Part Recognition in Additive Production Systems using a Computer-vision Approach

Abstract

Nowadays, additive manufacturing is becoming increasingly important in various industries, e.g., automotive, aerospace, and dental. In particular, powder bed-based additive manufacturing processes for polymers have gained acceptance, as the technology itself offers high productivity and thus a high potential for industrialization. However, the technology is utilized in so-called additive production systems, in which a lack of standardization in the digital and physical processes is common these days. One example is the part recognition within the powder-bed-based process chain. Today, this task is done completely by manual labor, resulting in increased costs. Different approaches for simplifying this step have been evaluated in research, but there has not been discussed an app-based approach for this recognition task. Accordingly, in this paper, an approach for an app-based recognition system is presented, optimized, and benchmarked to existing solutions.

Authors 4

1. Günther Schuh Aachen

   RWTH Aachen University

   Affiliation as printed

   Labratory for Machine Tools and Production Engineering (WZL) at RWTH Aachen University, Campus-Boulevard 30,Aachen,Germany,52074

2. Gerret Lukas Aachen

   RWTH Aachen University

   Affiliation as printed

   Labratory for Machine Tools and Production Engineering (WZL) at RWTH Aachen University, Campus-Boulevard 30,Aachen,Germany,52074

3. Steffen Hohenstein Aachen

   RWTH Aachen University

   Affiliation as printed

   Labratory for Machine Tools and Production Engineering (WZL) at RWTH Aachen University, Campus-Boulevard 30,Aachen,Germany,52074

4. J.L. Drescher Aachen

   RWTH Aachen University

   Affiliation as printed

   Labratory for Machine Tools and Production Engineering (WZL) at RWTH Aachen University, Campus-Boulevard 30,Aachen,Germany,52074