An accurate and efficient measurement for unknown rotor profile of screw compressor has been a nodus in the field of coordinate measuring machine (CMM) measurement because of its complexity of 3D helical surface, whose normal vectors vary with different measured points, while conventional 2D measuring methods have the inevitable radius compensation. If measured points and corresponding normal vectors are known, a 3D radius compensation then could be applied without a theoretical error. In this paper, a double-measurement method based on Reverse Engineering (RE) is proposed to solve this problem. The first measurement focused on constructing a 3D CAD model as accurate as possible. So, according to the structure characteristics of the unknown rotor, a reasonable WCS is established firstly. Then a DRCH method is presented to eliminate the outliers of measured points. Finally, an indirect method is presented to measure the screw pitch with projection and transformation of measured point sets. In second section, a 3D measurement is planned by DIMS language with setting measured points and corresponding normal vectors, which are calculated according to 3D CAD model constructed in first section. Final experimental analysis indicates that measuring accuracy with this double-measurement method is improved greatly.

Considering the failure of fully exploring the transferability of samples, domain adaptation (DA) is still troubled by the problem of the disturbed distribution of the target domain. In this article, a conditional domain confusion network (CDCN) is proposed, which commits to exploit the minimum class confusion (MCC) to achieve unsupervised cross-domain detection. To utilize the transferability of samples to better respond to domain shift, we first propose a transfer disturbance factor under the guidance of class confusion to constrain the classifier, designed to eliminate the impact of hard-to-transfer samples on the model. Moreover, an adaptive auxiliary component is embedded before the classifier, aiming at improving the generalization performance of the model, which adjusts the intra- and interclass distances of samples by uniting the classification loss to better fit the classifier. Extensive experiments are performed on multiple transfer tasks, which demonstrate the superiority of our proposed CDCN over the state-of-the-art DA strategies.

Clinically, skin flap transplantation was often used to repair skin wounds. However, the flap design process with sample cloth is rough and easy to cause infection and necrosis. So an accurate and individual shape design of preoperative flap should be solved. Therefore, a 3D wound flattening method for mapping skin mechanical properties based on finite element method was proposed. Firstly, the 3D point cloud of skin wound was obtained by 3D scanner, and the hierarchical structure of wound model was established. Then a geometric flattening method of wound surface was proposed based on the existing surface flattening theory. The concept of deformed point was introduced according to the special shape of wound surface, and the corresponding modification was given to the original flattening process. Secondly, the mechanical properties of pig skin samples with different orientations were measured by static tensile test. Finally, based on the morphological flattening of wound model and the mechanical parameters of pig skin, a unit material model based on material deformation energy was established. The unit deformation was attributed to the equivalent load acting on the node, and a finite element optimization method of wound unfolding shape based on material deformation energy was proposed. In order to optimize the overall deformation energy, the flap shape was optimized and adjusted to achieve the preoperative design. Clinical examples were selected for verification and analysis. The results show that the proposed method can provide a reasonable and reliable preliminary guide for preoperative flap shape design in clinical wound repair.