[1]Kuchinic, A.E., Seidmann, A., 1988, “Tool management in automated manufacturing: operational issues and mathematical models”, Proceeding of International Industrial Engineering Conference, 379-382.
[2]Svinjarević, G., Stoić, A., Kopac, J., 2007, “Implementation of cutting tool management system”, Journal of Achievements in Materials and Manufacturing Engineering, 23(1), 99-102.
[3]Zhu, Y., Wu, J., Wu, J., and Liu, S., 2022, “Dimensionality reduce-based for remaining useful life prediction of machining tools with multisensor fusion”, Reliability Engineering & System Safety, 218, 108179. https://doi.org/10.1016/j.ress.2021.108179
[4]Archard, J. F., 1953, “Contact and rubbing of flat surfaces”, Journal of Applied Physics, 24(8), 981-988. https://doi.org/10.1016/j.ress.2021.108179
[5]An, Q., Tao, Z., Xu, X., El Mansori, M., and Chen, M., 2020, “A data driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network”, Measurement, 154, 107461.
https://doi.org/10.1016/j.measurement.2019.107461
[6]Wang, Y., Zheng, L., Wang, Y., 2021, “Event-driven tool condition monitoring methodology considering tool life prediction based on industrial internet”, Journal of Manufacturing Systems, 58, 205-222. https://doi.org/10.1016/j.jmsy.2020.11.019
[7]Luo, W., Hu, T., Ye, Y., Zhang, C., and Wei, Y., 2020, “A hybrid predictive maintenance approach for CNC machine tool driven by digital twin”, Robotics and Computer-Integrated Manufacturing, 65, 101974. https://doi.org/10.1016/j.rcim.2020.101974
[8]Cai, W., Zhang, W., Hu, X., Liu, Y., 2020, “A hybrid information model based on long short-term memory network for tool condition monitoring”, Journal of Intelligent Manufacturing, 31(6), 1497-1510. https://doi.org/10.1007/s10845-019-01526-4
[9]Chandra, D. G., 2015, “Base analysis of NoSQL database”, Future Generation Computer Systems., 52, 13-21. https://doi.org/10.1016/j.future.2015.05.003
[10]Kvalheim, C., 2012, “Data Modeling: Sample E-Commerce System with MongoDB”, https://www.infoq.com/articles/data-model-mongodb/
[11]Ramesh, D., Khosla, E., Bhukya, S. N., 2016, “Inclusion of e-commerce workflow with NoSQL DBMS: MongoDB document store”, Proceeding of the International Conference on Computational Intelligence and Computing Research (ICCIC).
https://doi.org/10.1109/iccic.2016.7919652
[12]Damaiyanti, T. I., Imawan, A., Indikawati, F. I., Choi, Y.-H., and Kwon, J., 2017, “A similarity query system for road traffic data based on a NoSQL document store”, Journal of Systems and Software, 127, 28–51. https://doi.org/10.1016/j.jss.2017.01.016
[13]Hu, C., Zhu, X., Zhou, Y., 2018, “The use of NoSQL in product traceability system construction”, Proceeding of the International Conference on Information Science and Control Engineering (ICISE), 574–577. https://doi.org/10.1109/ICISCE.2018.00124
[14]Catarinucci, L., de Donno, D., Mainetti, L., Palano, L., Patrono, L., Stefanizzi, M. L., and Tarricone, L., 2015, “An IOT-aware architecture for smart healthcare systems”, IEEE Internet of Things Journal, 2(6), 515-526. https://doi.org/10.1109/jiot.2015.2417684.
[15]Tsai, C., Shin, W. Y., Lu, Y.S., Huang, J. L., Yeh, L.Y. 2019, “Design of a data collection system with data compression for small manufacturers in industrial IoT environments”, Proceeding of the 20th Asia-Pacific Network Operations and Management Symposium, 1-4.
[16]Sukimin, Z., Haron, H., 2008, “Geometric entities information for feature extraction of solid model based on DXF file,” International Symposium on Information Technology.
https://doi.org/10.1109/itsim.2008.4632024
[17]Dimri, J., Gurumoorthy, B., 2005, “Handling sectional views in volume-based approach to automatically construct 3D solid from 2D views”, Computer-Aided Design, 37(5), 485-495. https://doi.org/https://doi.org/10.1016/j.cad.2004.10.007
[18]Çiçek, A., Gülesın, M., 2004, “Reconstruction of 3D models from 2D orthographic views using solid extrusion and revolution”, Journal of Materials Processing Technology, 152(3), 291-298. https://doi.org/10.1016/j.jmatprotec.2004.04.368
[19]Brookes, K. J. A., 2019, “ISO 13399 What is it and why do we need it”, Metal Powder Report, 74(6), 305-307. https://doi.org/10.1016/j.mprp.2019.08.005
[20]Stack Overflow Developer Survey 2022,
https://survey.stackoverflow.co/2022/#most-popular-technologies-webframe-learn
[21]mdn web docs,
https://developer.mozilla.org/zh-TW/docs/Learn/Server-side/Django/Introduction
[22]Greatness, M., 2022, “What is Django MVT Architecture? what differs MVT from MVC Architecture?”, DEV Community.
