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

arXiv: https://arxiv.org/abs/2209.10178 · HTML: https://mlsanity.com/paper/2209.10178/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 3 of 3)

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [A Data-Centric Approach for Training Deep Neural Networks with Less Data](https://mlsanity.com/paper/2110.03613/) (2021)
- [A Survey on Data Cleaning Methods for Improved Machine Learning Model Performance](https://mlsanity.com/paper/2109.07127/) (2021)

## Abstract (arXiv)

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.