https://doi.org/10.3934/mbe.2020379
Montgomery, D. C. (2017). Design and analysis of experiments. John wiley & sons.
Mounika, G., Rajyalakshmi, K., Rajkumar, G. V. S., & Sravani, D. (2024). Prediction and optimization of process parameters using design of experiments and fuzzy logic. International Journal on Interactive Design and Manufacturing (IJIDeM), 18(4), 2333–2343. https://doi.org/10.1007/s12008-023-01446-x
Myftarago, A., Bier, T. A., Qoku, E., Aliti, R., & Zogaj, M. (2023). Multi-Response Optimization on Hydrated Calcium Aluminate Rich Ternary Binders Using Taguchi Design of Experiments and Principal Component Analysis. Buildings, 13(10), 2494. https://doi.org/10.3390/buildings13102494
Ramasamy, S. R., Gould, J., & Workman, D. (2002). Design-of-experiments study to examine the effect of polarity on stud welding. WELDING JOURNAL-NEW YORK-, 81(2), 19-S.
Rathi, A. K. (2021). To study the effect of submerged arc welding parameters on bead geometry and hardness for mild steel (IS-2062A) using fractional factorial design. Materials Today: Proceedings, 34, 525–530.
Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Kaluri, R., Rajput, D. S., Srivastava, G., & Baker, T. (2020). Analysis of dimensionality reduction techniques on big data. Ieee Access, 8, 54776–54788. https://doi.org/10.1109/ACCESS.2020.2980942
Richmire, S., Hall, K., & Haghshenas, M. (2018). Design of experiment study on hardness variations in friction stir welding of AM60 Mg alloy. Journal of Magnesium and Alloys, 6(3), 215–228. https://doi.org/10.1016/j.jma.2018.07.002
Sabegh, M. H. Z., Mirzazadeh, A., Salehian, S., & Weber, G.-W. (2014). A literature review on the fuzzy control chart; classifications & analysis. International Journal of Supply and Operations Management, 1(2), 167.
Sharma, A., Paliwal, K. K., & Onwubolu, G. C. (2006). Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification. Pattern Recognition, 39(7), 1215–1229. https://doi.org/10.1016/j.patcog.2006.02.001
Skrobot, V. L., Castro, E. V. R., Pereira, R. C. C., Pasa, V. M. D., & Fortes, I. C. P. (2007). Use of principal component analysis (PCA) and linear discriminant analysis (LDA) in gas chromatographic (GC) data in the investigation of gasoline adulteration. Energy & Fuels, 21(6), 3394–3400. https://doi.org/10.1021/ef0701337
Soleimanipour, A., Chegini, G. R., & Massah, J. (2018). Classification of Anthurium flowers using combination of PCA, LDA and support vector machine. Agricultural Engineering International: CIGR Journal, 20(1).
Tesfaw, T., Singh, A. P., Gezahegn, A. M., & Garedew, B. T. (2022). Optimization of MAG welding process parameters using Taguchi design method on dead mild steel used in automotive industry. International Journal of System Assurance Engineering and Management, 13(1), 79–89. https://doi.org/10.1007/s13198-021-01107-w
Zhang, H., Bai, X., Dong, H., & Zhang, H. (2024). Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning. Metals, 14(5), 567. https://doi.org/10.3390/met14050567
Zhang, Y., & Edgar, T. F. (2008). PCA combined model-based design of experiments (DOE) criteria for differential and algebraic system parameter estimation. Industrial & Engineering Chemistry Research, 47(20), 7772–7783. https://doi.org/10.1021/ie071206c
