Abstract
In the manufacturing world, time truly equals money. The Overall Equipment Effectiveness (OEE) is a key performance indicator used in the manufacturing industry to determine the efficiency of plant operations. OEE is able to measure and visualize plant’s operational effectiveness, and to create actionable steps to improve quality, save time, and eliminate waste. It can be used as a benchmark to compare the organization performance to industry standards. In the era of Industry 4.0, smart manufacturing must embrace production big data to improve the manufacturing system significantly. Smart data exploitation provides advantages impacting key performance indicators (KPIs) like productivity, quality, and efficiency. In this chapter, an online OEE improvement system using machine learning and data analytics techniques is presented for the CNC machining production. Firstly, sensors connected to a CNC machine were used to collect machining process data relating to tool conditions and equipment conditions. Secondly, the machine data was analyzed to estimate the occurrence of tool failures, predict product quality and investigate machine performance, thus minimizing unnecessary downtime and maximizing machine efficiency for OEE improvement. We have conducted experiments on a CNC machine to validate how data analytics techniques could improve the OEE in the CNC machining process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Badiger, S., Gandhinathan, R.: A proposal: evaluation of OEE and impact of six big losses on equipment earning capacity. Int. J. Process Manage. Benchmarking 2(3), 234–248 (2008)
Bonada, F., Echeverria, L., Domingo, X., Anzaldi, G.: AI for improving the overall equipment efficiency in manufacturing industry. IntechOpen (2020). https://doi.org/10.5772/intechopen.89967
Brunellia, L., Masieroa, C., Tosatob, D., Beghic, A., Susto, G.A.: Deep Learning-based production forecasting in manufacturing: a packaging equipment case study. In: 29th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2019), June 24–28, Limerick, Ireland (2019)
Chand, G., Shirvani, B.: Implementation of TPM in cellular manufacture. J. Mater. Process. Technol. 149–154 (2000)
Dal, B., Tugwell, P., Greatbanks, R.: Overall equipment effectiveness as a measure of operational improvement–a practical analysis. Int. J. Oper. Prod. Manage. 20(12), 1488–1502 (2000)
DMG MORI NLX Series Turning Machines: https://en.dmgmori.com/products/machines/turning/universal-turning/nlx
Fam, S.F., Ismail, N., Yanto, H., Prastyo, D.D., Lau, B.P.: Lean manufacturing and overall equipment efficiency in paper manufacturing and paper products industry. J. Adv. Manuf. Technol. (2016)
Folmer, J., Schrüfer, C., Fuchs J., Vogel-Heuser, B.: Data-driven valve diagnosis to increase the overall equipment effectiveness in process industry. In: 2016 IEEE 14th International Conference on Industrial Informatics (INDIN) (2016)
Hassani, I., Mazgualdi, C., Masrour, T.: Artificial intelligence and machine learning to predict and improve efficiency in manufacturing industry. https://arxiv.org/abs/1901.02256
Hochreiter, A., Schmidhuber, J.: Long short-term memory. Neural Comput. 1735–1780 (1997)
Huang, S.H., Dismukes, J.P., Mousalam, A., Razzak, R.B., Robinson, D.E.: Manufacturing productivity improvement using effectiveness metrics and simulation analysis. Int. J. Prod. Res. 513–527 (2003)
Ingemansson, A., Bolmsjö, G.S.: Improved efficiency with production disturbance reduction in manufacturing systems based on discrete-event simulation. J. Manuf. Technol. Manage. 15(3), 267–279 (2004)
Jeong, K.Y., Phillips, D.T.: Operational efficiency and effectiveness measurement. Int. J. Oper. Prod. Manage. 21(11) (2001)
Konopka, J.M.: Improvement output in semiconductor manufacturing environments. Ph.D. dissertation, Arizona State University (1996)
Kusiak, A.: Smart manufacturing must embrace big data. Nature 544(7648), 23–25 (2017)
Leachman, R.C.: Closed-loop measurement of equipment efficiency and equipment capacity. IEEE Trans. Semicond. Manuf. 10 (1997)
Liao, D.Y., Tsai, W.P., Chen, H.T., Ting, Y.P., Chen, C.Y., Chen H.C., Chang, S.C.: Recurrent reinforcement learning for predictive overall equipment effectiveness. In: e-Manufacturing & Design Collaboration Symposium (2018)
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)
Muchiri, P., Pintelon, L.: Performance measurement using overall equipment effectiveness (OEE): literature review and practical application discussion. Int. J. Prod. Res. 3517–3535 (2008)
Nakajima, S: Introduction to TPM: total productive maintenance. Productivity Press (1988)
National Instruments (NI) Data Acquisition System: http://www.ni.com/data-acquisition
OEE calculation: https://www.oee.com/
Wong, S.Y., Chuah, J.H., Yap, H.J.: Technical data-driven tool condition monitoring challenges for CNC milling: a review, 4837–4857. Springer (2020)
Acknowledgements
This research is supported by the Agency for Science, Technology and Research (A*STAR) under its Advanced Manufacturing & Engineering (AME) Industry Alignment Funding—Pre-positioning funding scheme (Project No: A1723a0035)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Yuan, M. et al. (2021). Online Overall Equipment Effectiveness (OEE) Improvement Using Data Analytics Techniques for CNC Machines. In: Toro, C., Wang, W., Akhtar, H. (eds) Implementing Industry 4.0. Intelligent Systems Reference Library, vol 202. Springer, Cham. https://doi.org/10.1007/978-3-030-67270-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-67270-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67269-0
Online ISBN: 978-3-030-67270-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)