Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques-Reference-Cited by-同舟云学术

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Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques

Published:2023-12-19 Issue: Volume: Page:
ISSN:
Container-title:RAiSE-2023
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Author:

Kamath Raghavendra C.¹^ORCID,Vijay G. S.¹,Prasad Ganesha²^ORCID,Rao P. Krishnananda¹,Shetty Uday Kumar¹,Parameshwaran Gautham¹,Shenoy Aniket¹,Shetty Prithvi¹

Affiliation:

1. Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India

2. Department of Artificial Intelligence and Machine Learning, Shri Madhwa Vadiraja Institute of Technology and Management, Vishwothama Nagar, Bantakal 574115, Karnataka, India

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/59/1/92/pdf

Reference20 articles.

1. Ashour, M.W., Khalid, F., Halin, A.A., and Abdullah, L.N. (2015, January 19–21). Machining process classification using PCA reduced histogram features and the Support Vector Machine. Proceedings of the IEEE 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, Malaysia.

2. Textural features for image classification;Haralick;IEEE Trans. Syst. Man. Cybern.,1973

3. Machine vision based image processing techniques for surface finish and defect inspection in a grinding process;Manish;Mater. Today Proc.,2018

4. Application of digital image processing in tool condition monitoring: A review;Dutta;CIRP J. Manuf. Sci. Technol.,2013

5. Davim, P.J. (2014). Modern Mechanical Engineering-Research, Development, and Education, Springer Nature.

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