Details
Title
From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep LearningJournal title
Archives of Foundry EngineeringYearbook
2025Volume
vol. 25Issue
No 2Authors
Affiliation
Burzyńska, A. : University of Warmia and Mazury in Olsztyn, PolandKeywords
Computer vision in foundry ; Quality 4.0. ; CNN for Tabular Data ; Industry 4.0Divisions of PAS
Nauki TechniczneCoverage
126-136Publisher
The Katowice Branch of the Polish Academy of SciencesBibliography
- Mohan, T.R., Roselyn, J.P., Uthra, R.A. (2022). Digital smart kaizen to improve quality rate through total productive maintenance implemented industry 4.0. In 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT), 7-9 October 2022 (pp. 1-6). Bangalore, India. DOI: 10.1109/GCAT55367.2022.9971890.
- Hsiao, H., Hung, M., Chen, Ch., Lin, Y. (2022). Cloud computing, internet of things (IoT), edge computing, and big data infrastructure. In Fan-Tien Cheng (Eds.), Industry 4.1: Intelligent Manufacturing with Zero Defects (pp.129-167). DOI: 10.1002/9781119739920.ch4.
- Aleksandrova, S.V., Vasiliev, V.A., Alexandrov, M.N. (2019). Integration of quality management and digital technologies. In 2019 International Conference Quality Management, Transport and Information Security, Technologies (IT&QM&IS), 23-27 September 2019 (pp. 20-22). Sochi, Russia. DOI: 10.1109/ITQMIS.2019.8928426.
- Shen, L., Wang, F., Zhou, Z., Xu, R. (2024). Research on the difficulty mining algorithm for the integration of multiple sets of data platforms based on big data analysis in smart factories. In 2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), 27-29 February 2024 (pp. 1444-1448). Changchun, China. DOI: 10.1109/EEBDA60612.2024.10485802.
- Li, D., Chen, X., Tan, P., Jia, J. (2024). How deep learning is implemented in manufacturing quality control. In 2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT), 20-22 September 2024 (pp. 1-4). Yichang, China. DOI: 10.1109/AICIT62434.2024.10729923.
- Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M. & Parmar, M. (2024). A review of convolutional neural networks in computer vision. Artificial Intelligence Review. 57(4), 99. DOI: 10.1007/s10462-024-10721-6.
- Islam, R., Zamil, Z.H., Rayed, E. & Kabir, M. (2024). Deep learning and computer vision techniques for enhanced quality control in manufacturing processes. IEEE Access. 12, 99, 121449-121479. DOI: 10.1109/ACCESS.2024.3453664.
- Ameri, R., Hsu, Ch., Band, S. (2024). A systematic review of deep learning approaches for surface defect detection in industrial applications. Engineering Applications of Artificial Intelligence. 130, 107717, 1-24. DOI: 10.1016/j.engappai.2023.107717.
- O’Shea K., Nash R. (2015). An introduction to convolutional neural networks. Retrieved December 15, 2024, from https://doi.org/10.48550/arXiv.1511.08458.
- Rahat, A.B. (2024). High-Pressure die casting (HPDC): precission, efficiency and innovation. Retrieved December 13, 2024, from https://www.linkedin.com/pulse/high-pressure-die-casting-hpdc-precision-efficiency-rahat-a-bhatia-vjh2c.
- Kridli, G. T., Friedman, P. A., & Boileau, J. M. (2021). Manufacturing processes for light alloys. In Materials, design and manufacturing for lightweight vehicles (pp. 267-320). Woodhead Publishing.
- Xu, L., Skoularidou, M., Cuesta-Infante, A., & Veeramachaneni, K. (2019). Modeling tabular data using conditional gan. Advances in neural information processing systems, 32.
- Raghu, M. & Ganesh Kumar, D. (2024). A conditional tabular generative adversarial network (CTGAN)-based approach to safeguarding artificially created smart IoT setting. International Innovative Research Journal of Engineering and Technology. 9(4), 1-9. https://doi.org/10.32595/iirjet.org/v9i4.2024.194.
- Topol, E.J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine. 25, 44-56. DOI: 10.1038/s41591-018-0300-7
- Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P.J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Zhang, Y., Flores, G., Duggan, G.E., Irvine, J., Le, Q., Litsch, K., Mossin, A., Tansuwan, J., Wang, D., Wexler, J., Wilson, J., Ludwig, D., Volchenboum, S.L., Chou, K., Pearson, M., Madabushi, S., Shah, N.H., Butte, A.J., Howell, M.D., Cui, C., Corrado, G.S. & Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine. 1(1), 18, 1-10. DOI: 10.1038/s41746-018-0029-1
- Bayat, A. (2002). Science, medicine, and the future: Bioinformatics. BMJ. 324, 1018-1022. DOI: 10.1136/bmj.324.7344.1018
- Zhu, Y., Qiu, P., Ji, Y. (2014). TCGA-Assembler: Open-source software for retrieving and processing TCGA data. Nature methods. 11(6), 599-600. https://doi.org/10.1038/nmeth.2956.
