Dimension reduction and 2D-visualization for early change of state detection in a machining process with a variational autoencoder approach

In this paper, we applied a variational autoencoder approach to an industrial machining problematic. We proposed a model based on a two-steps training process and a two-dimensional latent space. This two-dimensional latent space has better dimension reduction capability compared to a principal component analysis, which would require 24 components to express 90.0% of the variation. Moreover, the proposed model is shown capable of classifying a cutting operation based solely on data obtained from sensors mounted on a CNC machine, with an accuracy of 99.24%. The suggested model is also shown capable to be an efficient visual process monitoring tool capable of detecting early changes of state in a machining process. We show that this approach can visually identify defect caused by an increase of less than 1% of the energy in the signal, which is earlier than conventional monitoring methods. Additionally, our work is based on an industrial dataset acquired during regular production. This increases the opportunity for technological transfer when it comes to better understanding and better monitoring early changes in a machining process.