Demonstrating computer vision to small‐ and medium‐sized enterprises in manufacturing: Toward overcoming costs and implementation challenges

Engineering Reports, vol. 6

Abstract

Abstract Computer vision (CV) systems are crucial in various fields, such as manufacturing. However, the general feedback from small and medium‐sized enterprises (SMEs) in manufacturing indicates that implementing CV systems are out of reach due to their high costs, the need for expertise, and the complexities of Machine Learning (ML). To demonstrate the feasibility and applicability for SMEs, we present a cost‐effective portable CV‐based demonstrator for SMEs in manufacturing. Incorporating low‐cost hardware components, the system integrates open‐source software and employs Transfer Learning to adapt pre‐existing ML models. We present two illustrative use cases with fault detection and inventory management based on plastic brick datasets including various colors and shapes to demonstrate the system's effectiveness. Bringing this into a showable demonstrator, all components are integrated into a portable suitcase as a plug and play demonstration. We showcase the portable demonstrator at German television and four different industrial fairs, leveraging these dynamic platforms for direct interaction with SMEs and stakeholders. Firsthand insights and feedback from SMEs regarding our demonstration and their challenges, as well as opportunities for CV in manufacturing were received and summarized in this research.

Authors 6

1. Jonas Werheid 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 RWTH Aachen University Aachen Germany

2. Sven Münker 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 RWTH Aachen University Aachen Germany

3. Nils Klasen 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 RWTH Aachen University Aachen Germany

4. Tobias Hamann 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 RWTH Aachen University Aachen Germany

5. Anas Abdelrazeq 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 RWTH Aachen University Aachen Germany

6. Robert Heinrich Schmitt Aachen Frauenhofer 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

   Frauenhofer Institute for Production Technology IPT RWTH Aachen University Aachen Germany

   Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University RWTH Aachen University Aachen Germany