Abstract
The increasing interest towards additive manufacturing (AM) is pushing the industry to provide new solutions to improve process stability. Monitoring is a key tool for this purpose but the typical AM fast process dynamics and the high data flow required to accurately describe the process are pushing the limits of standard statistical process monitoring (SPM) techniques. The adoption of novel smart data extraction and analysis methods are fundamental to monitor the process with the required accuracy while keeping the computational effort to a reasonable level for real-time application. In this work, a new framework for the detection of defects in metal additive manufacturing processes via in-situ high-speed cameras is presented: a new data extraction method is developed to efficiently extract only the relevant information from the regions of interest identified in the high-speed imaging data stream and to reduce the dimensionality of the anomaly detection task performed by three competitor machine learning classification methods. The defect detection performance and computational speed of this approach is carefully evaluated through computer simulations and experimental studies, and directly compared with the performance and computational speed of other existing methods applied on the same reference dataset. The results show that the proposed method is capable of quickly detecting the occurrence of defects while keeping the high computational speed that would be required to implement this new process monitoring approach for real-time defect detection.
Highlights
In recent years, the increasing availability of low-cost machine vision systems and the advances in computational capabilities for image and video processing have pushed the adoption of these systems for change detection and process monitoring
The results achieved with the methods described in “ Methodology” section are discussed in “Classification results and comparison study” section together with a comparison with other state of the art approaches reported in literature on the same study case
The last classification method implemented in this work is a fully connected neural network (NN)
Summary
The increasing availability of low-cost machine vision systems and the advances in computational capabilities for image and video processing have pushed the adoption of these systems for change detection and process monitoring. High space and temporal resolution data streams from machine vision systems have found their application in metal additive manufacturing (AM) process monitoring (Everton et al 2016; Grasso and Colosimo 2017; Mani et al 2017; Spears and Gold 2016; Tapia and Elwany 2014). AM process monitoring is expected to be one of the key features of the new generation of AM machines (Grasso and Colosimo 2017) to limit the process variability that burdens this technology since its birth. In the last few years, machine builders (e.g. Renishaw, Trumpf) and independent companies (Sigma Labs) have started to implement monitoring sensors on industrial machines and to develop robust monitoring strategies for defect detection
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