Nondestructive, fast, and cost-effective image processing method for roughness measurement of randomly rough metallic surfaces

Abstract

In recent years, various surface roughness measurement methods have been proposed as alternatives to the commonly used stylus profilometry, which is a low-speed, destructive, expensive but precise method. In this study, a novel method, called "image profilometry," has been introduced for nondestructive, fast, and low-cost surface roughness measurement of randomly rough metallic samples based on image processing and machine vision. The impacts of influential parameters such as image resolution and filtering approach for elimination of the long wavelength surface undulations on the accuracy of the image profilometry results have been comprehensively investigated. Ten surface roughness parameters were measured for the samples using both the stylus and image profilometry. Based on the results, the best image resolution was 800 dpi, and the most practical filtering method was Gaussian convolution+cutoff. In these conditions, the best and worst correlation coefficients (R) between the stylus and image profilometry results were 0.9892 and 0.9313, respectively. Our results indicated that the image profilometry predicted the stylus profilometry results with high accuracy. Consequently, it could be a viable alternative to the stylus profilometry, particularly in online applications.

Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

“…Further, all three of the models can obtain rather low convergent values in the learning process. In the comparison of the regression predictive accuracy in the learning results, the RMSE and MAPE utilized to assess the surface roughness prediction value are defined in Equations (10) and (11). The results of each of the models are shown in Table 1, the individual self-regression-predicted Ra is plotted (red) in Figure 11a to Figure 13a, and the regression-predicted Ra error is depicted in Figure 11b to Figure 13b.…”

“…The crucial factors affecting the performance of the data-driven approach are two-fold: the features extracted for model inputs and the selection of the model used for prediction. Regarding the feature extraction aspects, surface roughness prediction can be achieved directly or indirectly based on various sensor inputs, including images [10][11][12][13], accelerometers [14][15][16][17], and dynamometers [18][19][20][21][22]. S. Ghodrati et al [11] utilized an image profilometry approach to measure the surface roughness of metallic samples and achieved a highly accurate result.…”

Observation of ground surface roughness values obtained by stylus profilometer and white light interferometer for common metal materials

“…At the same time, this method has other significant disadvantages such as limitation to measurable surface profile, sensitive to vibrations and inability of on‐line measurement. And it has low measurement efficiency because the measurement is done based on several surface lines which are required to choose a suitable location and direction and the moving process has low speed 12,13 …”

“…These optical methods have the great advantage that can measure surface profiles with non‐contact scanning and thus does not scratch the surface to be measured. However, the above‐mentioned optical measuring methods have also disadvantages such as high operating environment requirements, lack of standards and usabilities 5,12 . For example, optical measuring instruments require strict isolation from environmental disturbances, including vibration, temperature changes and dust.…”

“…And it has low measurement efficiency because the measurement is done based on several surface lines which are required to choose a suitable location and direction and the moving process has low speed. 12,13 The first and second authors contribute equally to the paper.…”