- Zhu, Y., Xu, Y., Helseth Jr, D. L., Gulukota, K., Yang, S., Pesce, L. L., Mitra, R., Muller, P., Sengupta, S., Guo, W., Silverstein, J.C., Foster, I., Parsad, N., White, K.P. & Ji, Y. (2015). A comprehensive depiction of genetic interactions in cancer by integrating TCGA data. Journal of the National Cancer Institute. 107(8), 129, 1-9. DOI: 10.1093/jnci/djv129.
- Taha, M.E., Mahmoud, T.M. & Abd-El-Hafeez, T. (2023). A novel hybrid approach to masked face recognition using robust PCA and GOA optimizer. Scientific Journal for Damietta Faculty of Science. 13(3) 25-35. DOI: 10.21608/SJDFS.2023.222524.1117.
- Elmessery, W. M., Maklakov, D. V., El-Messery, T. M., Baranenko, D. A., Gutiérrez, J., Shams, M. Y., El-Hafeez, T.A., Elsayed, S., Alhag, S.K., Moghanm, F.S., Mulyukin, M.A., Petrova, Y,Y. & Elwakeel, A.E. (2024). Semantic segmentation of microbial alterations based on SegFormer. Frontiers in Plant Science. 15, 1352935, 1-20. DOI: 10.3389/fpls.2024.1352935.
- Girgis, M.R., Mahmoud, T.M., & Abd-El-Hafeez, T. (2010). A new effective system for filtering pornography images from web pages and PDF files. International Journal of Web Applications. 2(1), 1-13.
- Eman, M., Mahmoud, T.M., Ibrahim, M.M. & Abd El-Hafeez, T. (2023). Innovative hybrid approach for masked face recognition using pretrained mask detection and segmentation, robust PCA, and KNN classifier. 23(15), 6727, 1-20. DOI:10.3390/s23156727.
- Abd El-Hafeez, T. (2010). A new system for extracting and detecting skin color regions from PDF documents. International Journal on Computer Science and Engineering. 9(2), 2838-2846. ISSN : 0975-3397.
- Mahmoud, T., Abd El-Hafeez, T. & Omar, A. (2014). A highly efficient content based approach to filter pornography websites. International Journal of Computer Vision and Image Processing. 2(1), 75-90. DOI:10.4018/ijcvip.2012010105.
- Ali, A., Abd El-Hafeez, T. & Mohany, Y. (2019). A robust and efficient system to detect human faces based on facial features. Asian Journal of Research in Computer Science. 2(4), 1-12. DOI:10.9734/AJRCOS/2018/v2i430080.
- Saabia, A.AB., El-Hafeez, T., Zaki, A.M. (2019). Face recognition based on grey wolf optimization for feature selection. In: Hassanien, A., Tolba, M., Shaalan, K., Azar, A. (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. Springer, Cham. DOI: 10.1007/978-3-319-99010-1_25.
- Ali, A.A., El-Hafeez, T.A. & Mohany, Y.K. (2019). An accurate system for face detection and recognition. Journal of Advances in Mathematics and Computer Science. 33(3), 1-19. DOI:10.9734/jamcs/2019/v33i330178.
- Sharma, A., Vans, E., Shigemizu, D., Boroevich, K.A. & Tsunoda, T. (2019). DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture. Scientific Reports. 9, 11399, 1-7. DOI: 10.1038/s41598-019-47765-6.
- Bazgir, O., Zhang, R., Dhruba, S. R., Rahman, R., Ghosh, S. & Pal, R. (2020) Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks. Nature Communications. 11, 4391, 1-13. DOI: 10.1038/s41467-020-18197-y.
- Ma, S., Zhang, Z. (2018). OmicsMapNet: Transforming omics data to take advantage of deep convolutional neural network for discovery. Machine Learning. 1, 1-33. https://arxiv.org/abs/1804.05283.
- Van der Maaten, L.J.P., Hinton, G.E. (2008). Visualizing high-dimensional data using t-SNE. Journal of Machine Learning Research. 9(11), 2579-2605.
- Nikhil, S., Sah, R. K., Parki, S. K., Tamang, T. B. & TR, M. (2023) Stock market prediction using genetic algorithm assisted LSTM-CNN hybrid model. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 6-8 July 2023 (pp. 1-6). Delhi, India. DOI: 10.1109/ICCCNT56998.2023.10306948.
