Wire Arc Additive Manufacturing (WAAM) offers the possibility to build up large-scale metal parts. Data which is obtained from a multivariate sensor system in-situ must be analyzed automatically to ensure an early and reliable detection of defects to reduce the costs due to production scrap. For that reason, a modular anomaly detector for multivariate time series in WAAM was investigated in this paper. The approach adressed major topics in real-life data sets of industrial applications such as miscellaneous signal sample rates, lack of synchronization and concept drift. A reference data set based on an anomaly-dependently splitted time horizon was defined to reduce the sensitivity loss of the detector after an anomaly. To avoid the need for labeled data, an unsupervised anomaly detection method based on neural networks was used. Hence, no time and costs for artificial defect creation on the machine tool are required when implementing the approach in industrial applications.
In subtractive manufacturing, differences in machinability among batches of the same material can be observed. Ignoring these deviations can potentially reduce product quality and increase manufacturing costs. To consider the influence of the material batch in process optimization models, the batch needs to be efficiently identified. Thus, a smart service is proposed for in-situ material batch identification. This service is driven by a supervised machine learning model, which analyzes the signals of the machine’s control, especially torque data, for batch classification. The proposed approach is validated by cutting experiments with five different batches of the same specified material at various cutting conditions. Using this data, multiple classification models are trained and optimized. It is shown that the investigated batches can be correctly identified with close to 90% prediction accuracy using machine learning. Out of all the investigated algorithms, the best results are achieved using a Support Vector Machine with 89.0% prediction accuracy for individual batches and 98.9% while combining batches of similar machinability.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.