No abstract

No abstract

No abstract

The identification of surface texture images from machining surfaces using image processing techniques has been a prominent research area in the recent decades. The aim of this paper is to identify various machined surface texture images using machine learning techniques. Charge coupled device is used to capture images of machined components. Based on captured images, twelve statistical features are extracted and feature vector is formed. Grey Level Co-occurrence Matrix is used to extract statistical features from the machined surface images. Four Machine learning algorithms such as Random Forest, Support Vector Machine, Artificial Neural Network and J48 were utilized to characterize machined surfaces. Training and Tenfold cross validation process is utilized for identification of machined component images. It is found that Artificial Neural Network and Random forest give100 % training accuracy and 99% cross validation accuracy. Results obtained demonstrate the efficiency of proposed methodology, which is useful for identifying texture images.

No abstract

In the present work, a machine vision system is introduced, which captures images and extracts surface texture features of machined surfaces. The texture feature parameters are extracted using the gray‐level co‐occurrence matrix and correlated with different surface roughness parameters recorded by a contact‐type surface profilometer. The image acquisition carried out at different roughness levels in order to extract texture features. The variation between each texture features and surface roughness parameter is investigated. Multiple regression models are developed to predict the subjective estimation of surface roughness parameter (Ra) and qualitative detection of the degree of surface roughness. It is observed that the linear detection model shows better performance characteristics compared with a nonlinear detection model. The comparison between measured and predicted results shows that the linear detection model had a maximum relative error of 2.01%, drastically better than nonlinear detection model of −9.60% error parts, hence indicating better surface detection capability over the nonlinear detection model. The results demonstrate that the prediction of surface roughness using linear regression model is a reliable approach of noncontact measurement.

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.

No abstract