The proposed technique of surface roughness assessment provides satisfactory results. Surface roughness parameters are obtained with adequate accuracy in comparison with the stylus based parameters. However, certain identified roughness parameters provide more accurate results than others since the definition of these parameters involves less sensitive models to local magnitude changes. The obtained values of the surface roughness parameters provide valid distinct values among the different specimens in a similar manner to the stylus based technique. No obvious change in the obtained roughness parameter values is resulted from the micro/nano regions of data in the proposed method. The adopted technique of cavity graphs succeeds to clearly provide distinguishable graph profiles with respect to the metal-cavity relationship for micro/nano-scale regions. It is found that the resulting graph shapes of the nano-scale region data incline to change more gradually. This denotes the capability of the technique for collecting the macro surface details that are invisible in micro-scale data. The technique of auto correlation also demonstrates the great capacity in providing vital information with respect to the periodicity and randomness of the surface texture features in nano-scale data. The overall results guarantee the validity of vision data to enable surface roughness assessment. Therefore, the proposed method supports further development of the techniques for extensive applications in industries.
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