  Machine-part interaction classification is a key capability required by
Cyber-Physical Systems (CPS), a pivotal enabler of Smart Manufacturing (SM).
While previous relevant studies on the subject have primarily focused on time
series classification, change point detection is equally important because it
provides temporal information on changes in behavior of the machine. In this
work, we address point detection and time series classification for
machine-part interactions with a deep Convolutional Neural Network (CNN) based
framework. The CNN in this framework utilizes a two-stage encoder-classifier
structure for efficient feature representation and convenient deployment
customization for CPS. Though data-driven, the design and optimization of the
framework are Subject Matter Expertise (SME) guided. An SME defined Finite
State Machine (FSM) is incorporated into the framework to prohibit intermittent
misclassifications. In the case study, we implement the framework to perform
machine-part interaction classification on a milling machine, and the
performance is evaluated using a testing dataset and deployment simulations.
The implementation achieved an average F1-Score of 0.946 across classes on the
testing dataset and an average delay of 0.24 seconds on the deployment
simulations.
