receiving a plurality of training machining datasets, wherein different training machining datasets are representative of different workpieces having an acceptable quality based on prior tests;

transforming the plurality of training machining datasets into a time-frequency domain to produce a plurality of training time-frequency domain datasets;

training, based on the plurality of the training time-frequency domain datasets, a function based on an autoencoder, which comprises an input layer, an output layer and a hidden layer, by providing the input layer and the output layer with each of the plurality of training time-frequency domain datasets; providing the trained function as a trained autoencoder function-for; and

performing the workpiece quality control based on the trained autoencoder function, comprising:

receiving a machining dataset based on time-series CNC machining data, wherein the machining dataset is representative for a quality of a workpiece;

transforming the machining dataset into a time-frequency domain to produce a time-frequency domain dataset;

applying the trained autoencoder function to the time-frequency domain dataset and generate data in the hidden layer;

analyzing the quality of the workpiece based on the data generated in the hidden layer of the trained autoencoder function; and

providing results of the analysis, wherein the autoencoder is a variational autoencoder with a specified statistical distribution for the hidden layer, and wherein the analyzing the quality of the workpiece comprises comparing the data generated in the hidden layer of the variational autoencoder with the specified statistical distribution.