References
- Bengio, Y., 2009. Learning Deep Architectures for AI. Foundations and Trends® in Machine Learning, 2(1), 1-127. DOI: 10.1561/2200000006
- Bensingh, R. J., Machavaram, R., Boopathy, S. R., & Jebaraj, C., 2019. Influence of geometrical and injection molding parameters on optics of a biaspheric lens. Measurement, 134, 359–374. DOI: 10.1016/j.measurement.2018.10.066.
- Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F., 2021. Machine Learning for industrial applications: A comprehensive literature review. Expert Systems with Applications, 175, 114820. DOI: 10.1016/j.eswa.2021.114820.
- Cramer, S., Buschmann, D., & Schmitt, R., 2022. Comparison of Feature Extraction Algorithms for Prediction of Quality Characteristics Injection Molding. Procedia CIRP, 112, 579–584. DOI: 10.1016/j.procir. 2022.09.061.
- Fan, Z., Gao, R. X., Wang, P., & Kazmer, D. O., 2016. Multi-sensor data fusion for improved measurement accuracy in injection molding. In Proceedings of the 2016 IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2016), Taipei, Taiwan, 23–26 May 2016, 1-5. https://ieeexplore.ieee.org/document/7520465.
- Farahani, F., Saeidi, A., & Tolouei-Rad, M., 2022. A data-driven approach for predicting porosity defects in additive manufacturing using ensemble machine learning methods. Journal of Manufacturing Processes, 80, 887–897. DOI: 10.1016/j.jmapro.2022.06.013.
- Gao, X., 2012. On-Line Monitoring of Batch Process with Multiway PCA/ICA. In P. Sanguansat (Ed.), Principal Component Analysis, 239–262), IntechOpen. DOI: 10.5772/38386.
- Ge, Z., & Song, Z., 2008. Online batch process monitoring based on multimodel ICA-PCA method. In Proceedings of the 2008 7th World Congress on Intelligent Control and Automation (WCICA 2008), 260-264. DOI: 10.1109/WCICA.2008.4594430.
- Lin, J., Ren, Z., Wu, Z., Ouyang, Z., & Yang, A., 2024. Fast and Accurate Quality Prediction for Injection Molding: An Improved Broad Learning System Method. IEEE Sensors Journal, 24(11), 18499-18510. DOI: 10.1109/jsen.2023.3346849.
- Mao, G., Wang, Y., Zhao, J., & Bie, X., 2018. Multiway kernel principal component analysis for monitoring large-scale nonlinear batch processes. Computers & Chemical Engineering, 118, 77–90. DOI: 10.1016/j.compchemeng.2018.07.009.
- Nguyen, V., Hoang, T., Nguyen, T., Nguyen, X., Trinh, V., Nguyen, M., Nguyen, V., & Nguyen, C., 2023. An Integrated Method in Product Quality Monitoring of the Injection Moulding Process. Industrial Engineering & Management Systems, 22(4), 503-512. DOI: 10.7232/iems. 2023.22.4.503.
- Parizs, R. D., Lõun, K., Lauer, A., Kadarik, V., & Frochte, J., 2022. Methodology for Establishing a Virtual Metrology System Applied to Injection Molding Processes Using Symbolic Regression. Sensors, 22(7), 2704. DOI: 10.3390/s22072704.
- Ribeiro, B., 2005. Support vector machines for quality monitoring in a plastic injection molding process. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35(3), 401–410. DOI: 10.1109/TSMCC.2004.843228.
- Rønsch, G. Ø., Kulahci, M., & Dybdahl, M., 2021. An investigation of the utilisation of different data sources in manufacturing with application in injection moulding. International Journal of Production Research, 59(16), 4851-4868. DOI: 10.1080/00207543.2021.1893853.
- Selvaraj, S. K., Raj, A., Rishikesh Mahadevan, R., Chadha, U., & Paramasivam, V., 2022. A Review on Machine Learning Models in Injection Molding Machines. Advances in Materials Science and Engineering, 2022, Article ID 1949061, 28 pages. DOI: 10.1155/2022/1949061.
- Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B., & Vandewalle, J., 2002. Least Squares Support Vector Machines. World Scientific, Singapore. DOI: 10.1142/5089.
- Suykens, J. A. K., & Vandewalle, J., 1999. Least Squares Support Vector Machine Classifiers. Neural Processing Letters, 9(3), 293-300. DOI: 10.1023/a:1018628609742.
- Zhao, P., Wu, H., & Huang, D., 2020. Intelligent Injection Molding on Sensing Optimization and Quality-Reliability Evaluation. Advances in Polymer Technology, 2020, 1–9. DOI: 10.1155/2020/7654249.