Evaluation of turned and milled surfaces roughness using convolutional neural network

Rifai, Achmad P. and Aoyama, Hideki and Tho, Nguyen Huu and Md Dawal, Siti Zawiah and Masruroh, Nur Aini (2020) Evaluation of turned and milled surfaces roughness using convolutional neural network. Measurement, 161. p. 107860. ISSN 0263-2241, DOI https://doi.org/10.1016/j.measurement.2020.107860.

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Official URL: https://doi.org/10.1016/j.measurement.2020.107860


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. © 2020 Elsevier Ltd

Item Type: Article
Uncontrolled Keywords: Surface roughness evaluation; Turned and milled surfaces; Machine vision; Convolutional neural network; Loss functions
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 05 Aug 2020 07:23
Last Modified: 05 Aug 2020 07:23
URI: http://eprints.um.edu.my/id/eprint/25263

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