Development of a Cyber-Physical System based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control

Cyber-Physical Systems (CPS) seen from the Industrie 4.0 paradigm are key enablers to give smart capabilities to production machines. However, close loop control strategies based on raw process data need large amounts of computing power, which is expensive and difficult to manage in small electronic devices. Complex production processes, like laser surface heat treatment, are data intensive, therefore, the CPS development for these type of processes is challenging. As a result, the work described in this paper uses machine learning techniques like naive Bayes classifiers and feature selection optimization, in order to evaluate its performance during surface roughness detection. Additionally, the feature selection techniques will define optimal measuring zones to reduce generated data. The models are the first step towards its future embedding into a laser process machine CPS and bring self-predict capabilities to it.