Real Time Big Data Analytics for Tool Wear Protection with Deep Learning in Manufacturing Industry

Cakir A., Ozkaya E., Akkus F., Kucukbas E., Yilmaz O.

4th International Conference on Intelligent and Fuzzy Systems (INFUS), Bornova, Turkey, 19 - 21 July 2022, vol.505, pp.148-155 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 505
  • Doi Number: 10.1007/978-3-031-09176-6_18
  • City: Bornova
  • Country: Turkey
  • Page Numbers: pp.148-155
  • Keywords: Real-Time, MTConnect, Kepware, LSTM, Big Data, Apache NiFi, Apache Parquet, Elasticsearch, Kibana, AI in Manufacturing, Industry 4.0
  • Istanbul Technical University Affiliated: Yes


Industry 4.0 is a motivation that represents the transformation by data-driven industrial operations and decision making by digitization of manufacturing processes to gain operational advantages in the market. Considering how the manufacturing sector is adopting data-driven operations is challenging, given that there is not a straightforward definition of machine traceability, receiving and storing raw data from manufacturing lines, gives an opportunity to analyse the processes in real time nature. Thanks to big data management platforms and artificial intelligence decision support algorithms, it gives the ability to deeply understand the complexity of the processes and, accordingly, to eliminate or minimise false methods and reduce the costs that are insufficient for production. In addition, one of the biggest preventable costs for metal machining processes is the tool breakage and tool wearing problems. The motivation of this paper is to discuss data-driven decision making possibilities of the tool wearing and optimise breakage costs with using artificial intelligence. Furthermore, the analysis provides a proof-of-concept that the existence of a digital infrastructure combined with the analytical capabilities, such as real-time data management and monitoring, and having a highly accurate LSTM based time-series integrated artificial intelligent predictive model, to deal with inefficiencies in production processes. To this end, in this context, by developing the latest advancements in big data analytics, we propose a scalable predictive and preventive maintenance architecture for metal machining processes domain. We also show the opportunities and challenges of utilizing the big data architecture in the manufacturing domain.