# Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing

arXiv: https://arxiv.org/abs/2302.08495 · HTML: https://mlsanity.com/paper/2302.08495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 13 Jan 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Differentiable Augmentation for Data-Efficient GAN Training](https://mlsanity.com/paper/2006.10738/) (2020)
- [Persistence Images: A Stable Vector Representation of Persistent Homology](https://mlsanity.com/paper/1507.06217/) (2015)
- [Improved Consistency Regularization for GANs](https://mlsanity.com/paper/2002.04724/) (2020)
- [A Geometric Approach to Obtain a Bird's Eye View from an Image](https://mlsanity.com/paper/1905.02231/) (2019)
- [Horizon Lines in the Wild](https://mlsanity.com/paper/1604.02129/) (2016)
- [Advanced Steel Microstructural Classification by Deep Learning Methods](https://mlsanity.com/paper/1706.06480/) (2017)
- [Triple Generative Adversarial Networks](https://mlsanity.com/paper/1912.09784/) (2019)
- [A Conditional Generative Model for Predicting Material Microstructures from Processing Methods](https://mlsanity.com/paper/1910.02133/) (2019)
- [Continuous Conditional Generative Adversarial Networks (cGAN) with Generator Regularization](https://mlsanity.com/paper/2103.14884/) (2021)
- [The Adoption of Image-Driven Machine Learning for Microstructure Characterization and Materials Design: A Perspective](https://mlsanity.com/paper/2105.09729/) (2021)
- [TopTemp: Parsing Precipitate Structure from Temper Topology](https://mlsanity.com/paper/2204.00629/) (2022)
- [Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing](https://mlsanity.com/paper/2112.01687/) (2021)

## Built on by (most cited first, 1 of 1)

- [Topological Data Analysis in smart manufacturing: State of the art and futuredirections](https://mlsanity.com/paper/2310.09319/) (2023)

## Abstract (arXiv)

The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery from an emerging manufacturing process, shear assisted processing and extrusion (ShAPE). We generate realistic images conditioned on temper and either experimental parameters or material properties. In doing so, we are able to integrate machine learning into the development cycle, by allowing a user to immediately visualize the microstructure that would arise from particular process parameters or properties. This work forms a technical backbone for a fundamentally new approach for understanding manufacturing processes in the absence of first-principle models. By characterizing microstructure from a topological perspective we are able to evaluate our models' ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples. Our method is successful in capturing the visual and general microstructural features arising from the considered process, with analysis highlighting directions to further improve the topological realism of our synthetic imagery.