论文标题
卷积神经网络：制造业的基本概念和应用
Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing
论文作者
论文摘要
我们讨论了卷积神经网络（CNN）和制造中轮廓用途的基本概念。首先，我们讨论制造中通常遇到的不同类型的数据对象（例如，时间序列，图像，显微照片，视频，光谱，分子结构）如何使用张量和图形以灵活的方式表示。然后，我们讨论CNN如何使用卷积操作从此类表示形式中提取信息特征（例如几何模式和纹理）来预测新兴的特性和现象和/或识别异常。我们还讨论了CNN如何利用颜色作为关键信息来源，从而可以使用现代计算机视觉硬件（例如，红外，热摄像机）。我们使用在光谱分析，分子设计，传感器设计，基于图像的控制和多元过程监测中产生的各种案例研究来说明这些概念。
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.
