论文标题
具有卷积神经网络的工业计算机断层扫描的轻量级解决方案
A Lightweight Solution of Industrial Computed Tomography with Convolutional Neural Network
论文作者
论文摘要
作为一种先进的非破坏性测试和质量控制技术，工业计算机断层扫描（ICT）在智能制造中发现了许多应用。现有的ICT设备通常笨重，涉及质量数据处理和传输。它导致效率低，无法与智能制造保持同步。在本文中，在物联网（IoT）和卷积神经网络（CNN）的支持下，我们提出了用于智能制造的ICT设备的轻量级解决方案。它包括两个方面的努力：分布式硬件分配和减少数据。在第一方面，ICT设备分为四个功能单元：数据采集，云存储，计算中心和控制终端。它们是由物联网分发和互连的。只有数据采集单元仍保留在生产线中。这种分布不仅纤细地占据了ICT设备，还允许相同功能单元的份额。在第二个方面，在数据采集单元中，采用了稀疏采样策略来减少原始数据，而单数值分解（SVD）用于压缩这些数据。然后将它们传输到云存储。在计算中心，将ICT图像重建算法和CNN应用于这些压缩的稀疏采样数据，以获得高质量的CT图像。使用实用ICT数据的实验已执行，以证明所提出的解决方案的有效性。结果表明，该解决方案可以实现急剧的数据降低，存储空间的节省和提高效率，而不会显着降解。提出的工作有助于推动ICT在智能制造中的应用。
As an advanced non-destructive testing and quality control technique, industrial computed tomography (ICT) has found many applications in smart manufacturing. The existing ICT devices are usually bulky and involve mass data processing and transmission. It results in a low efficiency and cannot keep pace with smart manufacturing. In this paper, with the support from Internet of things (IoT) and convolutional neural network (CNN), we proposed a lightweight solution of ICT devices for smart manufacturing. It consists of efforts from two aspects: distributed hardware allocation and data reduction. At the first aspect, ICT devices are separated into four functional units: data acquisition, cloud storage, computing center and control terminals. They are distributed and interconnected by IoT. Only the data acquisition unit still remains in the production lines. This distribution not only slims the ICT device, but also permits the share of the same functional units. At the second aspect, in the data acquisition unit, sparse sampling strategy is adopted to reduce the raw data and singular value decomposition (SVD) is used to compress these data. They are then transmitted to the cloud storage. At the computing center, an ICT image reconstruction algorithm and a CNN are applied to these compressed sparse sampling data to obtain high quality CT images. The experiments with practical ICT data have been executed to demonstrate the validity of the proposed solution. The results indicate that this solution can achieve a drastic data reduction, a storage space save and an efficiency improvement without significant image degradation. The presented work has been helpful to push the applications of ICT in smart manufacturing.
