Generalized Statistical Process Control via 1D-ResNet Pretraining

Procedia CIRP, vol. 138, pp. 1073–1078

Abstract

Statistical Process Control (SPC) suffers from high false positive rates for non-normally distributed quality characteristics as stability criteria are too sensitive. This makes SPC uneconomic when applied consequently due to production downtimes. To overcome limitations of SPC, we develop an approach limiting the false postitives without changing the quality inspection workflow. Based on synthetic data subject to an approach-specific definition of stability, a 1D-Residual Neural Network (1D-ResNet) is pretrained. The pretrained model can subsequently be applied to various use cases without the need of large amounts of data. A benchmark against SPC shows a significant decrease in false positives.

Authors 4

1. Tobias Schulze corresponding Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aach en University, Campus-Boulevard 30, 52074 Aachen, Germany

2. Louis Huebser Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aach en University, Campus-Boulevard 30, 52074 Aachen, Germany

3. Sebastian Beckschulte Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aach en University, Campus-Boulevard 30, 52074 Aachen, Germany

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

   RWTH Aachen University · Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

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

   Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aach en University, Campus-Boulevard 30, 52074 Aachen, Germany