Evaluation of turned and milled surfaces roughness using convolutional neural network

•This study develops deep learning models for evaluation of surface roughness.•The prediction is based on the pattern of ridge-valley of the cutter marks.•Five loss functions are analyzed to find its suitability for the roughness data.•The results of Ra, Rz, and RzJ prediction and its discrepancy ar...

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Veröffentlicht in:	Measurement : journal of the International Measurement Confederation 2020-09, Vol.161, p.107860, Article 107860
Hauptverfasser:	Rifai, Achmad P., Aoyama, Hideki, Tho, Nguyen Huu, Md Dawal, Siti Zawiah, Masruroh, Nur Aini
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Computer vision Convolution Convolutional neural network Diameters Digital imaging Feature extraction Image filters Image segmentation Loss functions Machine vision Measurement methods Morphology Neural networks Prediction models Profilometers Side milling Slot milling Styli Surface roughness Surface roughness evaluation Turned and milled surfaces Turning (machining) Vision systems
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