İmalatta Makine Öğrenmesi Alanında Patent Analizi

Year 2024, Volume: 2024 Issue: 1, 80 - 94, 30.04.2024

Murat Akkalender Haydar Yalçın

https://doi.org/10.56337/sbm.1461449

Abstract

Patent analizi, 1980’den bu yana üretim için makine öğrenmesinde bir artış olduğunu ortaya koymaktadır ve geleneksel uygulamaların ötesindeki potansiyeline işaret etmektedir. Bu çalışma bu eğilimi üç temel soru aracılığıyla araştırmaktadır: Makine öğrenmesi kullanımının nasıl geliştiği, patentlerin hangi teknolojik alanları kapsadığı ve bu makine öğrenimi uygulamalarının nerede geliştirildiği. Analiz, makine öğrenmesinin çeşitli endüstrilerde tıbbi cihazlar ve kalite kontrol gibi alanları etkilediğini ortaya koymaktadır. Bu bulgular, makine öğrenmesinin verimliliği artırabileceğini, kaliteyi güvence altına alabileceğini ve yeniliği teşvik ederek belirli uygulamalara, üretkenlik etkilerine ve potansiyel zorluklara yönelik gelecekteki araştırmaların önünü açabileceğini göstermektedir. Lens.org’dan alınan patent verileri BibExcel, Pajek, ve VOSviewer kullanılarak görselleştirilmiştir.

Keywords

Makine Öğrenmesi, İmalat, Akıllı Üretim, Patent Analizi, Endüstri 4.0

Patent Analysis in the Realm of Machine Learning in Manufacturing

Abstract

Patent analysis reveals a surge in machine learning for manufacturing since 1980, hinting at its potential beyond traditional applications. This study explores this trend through three key questions: how machine learning use is evolving, what technological areas patents cover, and where these machine learning applications are being developed. The analysis finds machine learning impacting areas like medical devices and quality control across various industries. These findings suggest that machine learning can improve efficiency, ensure quality, and drive innovation, paving the way for future research into specific applications, productivity impacts, and potential challenges. Patent data from Lens.org was visualized employing of BibExcel, Pajek and VOSviewer.

Keywords

Machine Learning, Manufacturing, Smart Manufacturing, Patent Analysis, Industry 4.0

