ABSTRACT

Online process control is a crucial task in modern production systems that use digital twin technology. The data acquisition from machines must provide reliable and on-the-fly data, reflecting the exact status of the ongoing process. This work presents an architecture to acquire data for an Additive Manufacturing (3D printer) process, using a set of consolidated Internet of Things (IoT) technologies to collect, verify and store these data in a trustful and secure way. The need for online monitoring and fault detection is addressed by the development of a classifier using Convolutional Neural Networks. This deep learning approach, using temporally aligned vibration data provided by the underlying architecture, allows raw data processing to detect patterns without signal pre-processing and without domain-specific knowledge for model building.

摘要

在使用数字孪生技术的现代生产系统中，在线过程控制是一项至关重要的任务。从机器获取的数据必须提供可靠的实时数据，以反映正在进行的过程的确切状态。这项工作提出了一种架构，该架构使用一组整合的物联网（IoT）技术以可信任和安全的方式收集，验证和存储这些数据，从而为增材制造（3D打印机）过程获取数据。通过使用卷积神经网络开发分类器，可以满足在线监视和故障检测的需求。这种深度学习 该方法使用由底层架构提供的时间对齐的振动数据，允许原始数据处理来检测模式，而无需进行信号预处理，也不需要用于模型构建的特定领域知识。