ABSTRACT

             Roughness plays a central role in the functional attributes of parts, performance and production costs as well as mechanical properties. It is the main parameter characterizing the quality of a surface, and provides an indication of quality assurance for the manufacturing process. The measurement of engineering surface roughness is becoming increasingly important. Current techniques of surface measurement use surface profilometer to estimate the nature of the surfaces. To overcome the disadvantages arising from the use of the stylus in roughness measurement, several surface analysis techniques have been developed including scanning electron microscopy, near field microscopy, and optical techniques. In this work, a non-contact method using computer vision for inspecting surface roughness of components has been presented.



                                       





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ABSTRACT

Our Project discusses an E-quality learning system developed to automatically

measure and monitor the surface roughness of products by utilizing vision

technology. Several methods have been developed to measure surface

roughness in industry. These methods utilize a contact-based approach to

perform the necessary measurements. Our system is developed based on a noncontact

method that uses a smart machine vision camera and Lab VIEW-based

programming. The method for determining the roughness is based on the

correlation of optical roughness parameters and the average surface roughness.

After the surface roughness monitoring system has been built, it can be applied

as an automated quality control system used for educational purposes.

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