Surface reconstruction method plays an important role in many engineering fields. It is an imperative procedure to carry out surface reconstruction from measurement data in reverse engineering, which is complicated with the presence of outliers. To achieve better accuracy and robustness of reconstruction, an improved moving total least squares (MTLS) algorithm based on k-means clustering called KMTLS method is proposed in this article. KMTLS adjusts the weights of discrete points within the support domain by adopting a two-step fitting procedure. Firstly, ordinary least squares (OLS) method is adopted to obtain the pre-fitting result and calculate the residuals as the input of k-means clustering. In kmeans clustering, abnormal nodes are classified into one cluster and a weight function based on clustering information is introduced to deal with these nodes. Secondly, based on the compact weight function in MTLS and the weight obtained in the pre-fitting procedure, weighted total least squares method is conducted to determine the final estimated value. The process of detecting outliers is automatic without setting threshold artificially. The experiment shows that KMTLS has great robustness to outliers.
When using a femtosecond laser to machine a single-crystal silicon wafer, it is accompanied with a diffraction spot of plasma. The existing literature reports that the brightness of the image of plasma can be used as an indicator to online measure the depth of the machined groove on a micrometer scale. Because the plasma spot is influenced by eruption and partial occlusion of ablated material, this method, which simply relies on the spot image brightness as a feedback parameter, is not reliable or accurate. The pixel area, perimeter, and brightness characteristics of the plasma spot image need to be comprehensively analyzed to provide a reliable and accurate feedback to establish close-loop micromachining technology. Therefore, we first analyze the chirped amplification principle of generating a femtosecond laser and the application of the diffraction spot of plasma during the micromachining processing using the femtosecond laser. Second, we experiment using femtosecond laser ablation with a piece of 10×10 mm and thickness of 650±10 μm single-crystal silicon wafer to obtain the corresponding relational data among parameters of laser processing power, processing speed, and laser spot image of plasma. Third, aiming at the characteristic of dim target of the laser spot image, the two-dimensional Otsu (maximum class square error method) is used to segment the laser spot image to improve the segmentation accuracy of the laser spot image. Finally, we analyze the relationship among area, perimeter of the laser spot image, and laser energy; the relationship among area, perimeter of the laser spot image, and the machined depth of groove; the relationship between brightness of the laser spot image and laser output power; and the relationship between brightness of laser spot image and machining speed.
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