- Shneiderman, B. (1992). Tree visualization with tree-maps: 2-d space-filling approach. ACM Transactions on Graphics. 11(1), 92-99. https://doi.org/10.1145/102377.115768.
- Zhu, Y., Brettin, T., Xia, F. & Partin, A. (2021). Converting tabular data into images for deep learning with convolutional neural networks. Scientific Reports. 11(1), 11325, 1-11. DOI:10.1038/s41598-021-90923-y.
- Okuniewska, A., Perzyk, M. & Kozłowski, J. (2021). Methodology for diagnosing the causes of die-casting defects, based on advanced big data modelling. Archives of Foundry Engineering. 21(4), 103-109. DOI:10.24425/afe.2021.138687.
- Okuniewska, A. (2023) Methodology for diagnosing the causes of product defects on the basis of advanced modelling based on big data sets. Retrieved December 13, 2024, from https://www.bip.pw.edu.pl/Postepowania-w-sprawie-nadania-stopnia-naukowego/Doktoraty/Wszczete-po-30-kwietnia-2019-r/Rada-Naukowa-Dyscypliny-Inzynieria-Mechaniczna/mgr-inz.-Alicja-Okuniewska/rozprawa-doktorska.
- Okuniewska, A., Perzyk, M. & Kozłowski, J. (2023). Machine learning methods for diagnosing the causes of die-casting defects. Computer Methods in Materials Science. 23(2), 45-56. DOI:10.7494/cmms.2023.2.0809.
- SDV (2024). Retrieved December 13, 2024, from Available: https://sdv.dev/SDV/api_reference/tabular/api/sdv.tabular.ctgan.CTGAN.html.
- Marin, J. (2022). Evaluating synthetically generated data from small sample sizes: an experimental study. Machine Learning. Computer Science. 1, 1-24. DOI: 10.48550/arXiv.2211.10760.
- Snoke, J., Raab, G. M., Nowok, B., Dibben, C., & Slavkovic, A. (2018) General and specific utility measures for synthetic data. Journal of the Royal Society Series, A: Statistics in Society. 181(3), 663-688. DOI: /10.1111/rssa.12358.
- Carr, A. (2024). Evaluating data sampling methods with a synthetic quality score. Retrieved December 13, 2024, from https://gretel.ai/blog/evaluating-data-sampling-methods-with-a-synthetic-quality-score.
- Borisov, V., Leemann, T., Seßler, K., Haug, J., Pawelczyk, M., Kasneci, G. (2022). Deep neural networks and tabular data: A survey. IEEE Transactions on Neural Networks and Learning Systems. 35(6), 7499-7519. DOI: 10.1109/TNNLS.2022.3229161.
- S.A., Zaman, M., Kaul, S. & Butt, M.A. (2022). Is deep learning on tabular data enough? An Assessment. International Journal of Advanced Computer Science and Applications. 13(4), 466-473. DOI: 10.14569/IJACSA.2022.0130454.
- Neto, L., et al., (2023). A comparative analysis of converters of tabular data into image for the classification of Arboviruses using Convolutional Neural Networks. PLoS One. 18(12), e0295598, 1-14. DOI:10.1371/journal.pone.0295598.
- Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K. & Ghayvat, H. (2021). CNN variants for computer vision: history, architecture, application, challenges and future scope. 10(20), 2470, 1-28. https://doi.org/10.3390/electronics10202470.
- Bouvrie, J (2006). Introduction Notes on Convolutional Neural Networks. Retrieved December 13, 2024, from https://web.mit.edu/jvb/www/papers/cnn_tutorial.pdf.
- Nwankpa, C., Ijomah, W., Gachagan, A. & Marshall, S. (2018). Activation functions: Comparison of trends in practice and research for deep learning. Machine Learning. ArXiv preprint. 1-20. https://doi.org/10.48550/arXiv.1811.03378.
- Kingma, D.P., Ba, J.A. (2014). A method for stochastic optimization. In 3rd International Conference for Learning Representations. San Diego.
- Rajawat, A.S., Ghosh, A. (2022). Chapter six - Renewable energy system for industrial internet of things model using fusion-AI. In Applications of AI and IOT in Renewable Energy. 107-128. DOI: /10.1016/B978-0-323-91699-8.00006-1.
- Kandel, I. & Castelli, M. (2020). The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express. 6(4), 312-315. DOI: 10.1016/j.icte.2020.04.010.
- Burzyńska, A. (2024). Review of data-driven decision support systems and methodologies for the diagnosis of casting defects. Archives of Foundry Engineering. 24(4), 126-135. DOI: 10.24425/afe.2024.151320.