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Ultra-precision diamond milling (UPDM) provides a promising generation of high-quality surface up to sub-micrometric form accuracy and nanometric surface roughness. Prior to its machining process, the investigation on surface generation would be beneficial to achieve desired quality with reduced cost and boosted productivity. However, few studies discussed this issue so far. In this study, a theoretical and experimental investigation into surface generation was carried out in UPDM. Firstly, a geometric model was built up considering the material removal process between the milling tool and the workpiece. Secondly, surface generation process was elaborated according to the developed model. Finally, UPDM experiments were performed on a typical brass material at different cutting conditions and surface texture parameters were characterized according to ISO 25178-2. Autocorrelation function (ACF) analysis was employed for a detailed evaluation of surface generation. The results showed that the static factors (cutting parameters) as well as the dynamic factors (material effects and spindle vibration), which were associated with the cutting conditions, yielded a great influence on surface generation. Surface texture characterization indicated that there was a good consistence between simulation and experiment, demonstrating the feasibility of the proposed method. ACF analysis indicated that, with the increased feedrate/step ratio, surface generation approximately achieved a transition from the periodical surface (dominated by cutting residuals) to the random surface (determined by multiple components). Significantly, this study draws up a fundamental understanding toward surface generation in UPDM. [ABSTRACT FROM AUTHOR]

In order to solve the problems of large error and low performance of traditional progressive image model matching information checking methods, an automatic progressive image model matching information checking method based on machine learning is proposed. The generation method of progressive image is analyzed, and the target image sample is obtained. On this basis, machine learning algorithm is used to segment progressive image samples. In each image segmentation part, crawler technology is used to automatically collect progressive image model matching information, and under the constraint of image model matching information checking standard, automatic checking of progressive image model matching information is realized from geometric structure, image content and other aspects. Experimental results show that the verification error of the design method is reduced by 0.687 Mb, and the quality of progressive image is improved. [ABSTRACT FROM AUTHOR]

Nanometric surface roughness (NSR) is commonly produced in ultra-precision grinding (UPG). However, the NSR would be significantly affected by the sampling frequency (SF), i.e. sampling effect, since it is governed by rich low, middle, and high spatial frequency features. Hereby, the case study focused on discussing sampling effect in the NSR of UPG. The theoretical and experimental results have been found that along with the SF increase the NSR rises and rapidly converges and the SF is over 10 times of the spatial frequency for the NSR within the 5% distortion. Moreover, the NSR is acceptable within its 5% variation ratio under two SFs. Further, the SFs should be provided as the measurement traceability, together. Significantly, the case study draws up a better insight into sampling effect in the NSR of UPG along with a feasible suggestion on its measurement. [ABSTRACT FROM AUTHOR]