https://dev.to/great_devxy/what-is-django-mvt-architecture-what-differs-mvt-from-mvc-architecture-3ahg
[23]傳統 MVC 模式與 Django MTV 模式介紹與比較
https://mropengate.blogspot.com/2015/08/mvcdjangomtv.html
[24]MongoDB Documentation, https://www.mongodb.com/docs/manual/sharding/
[25]MongoDB Documentation, https://www.mongodb.com/docs/manual/replication/
[26]Venkat Nagappan, https://github.com/justmeandopensource/learn-mongodb
[27]MongoDB Documentation,
https://www.mongodb.com/docs/manual/core/read-preference/
[28]賴昱勝,2021,端銑刀刀刃口幾何角度預測模型之建構,國立虎尾科技大學機械與電腦輔助工程研究所,碩士論文[29]Juan, H., Yu, S. F., Lee, B. Y., 2003, “The optimal cutting-parameter selection of production cost in HSM for SKD61 tool steels”, International Journal of Machine Tools and Manufacture, 43(7), 679-686. https://doi.org/10.1016/s0890-6955(03)00038-5
[30]Bourdim M., Bourdim A., Kerrouz S., 2017, “Influence of cutting parameters on cutting forces”. International Journal of Materials, 4, 26-30.
[31]https://www.mongodb.com/docs/manual/core/gridfs/
[32]ISO 13399切削刀具數據表達與交換,2007
[33]Li, H., Yang, X., 2012, “Development of cutting tools information coding rules for the tools management system”, Proceeding of the 8th International Conference on Information Science and Digital Content Technology, 2, 397-400.
[34]Sukimin, Z., Haron, H., 2008, “Geometric entities information for feature extraction of solid model based on DXF file”, International Symposium on Information Technology. https://doi.org/10.1109/itsim.2008.4632024
[35]Dimri, J., Gurumoorthy, B., 2005, “Handling sectional views in volume-based approach to automatically construct 3D solid from 2D views”, Computer-Aided Design, 37(5), 485-495. https://doi.org/https://doi.org/10.1016/j.cad.2004.10.007
[36]FANUC數控系統中PMC的應用
[37]BEIJING-FANUC PMC MODEL PA1/SA1/SA3梯形圖語言編程說明書,2001 https://fs.gongkong.com/files/technicalData/200707/8-9739-161370EDFB14.pdf
[38]Brierley, J.A., Cowton, C.J., Drury, C., 2006, “A comparison of product costing practices in discrete-part and assembly manufacturing and continuous production process manufacturing”, International Journal of Production Economics, 100(2), 314-321.
https://doi.org/10.1016/j.ijpe.2004.12.020
[39]Daschbach, J. M., Apgar, H., 1988, “Design analysis through techniques of parametric cost estimation”, Engineering Costs and Production Economics, 14(2), 87-93.
https://doi.org/10.1016/0167-188x(90)90111-t
[40]Cavalieri, S., Maccarrone, P., Pinto, R., 2004, “Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry”, International Journal of Production Economics, 91(2), 165-177.
https://doi.org/10.1016/j.ijpe.2003.08.005
[41]Johnson, M., Kirchain, R., 2009, “Quantifying the effects of parts consolidation and development costs on material selection decisions: A process-based costing approach”, International Journal of Production Economics, 119(1), 174-186.
https://doi.org/10.1016/j.ijpe.2009.02.003
[42]Curran, R., Raghunathan, S., Price, M., 2004, “Review of aerospace engineering cost modelling: the genetic causal approach”, Progress in Aerospace Sciences, 40(8), 487-534.
https://doi.org/10.1016/j.paerosci.2004.10.001
[43]Asiedu, Y., Gu, P., 1998, “Product life cycle cost analysis: state of the art review”, International Journal of Production Research, 36(4), 883-908.