References

• Al-Sakkari, E. G., Ragab, A., Dagdougui, H., Boffito, D. C., & Amazouz, M. (2024). Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities. Science of The Total Environment.
• Chua, C., Liu, Y., Williams, R. J., Chua, C. K., & Sing, S. L. (2024). In-process and post-process strategies for part quality assessment in metal powder bed fusion: A review. Journal of Manufacturing Systems, 75-105.
• Dogan, A., & Birant, D. (2021, March 15). Machine learning and data mining in manufacturing. Expert Systems With Applications, p. 1-22.
• Gajdoš, P., Ježowicz, T., Uher, V., & Dohnálek, P. (2016). A parallel Fruchterman-Reingold algorithm optimized for fast visualization of large graphs and swarms of data. Swarm and Evolutionary Computation, 56-63.
• Hussin, F., Rahim, S. A., Hatta, N. S., Aroua, K. M., & Mazari, S. A. (2023). A systematic review of machine learning approaches in carbon capture applications. Journal of CO2 Utilization.
• Iftikhar S., Gill S.S., Song C., Xu M., Aslanpour M.S., Toosi A.N., Du J., Wu H., Ghosh S., Chowdhury D., Golec M., Kumar M., Abdelmoniem A.M., Cuadrado F., Varghese B., Rana O.F., Dustdar S., & Uhlig S. (2023). AI-based fog and edge computing: A systematic review, taxonomy and future directions. Internet of Things.
• Jiang, J. (2023). A survey of machine learning in additive manufacturing technologies. International Journal of Computer Integrated, p. 1258-1280.
• Kamada, T., & Kawai, S. (1988). A simple method for computing general position in displaying three-dimensional objects. Computer Vision, Graphics, and Image Processing, 43-56.
• Kilic, A., Oral, B., Eroglu, D., & Yildirim, R. (2023). Machine learning for beyond Li-ion batteries: Powering the research. Journal of Energy Storage.
• Meng, L., McWilliams, B., Jarosinski, W., Park, H. Y., Jung, Y. G., Lee, J., & Zhang, J. (2020, April 17). Machine Learning in Additive Manufacturing: A Review. The Journal of The Minerals, Metals & Materials Society (TMS), pp. 2363–2377.
• Mousavizadegan, M., Firoozbakhtian, A., Hosseini, M., & Ju, H. (2023). Machine learning in analytical chemistry: From synthesis of nanostructures to their applications in luminescence sensing. TrAC Trends in Analytical Chemistry.
• Pham, D. T., & Afify, A. A. (2005, May). Machine-learning techniques and their applications in manufacturing. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, p. 395-412.
• Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi , M. U., Heymach, J., Le. X., Yan, H. & Alam, T. (2023). AI in drug discovery and its clinical relevance. Heliyon.
• Rai, R., Tiwari, M. K., Ivanov, D., & Dolgui, A. (2021, August 18). Machine learning in manufacturing and industry 4.0 applications. International Journal of Production Research, p. 4773-4778.
• Sánchez-Garrido, A. J., Navarro, I. J., García, J., & Yepes, V. (2023). A systematic literature review on modern methods of construction in building: An integrated approach using machine learning. Journal of Building Engineering.
• Shah, S. S. A., Zafar, H. K., Javed, M. S., Ud Din, M. A., Alarfaji, S. S., Balkourani, G., Sohail, M., Tsiakaras, P., &. Najam, T. (2024). Mxenes for Zn-based energy storage devices: Nano-engineering and machine learning. Coordination Chemistry Reviews.
• Tamir, T. S., Xiong, G., Shen, Z., Leng, J., Fang, Q., Yang, Y., Jiang, J., Lodhi, E., & Wang, F.-Y. (2023). 3D printing in materials manufacturing industry: A realm of Industry 4.0. Heliyon.
• Tauhid, A., Xu, L., Rahman, M., & Tomai, E. (2023). A survey on security analysis of machine learning-oriented hardware and software intellectual property. High-Confidence Computing.
• Thangavel, K., Sabatini, R., Gardi, A., Ranasinghe, K., Hilton, S., Servidia, P., & Spiller, D. (2024). Artificial Intelligence for Trusted Autonomous Satellite Operations. Progress in Aerospace Sciences.
• Usman, M., Cheng, S., Boonyubol, S., & Cross, J. S. (2024). From biomass to biocrude: Innovations in hydrothermal liquefaction and upgrading. Energy Conversion and Management.
• Wang, C., Tan, X. P., Tor, S. B., & Lim, C. S. (2020, December). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, p. 101538.
• White, D. R., & Borgatti, S. P. (1994). Betweenness centrality measures for directed graphs. Social Networks, 335-346.
• Wuest, T., Irgens, C., & Thoben, K.-D. (2014). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing, 1167-1180.
• Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016). Machine learning in manufacturing: advantages, challenges, and applications. Production & Manufacturing Research, p. 23-45.
• Xie, Y., Sattari, K., Zhang, C., & Lin, J. (2023). Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation. Progress in Materials Science.
• Yalcin, H., & Daim, T. (2021). Mining research and invention activity for innovation trends: case of blockchain technology. Scientometrics, 3775-3806.
• Zeba, G., Dabić, M., Čičak, M., Daim, T., & Yalcin, H. (2021). Technology mining: Artificial intelligence in manufacturing. Technological Forecasting & Social Change.
• Zhang, C., Wang, Z., Zhou, G., Chang, F., Ma, D., Jing, Y., Cheng, W., Ding, K., & Zhao, D. (2023). Towards new-generation human-centric smart manufacturing in Industry 5.0: A systematic review. Advanced Engineering Informatics.
• Zhang, H. L., Liu, J., Feng, C., Pang, C., Li, T., & He, J. (2016). Complex social network partition for balanced subnetworks. 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 4177-4182). Vancouver, BC, Canada: IEEE.
• Zhu, Z., Hu, Z., Seet, H. L., Liu, T., Liao, W., Ramamurty, U., & Nai, S. M. (2023). Recent progress on the additive manufacturing of aluminum alloys and aluminum matrix composites: Microstructure, properties, and applications. International Journal of Machine Tools and Manufacture.