Surface roughness prediction of machined components using gray level co-occurrence matrix and Bagging Tree

Abstract

Surface Roughness of a machined component is crucial in identifying its functional capability when the manufactured specimen has metal to metal contact during operating condition since most wear and tear of the parts occurs due to friction between the surfaces of the moving parts. It is quite difficult to manually check the surface roughness of each component being manufactured on a manufacturing line. This paper aims to present a methodology to predict surface roughness using Image Processing, Computer Vision, and Machine Learning. Two machine learning algorithms Bagging Tree and Stochastic Gradient Boosting are compared and evaluated based on statistical parameters .It is observed that Stochastic Gradient Boosting predicts surface roughness in an efficient way both for training and Ten-fold cross-validation. The methodology used can be employed for online inspection and qualitative assessment of machined components.

Keywords

• surface roughness
• computer vision
• glcm
• machine learning