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Deep multi-sensorial data analysis for production monitoring in hard metal industry

Abstract

The industry practice of machining hard metal parts using CNC lathe turning machines is through grinding and milling procedures. The typical practice for quality control is through manual inspection, as automated solutions are difficult to integrate in production and do not reach the same level of accuracy. In this scope, the proposed system aims to automate the manufacturing process for the machine condition monitoring and 3D inspection of defective hard metal parts, by utilizing deep neural networks (DNNs) and investigating the defects on real production samples. Concretely, data are collected with (a) shop floor sensors, (b) high-resolution laser microprofilometer and (c) ultrasound scanner. The proposed system analyzes the collected data through AI models for quality control. Moreover, a fusion scheme is proposed to further improve accuracy. The system is validated on the classification of defective and non-defective samples, using metrics including accuracy, F-score, precision and recall for the performance evaluation.

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Funding

This work has been co-funded by the European Union and the General Secretariat of Research and Technology, Ministry of Development & Investments, under the project INVIVO/T2DGE-0951 of the Bilateral S&T Cooperation Program Greece - Germany 2017. This work has been partially supported by the European Commission through project RECLAIM funded by the European Union H2020 programme under Grant Agreement No. 869884. This work has been also partially supported by the European Commission through application experiment CloudEcho of project CloudiFacturing funded by the European Union H2020 programme under Grant Agreement No. 768892.

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Correspondence to Thanasis Kotsiopoulos.

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Kotsiopoulos, T., Leontaris, L., Dimitriou, N. et al. Deep multi-sensorial data analysis for production monitoring in hard metal industry. Int J Adv Manuf Technol 115, 823–836 (2021). https://doi.org/10.1007/s00170-020-06173-1

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Keywords

  • Production monitoring
  • Deep multi-sensorial data analysis
  • Smart manufacturing
  • Deep learning
  • Hard metal industry