Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes

TL;DR

This paper presents a deep learning pipeline utilizing synthetic data and domain randomization to detect anomalies and improve quality in industrial additive manufacturing processes, addressing label scarcity challenges.

Contribution

It introduces a novel deep learning-based image processing pipeline combined with domain randomization and synthetic data for anomaly detection in industrial settings.

Findings

Promising results in anomaly detection accuracy.

Effective use of synthetic data to overcome label scarcity.

Potential for real-world industrial application.

Abstract

Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. Industrial processes are a domain where predicitve models are needed for finding anomalous data instances for quality enhancement. A main challenge, however, is absence of labels in this environment. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production. With a use case from additive manufacturing for automotive components we present a deep-learning-based image processing pipeline. Additionally, we integrate the concept of domain randomisation and synthetic data in the loop that shows promising results for bridging advances in deep learning and its application to real-world, industrial production processes.