Surface roughness grade recognition model for milled workpieces based on deep transfer learning

Many roughness measurement methods rely on designed feature indexes that cannot accurately characterize the roughness and are demanding on the workpiece imaging environment. And roughness measurement methods based on deep neural networks require huge number of training samples and the same data distribution for training samples and testing samples, which makes it difficult to achieve wide application in the machining industry. Deep AlexCORAL, a surface roughness grade recognition model for milled workpieces based on deep transfer learning, is proposed in this paper to automatically extract more general roughness-related features. It not only reduces the amount of data required by the model, but also the difference in data distribution between the source domain (training set) and the target domain (testing set). The experimental results show that Deep AlexCORAL has 99.33% cross-domain recognition accuracy in a variety of cases with inconsistent data distribution due to various lighting environments. This is unmatched by other roughness grade recognition models.

A Novel Measurement Standard for Surface Roughness on Involute Gears

Although manufacturers of coordinate measurement systems and gear measurement systems already provide instruments that enable an end-of-line-monitoring of the roughness properties of gears, the roughness measurement on gear flanks still lacks traceability with respect to the standardised SI-units. There is still a gap between well standardised roughness measurements on planar surfaces and gear measurements on involutes. This gap is bridged by a novel physical measurement standard (PMS), also referred to as material measure, for roughness measurements on involute gears that has been developed at the Physikalisch-Technische Bundesanstalt (PTB). The necessary transformations between the systems of roughness and gear measurements have been implemented. The measurement standard itself represents calibrated roughness values for the parameters Ra, Rz, Rq, Rk, Rpk and Rvk and Mr1 and Mr2. Furthermore, the PMS can be measured both with classic profilometers as well as gear measurement systems with integrated roughness probes.

Numerical Study on Surface Roughness Measurement Based on Nonlinear Ultrasonics in Through-Transmission and Pulse-Echo Modes

Ultrasonic is one of the well-known methods for surface roughness measurement, but small roughness will only lead to a subtle variation of transmission or reflection. To explore sensitive techniques for surfaces with small roughness, nonlinear ultrasonic measurement in through-transmission and pulse-echo modes was proposed and studied based on an effective unit-cell finite element (FE) model. Higher harmonic generation in solids was realized by applying the Murnaghan hyperelastic material model. This FE model was verified by comparing the absolute value of the nonlinearity parameter with the analytical solution. Then, random surfaces with different roughness values ranging from 0 μm to 200 μm were repeatedly generated and studied in the two modes. The through-transmission mode is very suitable to measure the surfaces with roughness as small as 3% of the wavelength. The pulse-echo mode is sensitive and effective to measure the surface roughness ranging from 0.78% to 5.47% of the wavelength. This study offers a potential nondestructive testing and monitoring method for the interfaces or inner surfaces of the in-service structures.

Roughness measurement of leaf surface based on shape from focus

Background Surface roughness has a significant effect on leaf wettability. Consequently, it influences the efficiency and effectiveness of pesticide application. Therefore, roughness measurement of leaf surface offers support to the relevant research efforts. To characterize surface roughness, the prevailing methods have drawn support from large equipment that often come with high costs and poor portability, which is not suitable for field measurement. Additionally, such equipment may even suffer from inherent drawbacks like the absence of relationship between pixel intensity and corresponding height for scanning electron microscope (SEM). Results An imaging system with variable object distance was created to capture images of plant leaves, and a method based on shape from focus (SFF) was proposed. The given space-variantly blurred images were processed with the proposed algorithm to obtain the surface roughness of plant leaves. The algorithm improves the current SFF method through image alignment, focus distortion correction, and the introduction of NaN values that allows it to be applied for precise 3d-reconstruction and small-scale surface roughness measurement. Conclusion Compared with methods that rely on optical three-dimensional interference microscope, the method proposed in this paper preserves the overall topography of leaf surface, and achieves superior cost performance at the same time. It is clear from experiments on standard gauge blocks that the RMSE of step was approximately 4.44 µm. Furthermore, according to the Friedman/Nemenyi test, the focus measure operator SML was expected to demonstrate the best performance.