Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing
This is an incremental review paper that provides an overview of CNNs for manufacturing applications, without introducing new methods or results.
The paper discusses basic concepts of convolutional neural networks (CNNs) and outlines their applications in manufacturing, illustrating these with diverse case studies in areas like spectral analysis and image-based control.
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.