The study of luminance effect on surface roughness of metallic surface by using vision system technique

Hamdan, Mohammad Noor Zaidi (2017) The study of luminance effect on surface roughness of metallic surface by using vision system technique. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Machine vision is a one of technology and method that applied in preparation of specimen regarding capturing image that usually for analysing image such as the application for production quality check-up, production process control and also for robot-guided system in the industry. In the other hand, machine vision commonly implemented a larger area which accordingly on what type of application based on their purposes. In this study, the image processing method which is one of the non-contact method is applied to measure the mean gray value (Ga) used to predict and calculate the value of defected surface roughness (Ra) on the specimen. All the specimens image are captured in different light source environment exposure which are Red LED light, white fluorescent and yellow bulb by using OMRON CCD camera. The captured image of each specimen are processed by using MATLAB software as to calculate Ga on the defected area on the captured image. In order to do so, the Graphical User Interface (GUI) could be developed by MATLAB software by implementation of Image Acquisition Toolbox to measure and calculate the Ga. The actual measurement of Ra of each specimen are taken by using stylus technique as the tool used is MITUTOYO surface measurement also the one of the contact method. When all the data, Ga specimen from different light sources and actual surface roughness are taken, it is easier to find the relationship between both data and a regression line can be generate. By using the regression line, model, Y = m X + C can be developed, which Y is represent Ra and X is represent Ga, so that the new Ra based on each different light source can be calculated. The new Ra regarding on the linear regression line will be compared with the value measured using stylus method as to determine which light source is the best model for calculating Ra. In order to determine which light is the best model, the correlation of each light source model was calculated for red LED light with R = 0.7459, white fluorescent with R = 0.9565 and yellow bulb with R = -0.9584. In this study reveals that by using the Ga from the image captured, it is possible to calculate the surface roughness of defected area on each specimen.

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