Digital Image Processing
Image Enhancement with Feature Extraction Using DWT and Surface Roughness Prediction Using Neutral Network
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In today's competitive world, the greatest method for a company to survive is to manufacture a high-quality and an excellence product. For producing of superior quality products they require of reliable calibration system. Surface Roughness has an extreme significant place in engineering field like performance, surface quality, significantly improves weariness strength, rust resistance, crawl life, resistance, light reflection, wear and tear, capability of distributing and holding a lubricant, heat transmission, etc., of any machined part. Surface roughness can be evaluated in various methods....
In today's competitive world, the greatest method for a company to survive is to manufacture a high-quality and an excellence product. For producing of superior quality products they require of reliable calibration system. Surface Roughness has an extreme significant place in engineering field like performance, surface quality, significantly improves weariness strength, rust resistance, crawl life, resistance, light reflection, wear and tear, capability of distributing and holding a lubricant, heat transmission, etc., of any machined part. Surface roughness can be evaluated in various methods. This work presents an automated, flexible predication and non-contact of surface roughness of milling and grinding parts, the machine vision scheme included by artificial neural network. The image processing is a computational intensive task used in various engineering fields. Surface roughness metrology, in image processing has become a challenging task with extensive applications in an industry. Machine vision for surface roughness is measurement of features in the metrology branch. In several research works, the use of machine vision, had shown benefit of non-contact and fast process.
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