CE10 - Industrie et usine du futur : Homme, organisation, technologies

Automatic visual inspection using Machines learing : applications for industrie 4.0 – TEMIS

Submission summary

Visual inspection and detection of defects in manufactured products on a production line must be carried out in real time and at very high rates (under the second). This activity is an integral part of the overall product quality improvement strategy, it helps limit costly customer returns. There are already approaches to automate visual inspections, but they often need to be reprogrammed and calibrated in the event of changes to the product or the production chain. All products, even the most standard of them, may evolve (e.g. forms, aspects, functions) to meet the specific needs of each customer. To remain competitive, companies must have automatic visual inspections that quickly adapt to changes in product configurations (high product variability) and that perform very well on the production chain. This means that in the context of customized products, new and unknown defects may appear, and must therefore be able to be detected at lower cost. The TEMIS project therefore aims to develop an automated and reconfigurable approach for the online control of manufactured products, while respecting strict production requirements (granularity and inference time <1 second).

The scientific and industrial objective of TEMIS is to experiment and then recommend existing state-of-the-art solutions based on Machine Learning (ML) or Deep Learning (DL) to meet the need for online defect identification. The research that will be carried out in this ANR program, focused on Industrial Engineering, is a complete and detailed experimental study on visual inspection carried out in real conditions via an industrial experimental platform (AMS Agile Manufacturing platform - LabCom DIMEXP) located at UTC and for which the data used as a validation dataset, which will from real conditions, will be made available in open source.

One of the innovative features of the project is the development of an experimental study establishing fine comparisons (of granularity and precision) between approaches from the literature. This study will aim to verify that supervised and unsupervised statistical learning approaches (in ML and DL) can offer agility in inspection. From this study will be produced recommendations on the use of ML and DL algorithms vis-à-vis production requirements criteria as well as generic pipelines (chain of approaches) to reach the desired detection levels.

Project coordination

Harvey Rowson (DELTACAD)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

ROBERVAL Laboratoire Roberval. Unité de recherche en mécanique acoustique et matériaux.
HEUDIASYC Heuristique et diagnostic des systèmes complexes
DeltaCAD DELTACAD

Help of the ANR 444,825 euros
Beginning and duration of the scientific project: - 42 Months

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