Acoustic Emission Condition Monitoring of the Coroning Process

Abstract

Acoustic emission (AE) is introduced for tool condition monitoring during the coroning process. The frequency components of the AE signal were used as features for classification. Two different feature selection methods were investigated, namely visual observation and the class mean scatter criterion. The minimum error rate Bayesian rule was used to distinguish between two extreme tool conditions. Although the features from visual observation could result in 100% classification, features based on the class mean scatter criterion showed excellent monitoring capability of tool failure when fewer features were used.

Monitoring tool wear using classifier fusion

“…Thus, the coroning process ensures final gear quality before its assembly in a transmission box. It has been used for transmission manufacturing, especially in volume production [54,55].…”