@article{Kotsiopoulos2020,
title = {Deep multi-sensorial data analysis for production monitoring in hard metal industry},
author = { T. Kotsiopoulos and L. Leontaris and N. Dimitriou and D. Ioannidis and F. Oliveira and J. Sacramento and S. Amanatiadis and G. Karagiannis and K. Votis and D. Tzovaras and P. Sarigiannidis},
url = {https://www.researchgate.net/publication/344881380_Deep_multi-sensorial_data_analysis_for_production_monitoring_in_hard_metal_industry},
doi = {10.1007/s00170-020-06173-1},
year = {2020},
date = {2020-01-01},
journal = {International Journal of Advanced Manufacturing Technology},
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. © 2020, Springer-Verlag London Ltd., part of Springer Nature.},
keywords = {Deep Learning, Deep multi-sensorial data analysis, Hard metal industry, Production monitoring, Smart manufacturing},
pubstate = {published},
tppubtype = {article}
}
