Towards automated quality control system for additive manufacturing post-processing

Quality control in additive manufacturing (AM) depends heavily on manual visual inspection. This will become a significant barrier for industry when companies are trying to scale up AM from prototyping to small patch and mass production. In the current paper, we present a setup suitable for automated quality monitoring tailored for AM and initial validation of the system. First, the paper describes the implementation of a chosen machine vision solution for monitoring part quality (e.g., surface quality) of AM objects. Then, we will proceed to present the results of two pilot studies focusing on evaluating the accuracy of the inspection of the system for both polymer and metal prints. Finally, we will discuss the possibility of integrating the system into an existing Industry 4.0 platform and the benefits the system can bring for automated part quality inspection for AM production lines.

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Publication Details

Journal
Procedia Computer Science
Published
2026-01-01
DOI
https://doi.org/10.1016/j.procs.2026.02.392
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
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article

Towards automated quality control system for additive manufacturing post-processing

Katri Salminen, Kari Naakka, Simo Huhtanen, Simo Häkkinen et al.
Procedia Computer Science
Additive Manufacturing and 3D Printing Technologies
article

Towards automated quality control system for additive manufacturing post-processing

Katri Salminen, Kari Naakka, Simo Huhtanen, Simo Häkkinen, Ville Jouppila
article en

Abstract

Quality control in additive manufacturing (AM) depends heavily on manual visual inspection. This will become a significant barrier for industry when companies are trying to scale up AM from prototyping to small patch and mass production. In the current paper, we present a setup suitable for automated quality monitoring tailored for AM and initial validation of the system. First, the paper describes the implementation of a chosen machine vision solution for monitoring part quality (e.g., surface quality) of AM objects. Then, we will proceed to present the results of two pilot studies focusing on evaluating the accuracy of the inspection of the system for both polymer and metal prints. Finally, we will discuss the possibility of integrating the system into an existing Industry 4.0 platform and the benefits the system can bring for automated part quality inspection for AM production lines.

Procedia Computer ScienceVol. 277
Tampere University of Applied Sciences (FI), Tampere University (FI)
Industry, innovation and infrastructure
Openalex Percentile: Top 12%
Additive Manufacturing and 3D Printing Technologies
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Towards automated quality control system for additive manufacturing post-processing — Katri Salminen, Kari Naakka, et al. · Procedia Computer Science (2026) | TGRS Research Map | TGRS