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

Research output: Contribution to journalArticlepeer-review

81 Citations (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.

Original languageEnglish
Article number107860
JournalMeasurement: Journal of the International Measurement Confederation
Publication statusPublished - 2020 Sept


  • Convolutional neural network
  • Loss functions
  • Machine vision
  • Surface roughness evaluation
  • Turned and milled surfaces

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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