Data-driven decision support for process quality improvements

Procedia CIRP, vol. 99, pp. 313–318

Abstract

This paper presents a data-driven approach for improving the process quality of production systems. Therefore, the product quality is detected during the production process. The worker is provided with reasonable parameter recommendations about the production process as decision support to improve the process quality. To achieve this, a cross-process data analysis of the process and quality data is carried out using decision trees. The results are visualized in a comprehensible form for the worker. Based on a case study from mass production, the approach is evaluated and its performance is demonstrated in comparison to classical statistical methods.

Authors 5

1. Daniel Buschmann 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 Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

2. Chrismarie Enslin Aachen Institute for Information Management in Mechanical Engineering IMA

   RWTH Aachen University

   Affiliation as printed

   Institute for Information Management in Mechanical Engineering IMA of RWTH Aachen University, Dennewartstraße 2, 52068 Aachen, Germany

3. Hannes Elser 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 Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

4. Daniel Lütticke Aachen Institute for Information Management in Mechanical Engineering IMA

   RWTH Aachen University

   Affiliation as printed

   Institute for Information Management in Mechanical Engineering IMA of RWTH Aachen University, Dennewartstraße 2, 52068 Aachen, Germany

5. Robert Heinrich Schmitt 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 Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany