Abstract
In this study we use machine vision to assess surface roughness of machined parts produced by the shaping and milling processes. Machine vision allows for the assessment of surface roughness without touching or scratching the surface, and provides the flexibility for inspecting parts without fixing them in a precise position. The quantitative measures of surface roughness are extracted in the spatial frequency domain using a two-dimensional Fourier transform. Two artificial neural networks, which take roughness features as the input, are developed to determine the surface roughness. The first network is for test parts placed in a fixed orientation, which minimises the deviation of roughness measures. The second network is for test parts placed in random orientations, which gives maximum flexibility for inspection tasks. Experimental results have shown that the proposed roughness features and neural networks are efficient and effective for automated classification of surface roughness.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Journal of Advanced Manufacturing Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
