Hybrid, multi-modal methodology for Automatic Tool Wear Inspection (ATWI)
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Metal cutting machining processes, tool wear, multi-modal analytics
The metal cutting sector is a very important part of the Flemish manufacturing industry comprising more than 5000 companies and 200.000 jobs. The globalised competition puts higher demands on the production of these companies. Customers are asking for a good reliability, a perfect quality, with minimum cost and at the highest possible productivity.
During metal cutting, tools are used to remove metal parts (chips) or to cut the metal product into the desired shape. These cutting tools have an important impact on the process (time and cost), and on the product (quality). When cutting tools start wearing, their behaviour changes, resulting in a suboptimal production process. Being able to monitor and predict the state of these tools is of vital importance for the Flemish manufacturing industry to stay competitive.
In this context, the ATWI project aims to accurately analyse and predict the tool wear in metal cutting processes. Both direct (vision-based) and non-direct (data-driven) methods will be combined, to develop a hybrid approach that defines new methods to extract relevant data for tool wear prediction, identify the correlation between status of the tool-wear and underlying data, and construct an innovative integration of the multimodal information in production strategies.
February 2021 - January 2023
With the support of (met de steun van)
This project is a cross-domain project in collaboration with the precision manufacturing programme and the mechatronics programme of Sirris.