In addition to high-precision and high production speed, high-quality machining of surfaces is also an important factor in computer numerical control (CNC) machining. Achieving these three factors simultaneously requires a balance between accuracy, velocity and surface quality. This can be obtained by appropriately adjusting the related parameters and controlling factors such as tolerance. Generally, the accuracy and speed of CNC machining can be quantified and evaluated based on error and machining time, respectively.
Several studies have attempted to develop effective surface quality evaluation methods. However, these methods mainly rely on photographic techniques or human visual inspection, which are difficult to perform, inconclusive and unsuitable for machining processes involving cutting that have more irregularities. In addition, there is no standard industrial surface quality evaluation method for parts machined by cutting. To this end, developing effective and robust methods for quantifying and evaluating the surface quality of machined parts is highly desirable.
The degree of light reflection is known to significantly affect human visual evaluation of surface quality. Nevertheless, luminance differences are rarely used to evaluate the quality of machined surfaces due to the lack of correlation between luminance and surface roughness. In this study, Dr. Toshiaki Otsuki, Mr. Kenji Okita and Professor Hiroyuki Sasahara from Tokyo University of Agriculture and Technology evaluated the quality of machined surfaces via surface roughness and luminance. An imaging method for visualizing the surface quality was proposed based on luminance, luminance differences and surface roughness indices. The work is currently published in the journal, Precision Engineering.
In their approach, surfaces machined under different conditions using a ball end mill were illuminated, and the indices of the luminance (reflected light intensity) measured using a vision system and surface roughness measured using a laser microscope were quantitatively correlated to evaluate the surface quality through minute division on the surface. The luminance differences were also imaged to visualize the surface quality of the surfaces. These parts were further highlighted by applying a threshold value for the image. Different feed rate amount and pick feed conditions were utilized, and the applicability of the proposed method was validated.
The authors observed that the low values of root mean square luminance/luminance mean as well as that of the root mean square luminance differences were of high quality and reliable indices, thereby desirable for evaluating surface quality. For all the examined minute divisions (150 × 150 µm,50 × 50 µm and 2 × 2µm), the relationship between the surface roughness index and luminance index and that between surface roughness index and root mean square luminance difference exhibited positive correlations with each other. By visually evaluating the surface quality, it was possible to identify the uneven machined surfaces based on the resulting luminance differences. This was more effectively and precisely achieved when threshold values were applied to the images.
In summary, the study reported the objective and quantitative evaluation of the quality of machined surfaces using the presented method based on the luminance, luminance differences and surface roughness indices. The ability to effectively and accurately image machined surfaces and identify parts with low surface quality would improve polishing process efficiency and significantly reduce the need for post-processing like hand polishing. In a statement to Advances in Engineering, the authors explained that the proposed approach is a powerful tool for facilitating high quality machining.
Otsuki, T., Okita, K., & Sasahara, H. (2022). Evaluating surface quality by luminance and surface roughness. Precision Engineering, 74, 147-162.