Deep Learning Pipeline for Defect Detection

Procedia CIRP, vol. 138, pp. 833–838

Abstract

Deep Learning is successfully applied for detecting visual quality defects but comes with several challenges, e.g., annotating the data. Various tools, technologies, and frameworks have been introduced to simplify the implementation of DL. However, manufacturing companies often lack a general understanding of structuring a DL project and making relevant design decisions. In this paper, we demonstrate a methodological approach to integrate DL for defect detection in production. As a result, challenges and decisions are outlined in a pipeline that guides users through the DL project development process. The pipeline is validated based on surface defect detection for the lithium-ion battery coating process.

Authors 4

1. Alexander Mattern corresponding Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstrasse 17, Aachen, 52074, Germany

2. Maximilian Motz Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstrasse 17, Aachen, 52074, Germany

3. Dennis Grunert Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstrasse 17, Aachen, 52074, Germany

4. Robert Heinrich Schmitt Aachen Fraunhofer Institute for Production Technology IPT Laboratory for Machine Tools and Production Engineering (WZL)

   Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstrasse 17, Aachen, 52074, Germany

   Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Chair of Production Metrology and Quality Management, Steinbachstrasse 17, Aachen, 52074, Germany