# Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing

arXiv: https://arxiv.org/abs/2210.07848 · HTML: https://mlsanity.com/paper/2210.07848/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 96 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 19 of 19)

- [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)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Analyzing Learned Molecular Representations for Property Prediction](https://mlsanity.com/paper/1904.01561/) (2019)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://mlsanity.com/paper/1901.02985/) (2019)
- [Classification of crystallization outcomes using deep convolutional neural networks](https://mlsanity.com/paper/1803.10342/) (2018)
- [Image-Based Model Predictive Control via Dynamic Mode Decomposition](https://mlsanity.com/paper/2006.06727/) (2020)
- [Convolutional Neural Nets in Chemical Engineering: Foundations, Computations, and Applications](https://mlsanity.com/paper/2101.04869/) (2021)
- [SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control](https://mlsanity.com/paper/2202.01816/) (2022)

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

- [Advancing machine fault diagnosis: A detailed examination of convolutional neural networks](https://mlsanity.com/paper/2502.08689/) (2025)
- [Topological Data Analysis for High-Dimensional Dynamic Process Monitoring](https://mlsanity.com/paper/2606.20443/) (2026)

## Abstract (arXiv)

We discuss basic concepts of convolutional neural networks (CNNs) and outline uses in manufacturing. We begin by discussing how different types of data objects commonly encountered in manufacturing (e.g., time series, images, micrographs, videos, spectra, molecular structures) can be represented in a flexible manner using tensors and graphs. We then discuss how CNNs use convolution operations to extract informative features (e.g., geometric patterns and textures) from the such representations to predict emergent properties and phenomena and/or to identify anomalies. We also discuss how CNNs can exploit color as a key source of information, which enables the use of modern computer vision hardware (e.g., infrared, thermal, and hyperspectral cameras). We illustrate the concepts using diverse case studies arising in spectral analysis, molecule design, sensor design, image-based control, and multivariate process monitoring.