Evaluation of turned and milled surfaces roughness using convolutional neural network

Achmad P. Rifai, Hideki Aoyama, Nguyen Huu Tho, Siti Zawiah Md Dawal, Nur Aini Masruroh

研究成果: Article査読

29 被引用数 (Scopus)


Existing computer vision methods to measure surface roughness rely on feature extraction to quantify the surface morphology and build prediction models. However, the feature extraction is a complicated process requiring advanced image filtering and segmentation steps, resulting in long prediction time and complex setup. This study proposes the use of convolutional neural network to evaluate the surface roughness directly from the digital image of surface textures. This method avoids feature extraction since this step is integrated inside the network during the convolution process. Five loss functions for the prediction models are selected and analyzed based on their suitability and accuracy. The predicted values obtained are compared to the actual surface roughness values measured using a stylus-based profilometer. The performance of the proposed model is evaluated for the prediction of the surface roughness of typical machining operations, such as outside diameter turning, slot milling, and side milling, at various cutting conditions.

ジャーナルMeasurement: Journal of the International Measurement Confederation
出版ステータスPublished - 2020 9月

ASJC Scopus subject areas

  • 器械工学
  • 電子工学および電気工学


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