# Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges

arXiv: https://arxiv.org/abs/2403.00669 · HTML: https://mlsanity.com/paper/2403.00669/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 314 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond](https://mlsanity.com/paper/2103.10689/) (2021)
- [Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networks](https://mlsanity.com/paper/2008.13547/) (2020)
- [A Fully Convolutional Neural Network for Speech Enhancement](https://mlsanity.com/paper/1609.07132/) (2016)
- [Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning](https://mlsanity.com/paper/2102.03355/) (2021)
- [Surrogate Modeling of Melt Pool Thermal Field using Deep Learning](https://mlsanity.com/paper/2207.12259/) (2022)
- [An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing](https://mlsanity.com/paper/1907.11778/) (2019)
- [Deep-Learned Generators of Porosity Distributions Produced During Metal Additive Manufacturing](https://mlsanity.com/paper/2205.05794/) (2022)
- [In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning](https://mlsanity.com/paper/2304.04598/) (2023)
- [Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network](https://mlsanity.com/paper/2210.17274/) (2022)
- [Toward Enabling a Reliable Quality Monitoring System for Additive Manufacturing Process using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.08749/) (2020)
- [PH-Net: Parallelepiped Microstructure Homogenization via 3D Convolutional Neural Networks](https://mlsanity.com/paper/2201.09672/) (2022)
- [Knowledge Distillation-based Information Sharing for Online Process Monitoring in Decentralized Manufacturing System](https://mlsanity.com/paper/2302.12004/) (2023)
- [Closed-Loop Control of Direct Ink Writing via Reinforcement Learning](https://mlsanity.com/paper/2201.11819/) (2022)
- [Deep Reinforcement Learning Based Toolpath Generation for Thermal Uniformity in Laser Powder Bed Fusion Process](https://mlsanity.com/paper/2404.07209/) (2024)
- [Toolpath design for additive manufacturing using deep reinforcement learning](https://mlsanity.com/paper/2009.14365/) (2020)

## Built on by (most cited first, 2 of 2)

- [Advanced Predictive Quality Assessment for Ultrasonic Additive Manufacturing with Deep Learning Model](https://mlsanity.com/paper/2410.24055/) (2024)
- [In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approach](https://mlsanity.com/paper/2604.09889/) (2026)

## Abstract (arXiv)

This paper presents the first comprehensive literature review of deep learning (DL) applications in additive manufacturing (AM). It addresses the need for a thorough analysis in this rapidly growing yet scattered field, aiming to bring together existing knowledge and encourage further development. Our research questions cover three major areas of AM: (i) design for AM, (ii) AM modeling, and (iii) monitoring and control in AM. We use a step-by-step approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to select papers from Scopus and Web of Science databases, aligning with our research questions. We include only those papers that implement DL across seven major AM categories - binder jetting, directed energy deposition, material extrusion, material jetting, powder bed fusion, sheet lamination, and vat photopolymerization. Our analysis reveals a trend towards using deep generative models, such as generative adversarial networks, for generative design in AM. It also highlights an increasing effort to incorporate process physics into DL models to improve AM process modeling and reduce data requirements. Additionally, there is growing interest in using 3D point cloud data for AM process monitoring, alongside traditional 1D and 2D formats. Finally, this paper summarizes the current challenges and recommends some of the promising opportunities in this domain for further investigation with a special focus on (i) generalizing DL models for a wide range of geometry types, (ii) managing uncertainties both in AM data and DL models, (iii) overcoming limited, imbalanced, and noisy AM data issues by incorporating deep generative models, and (iv) unveiling the potential of interpretable DL for AM.