https://doi.org/10.1080/002075498193444
[44]Niazi, A., Dai, J. S., Balabani, S., and Seneviratne, L., 2007, “A new overhead estimation methodology: a case study in an electrical engineering company”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221(4), 699-710. https://doi.org/10.1243/09544054jem681
[45]Alami, D., ElMaraghy, W., 2021, “A cost benefit analysis for industry 4.0 in a job shop environment using a mixed integer linear programming model”, Journal of Manufacturing Systems, 59, 81-97. https://doi.org/10.1016/j.jmsy.2021.01.014
[46]Nentwich, C., Daub, R., 2022, “Cost-benefit analysis of industrial robot gear condition monitoring”, Procedia CIRP, 107, 143-148. https://doi.org/10.1016/j.procir.2022.04.024
[47]Dui, H., Lu, Y., Chen, L., 2024, “Importance-based system cost management and failure risk analysis for different phases in life cycle”, Reliability Engineering & System Safety, 242, 109785. https://doi.org/10.1016/j.ress.2023.109785
[48]Papathanasiou, D., Demertzis, K., Tziritas, N., 2023, “Machine failure prediction using survival analysis”, Future Internet, 15(5), 153. https://doi.org/10.3390/fi15050153
[49]Moat, G., Coleman, S., 2021, “Survival analysis and predictive maintenance models for non-censored assets in facilities management”, Proceedings of the 2021 IEEE International Conference on Big Data.
https://doi.org/10.1109/bigdata52589.2021.9671625
[50]Dong, Y., Xia, T., Fang, X., Zhang, Z., and Xi, L., 2019, “Prognostic and health management for adaptive manufacturing systems with online sensors and flexible structures”, Computers & Industrial Engineering, 133, 57-68.
https://doi.org/10.1016/j.cie.2019.04.051
[51]Abu-Samah, A., Shahzad, M. K., Zamai, E., and Said, A. B., 2015, “Failure prediction methodology for improved proactive maintenance using bayesian approach”, IFAC-PapersOnLine, 48(21), 844-851. https://doi.org/10.1016/j.ifacol.2015.09.632
[52]Kumar, A., Chinnam, R. B., Tseng, F., 2019, “An HMM and polynomial regression based approach for remaining useful life and health state estimation of cutting tools”, Computers & Industrial Engineering, 128, 1008-1014. https://doi.org/10.1016/j.cie.2018.05.017
[53]Wang, J., Yin, H., 2019, “Failure rate prediction model of substation equipment based on Weibull distribution and time series analysis”, IEEE Access, 7, 298-309.
https://doi.org/10.1109/access.2019.2926159
[54]張石平、王智明、楊建國,2015,機台刀具可靠性及壽命評估,計算機集成製造系统,6, 1-9.
[55]陳保家、陳雪峰、李兵等,2011,Logistic回歸模型在機台刀具可靠性評估中的應用,機械工程學報,47(18), 158-163.
[56]Yu, J., 2018, “Tool condition prognostics using logistic regression with penalization and manifold regularization”, Applied Soft Computing, 64, 454-467.
https://doi.org/10.1016/j.asoc.2017.12.042
[57]Battifarano, M., 2018, “Predicting Future Machine Failure from Machine State Using Logistic Regression”.
[58]Li, H., Wang, Y., Zhao, P., Zhang, X., and Zhou, P., 2015, “Cutting tool operational reliability prediction based on acoustic emission and logistic regression model”, Journal of Intelligent Manufacturing, 26, 923-931. https://doi.org/10.1007/s10845-014-0941-4
[59]Vafaei, N., Ribeiro, R. A., and Camarinha-Matos, L. M., 2019, “Fuzzy early warning systems for condition based maintenance”, Computers & Industrial Engineering, 128, 736-746. https://doi.org/10.1016/j.cie.2018.12.056
[60]Marcello, B., Davide, C., Marco, F., Roberto, G., Leonardo, M., and Luca, P., 2020, “An ensemble-learning model for failure rate prediction”, Procedia Manufacturing, 42, 41-48. https://doi.org/10.1016/j.promfg.2020.02.022
[61]Bevilacqua, M., Braglia, M., Frosolini, M., and Montanari, R., 2005, “Failure rate prediction with artificial neural networks”, Journal of Quality in Maintenance Engineering, 11(3), 279-294. https://doi.org/10.1108/13552510510616487
[62]Kutyłowska, M., 2015, “Neural network approach for failure rate prediction”, Engineering Failure Analysis, 47, 41-48. https://doi.org/10.1016/j.engfailanal.2014.10.007
[63]Yang, B., Guo, K., Feng, B., Zhou, C., Sun, C., and Sun, J., 2020, “Reliability assessment of cutting tools based on zero-failure data”, https://doi.org/10.21203/rs.3.rs-25508/v1
[64]Gaddafee, M., Chinchanikar S., 2020, “An experimental investigation of cutting tool reliability and its prediction using Weibull and Gamma models: A comparative assessment”, Mater Today Proceed, 24, 1478-1487.
https://doi.org/10.1016/j.matpr.2020.04.467
[65]Sun, H., Liu, Y., Pan, J., Zhang, J., and Ji, W., 2020, “Enhancing cutting tool sustainability based on remaining useful life prediction”, Journal of Cleaner Production, 244, 118794.
https://doi.org/10.1016/j.jclepro.2019.118794