Abstract
CyberManufacturing System is a vision for future manufacturing where physical components are fully integrated with computational processes in a connected environment. However, realizing the vision requires that its security be adequately ensured. This paper presents a vision-based system to detect intentional attacks on additive manufacturing processes, utilizing machine learning techniques. Particularly, additive manufacturing systems have unique vulnerabilities to malicious attacks, which can result in defective infills but without affecting the exterior. In order to detect such infill defects, the research uses simulated 3D printing process images as well as actual 3D printing process images to compare accuracies of machine learning algorithms in classifying, clustering and detecting anomalies on different types of infills. Three algorithms - (i) random forest, (ii) k nearest neighbor, and (iii) anomaly detection - have been adopted in the research and shown to be effective in detecting such defects.
Highlights
CyberManufacturing System is a vision for future manufacturing where physical components are fully integrated with computational processes in a connected environment
Additive manufacturing systems have unique vulnerabilities to malicious attacks, which can result in defective infills but without affecting the exterior
Image classification using machine learning techniques is adopted to detect such defects caused by deliberate attacks, from normal parts as well as common defects such as weak infill, gaps in thin walls, inconsistent extrusion, layer separation and splitting, and bed drop
Summary
CyberManufacturing System is a vision for future manufacturing where physical components are fully integrated with computational processes in a connected environment. CyberManufacturing system utilizes recent developments in Internet of Things, Cloud Computing, Fog Computing, Service-Oriented Technologies, Modeling and Simulation, Virtual Reality, Embedded Systems, Sensor Networks, Wireless Communications, Machine Learning, Data Analytics, and Advanced Manufacturing Processes. Additive manufacturing systems have unique vulnerabilities to malicious attacks, which can result in defective infills but without affecting the exterior. This can lead to the production of malicious defective parts without any warning. Image classification using machine learning techniques is adopted to detect such defects caused by deliberate attacks, from normal parts as well as common defects such as weak infill, gaps in thin walls, inconsistent extrusion, layer separation and splitting, and bed drop. Type of infill (Honeycomb), to test the infill type factor on accuracy of machine learning methods
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