Electromechanical Engineer in Modern Manufacturing: Challenges, Responsibility, and Technical Solutions
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Advanced Technology Services. (2025, September 24). What is tool room management? https://www.advancedtech.com/blog/what-is-tool-room-management/
Allied Modular Building Systems. (2026, April 10). Tool rooms: Secure, organized & efficient storage for equipment & supplies. https://alliedmodular.com/what-is-a-tool-room-a-quick-guide/
Bergs, T., Holst, C., Gupta, P., & Augspurger, T. (2020). Digital image processing with deep learning for automated cutting tool wear detection. Procedia Manufacturing, 48, 947–958. https://doi.org/10.1016/j.promfg.2020.05.134
Cheng, M., Jiao, L., Yan, P., Jiang, H., Wang, R., Qiu, T., & Wang, X. (2022). Intelligent tool wear monitoring and multi-step prediction based on deep learning model. Journal of Manufacturing Systems, 62, 286–300. https://doi.org/10.1016/j.jmsy.2021.12.002
Cheng, Y., Lu, M., Gai, X., Guan, R., Zhou, S., & Xue, J. (2023). Research on multi-signal milling tool wear prediction method based on GAF-ResNext. Robotics and Computer-Integrated Manufacturing, 85, 102634. https://doi.org/10.1016/j.rcim.2023.102634
del Olmo, A., López de Lacalle, L. N., de Pissón, G. M., Pérez-Salinas, C., Ealo, J. A., Sastoque, L., & Fernandes, M. H. (2022). Tool wear monitoring of high-speed broaching process with carbide tools to reduce production errors. Mechanical Systems and Signal Processing, 172, 109003. https://doi.org/10.1016/j.ymssp.2022.109003
Ferraro, A., Galli, A., Moscato, V., & Sperlì, G. (2023). Evaluating explainable artificial intelligence tools for hard disk drive predictive maintenance. Artificial Intelligence Review, 56(7), 7279–7314. https://doi.org/10.1007/s10462-022-10354-7
Holst, C., Yavuz, T. B., Gupta, P., Ganser, P., & Bergs, T. (2022). Deep learning and rule-based image processing pipeline for automated metal cutting tool wear detection and measurement. IFAC-PapersOnLine, 55(2), 534–539. https://doi.org/10.1016/j.ifacol.2022.04.249
Igbagbosanmi John, B. (2023). Data-driven resource optimization approaches enhancing capacity planning, labor utilization, material efficiency and continuous improvement across manufacturing project lifecycles. GSC Advanced Research and Reviews, 17(3), Article 0467. https://doi.org/10.30574/gscarr.2023.17.3.0467
Kim, D. G., & Choi, J. Y. (2021). Optimization of design parameters in LSTM model for predictive maintenance. Applied Sciences, 11(14), 6450. https://doi.org/10.3390/app11146450
Li, W., Fu, H., Han, Z., Zhang, X., & Jin, H. (2022). Intelligent tool wear prediction based on Informer encoder and stacked bidirectional gated recurrent unit. Robotics and Computer-Integrated Manufacturing, 77, 102368. https://doi.org/10.1016/j.rcim.2022.102368
Li, Z., Liu, X., Incecik, A., Gupta, M. K., Królczyk, G. M., & Gardoni, P. (2022). A novel ensemble deep learning model for cutting tool wear monitoring using audio sensors. Journal of Manufacturing Processes, 79, 233–249. https://doi.org/10.1016/j.jmapro.2022.04.066
Mallioris, P., Aivazidou, E., & Bechtsis, D. (2024). Predictive maintenance in industry 4.0: A systematic multi-sector mapping. CIRP Journal of Manufacturing Science and Technology, 50, 80–103. https://doi.org/10.1016/j.cirpj.2024.02.003
Miao, H., Zhao, Z., Sun, C., Li, B., & Yan, R. (2021). A U-Net-based approach for tool wear area detection and identification. IEEE Transactions on Instrumentation and Measurement, 70, 1–10. https://doi.org/10.1109/TIM.2020.3033457
Omole, S., Dogan H., Lunt A.J.G., Kirk S., & Shokrani, A. (2023). Using machine learning for cutting tool condition monitoring and prediction during machining of tungsten. International Journal of Computer Integrated Manufacturing, 37(6), 747–771. https://doi.org/10.1080/0951192X.2023.2257648
Pech, M., Vrchota, J., & Bednář, J. (2021). Predictive maintenance and intelligent sensors in smart factory: Review. Sensors, 21(4), 1470. https://doi.org/10.3390/s21041470
Pimenov, D. Y., Bustillo, A., Wojciechowski, S., Sharma, V. S., Gupta, M. K., & Kuntoğlu, M. (2023). Artificial intelligence systems for tool condition monitoring in machining: Analysis and critical review. Journal of Intelligent Manufacturing, 34(5), 2079–2121. https://doi.org/10.1007/s10845-022-01923-2
Pinciroli, L., Baraldi, P., & Zio, E. (2023). Maintenance optimization in Industry 4.0. Reliability Engineering & System Safety, 234, 109204. https://doi.org/10.1016/j.ress.2023.109204
Qin, L., Zhou, X., & Wu, X. (2022). Research on wear detection of end milling cutter edge based on image stitching. Applied Sciences, 12(16), 8100. https://doi.org/10.3390/app12168100
QRmaint. (2025, March 31). Tool room in a manufacturing plant. https://qrmaint.com/blog/toolroom-in-manufacturing-plant/
Soldatov, A., Remnev, A., & Okada, A. (2022). Reconditioning of diamond coated tools and its impact on cutting performance for CFRP laminates. Applied Sciences, 12(3), 1288. https://doi.org/10.3390/app12031288
Teti, R., Mourtzis, D., D'Addona, D. M., & Caggiano, A. (2022). Process monitoring of machining. CIRP Annals, 71(2), 529–552. https://doi.org/10.1016/j.cirp.2022.05.009
Wang, X., Liu, M., Liu, C., Ling, L., & Zhang, X. (2023). Data-driven and knowledge-based predictive maintenance method for industrial robots for the production stability of intelligent manufacturing. Expert Systems with Applications, 234, 121136. https://doi.org/10.1016/j.eswa.2023.121136
Xu, Z., & Zhang, Q. (2026). Predictive maintenance optimization for industrial equipment via reliable prognosis and risk-aware reinforcement learning. Complex & Intelligent Systems, 12, Article 11. https://doi.org/10.1007/s40747-025-02127-w
Zippia. (2025, January 8). What does an electromechanical engineering technologist do? Roles and responsibilities. https://www.zippia.com/electromechanical-engineering-technologist-jobs/what-does-an-electromechanical-engineering-technologist-do/
Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150, 106889. https://doi.org/10.1016/j.cie.2020.106889