The influence of component inclination on surface finish evaluation using digital image processing

Many researchers have so far used Machine Vision and digital image processing for grabbing images of machined surfaces, improving their quality by pre-processing and then analysed them for evaluation of surface finish with a reasonable success. In the conventional mechanical stylus method used for roughness evaluation, many of the fundamental requirements are taken care of during measurement which includes alignment of component with the stylus pick up movement, tracing length, filter cut off length, etc. Practical use of Machine Vision for surface roughness estimation faces many challenges, as in this case only image is used for evaluation and not the component. For example, if the component is kept at an angle during imaging, there is a possibility of getting distorted information and the consistency of evaluation/quantification would become a problem. Therefore, there is a need to ensure that the measured surface is kept horizontal when the image is being taken. In this work, estimation of the surface roughness has been done and analysed using digital images of machined surfaces obtained by a Machine Vision system deliberately maintained at varying angles. The optical surface finish values (Ga) estimated in all such cases using Machine Vision approach are compared with that obtained using conventional stylus method (Ra). An artificial neural network (ANN) is trained and tested to arrive at the Ra values using the input obtained from the digital images of inclined surfaces which include optical roughness parameters estimated and angle of inclination of test parts. The experimental result indicates that the surface roughness could be estimated/predicted with a reasonable accuracy using Machine Vision and ANN, respectively. In addition, a shadow removal algorithm is used to improve the quality of the images of inclined surfaces and then the optical roughness parameter is estimated. All the results are compared with that obtained using stylus method and analysed in this paper.