# An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing

arXiv: https://arxiv.org/abs/1907.11778 · HTML: https://mlsanity.com/paper/1907.11778/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Deep Neural Operator Enabled Digital Twin Modeling for Additive Manufacturing](https://mlsanity.com/paper/2405.09572/) (2024)
- [AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines](https://mlsanity.com/paper/2408.02181/) (2024)
- [Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges](https://mlsanity.com/paper/2403.00669/) (2024)
- [Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection](https://mlsanity.com/paper/2111.13884/) (2021)
- [Applying Deep Learning to Anomaly Detection of Russian Satellite Activity for Indications Prior to Military Activity](https://mlsanity.com/paper/2509.00050/) (2025)
- [GuidedFlow: An Attention-Guided Framework for Anomaly Detection in Additive Manufacturing](https://mlsanity.com/paper/2608.22789/) (2026)

## Abstract (arXiv)

We present a novel unsupervised deep learning approach that utilizes the encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed not only to detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) process. We demonstrate our approach on a dataset collected from a real-world testbed. The dataset contains images collected under both normal conditions and synthetic anomalies. We show that the encoder-decoder model is able to identify the injected anomalies in a modern manufacturing process in an unsupervised fashion. In addition, it also gives hints about the temperature non-uniformity of the testbed during manufacturing, which is what we are not aware of before doing the experiment.