It is generally difficult to estimate the degree of deterioration of forging dies, but it is necessary to prevent a large number of defective products. In this study, we propose a deterioration score in cold lateral forging using acoustic emission (AE) signals. From the analysis of the measured data, the transition of the signal from the initial state to the deteriorated state can be observed, and the transition can be numerically evaluated. In the evaluation, variational auto-encoder (VAE) is used for learning the initial distribution, and the deterioration score is calculated by the degree of deviation from the learned distribution. The AE cumulative maximum amplitude and AE cumulative count during the linearly increasing stress period for each forging shot are given to the input of the VAE encoder, and valid deterioration scores are obtained for multiple actual measurements.
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.