A
system for measuring the
surface roughness of surfaces machined by
electrical discharge machining (EDM) using a
convolutional neural network, comprising: an
image recording device with a
camera module for recording images of a
machined surface with depressions and burrs produced by
electrical discharge machining; an image preprocessing module that is connected to the
image capture device and is configured to improve the texture contours of the captured images and optimize the image; a
Convolutional Neural Network module configured to receive images captured by the image preprocessing module, wherein the
Convolutional Neural Network module comprises the following: a
feature extraction submodule with multiple
convolution and
pooling layers, wherein this submodule is configured to analyze the distribution of
light intensity variations in the acquired images, where darker areas correspond to valleys and lighter areas to edges of the processed surface, and extracts features from this; and a classification submodule with fully connected
layers and an output layer, wherein the classification submodule is configured to predict an arithmetic mean roughness value (Ra) based on the extracted features; a
computer device comprising a processor, a
working memory and a storage unit, wherein the
computer device is configured to execute the
convolutional neural network module using the processor, the execution parameters being loaded from memory into the
working memory; and an output module integrated into the
computer device, wherein the processor is configured to create a
database of samples with recorded
surface roughness values within the storage unit, the
database containing the preprocessed image with
timestamp and image
metadata as well as the results obtained from the prediction, and wherein the processor is further configured to deliver the predicted arithmetic mean roughness value (Ra) from the output layer to a
user interface.