D-type shaft is widely used in precision machinery products such as motors and intelligent robots. The straightness of the D-type shaft is an important factor influencing its machining accuracy and dynamic performance, which is normally improved by the three-point pressure straightening process. This paper proposes a general stroke-based model to predict the relevant parameters for the straightening process of D-type shaft, considering the bending deformations in three dimensions. The distribution of stress and strain inside the D-type shaft during the straightening process in arbitrary position of the cross section and the bending moment are analyzed by using linear hardening material model. The relationship between deflection and the internal stress on the loading position is explored, and a straightening stroke model of D-type shaft is obtained. The correctness of the stroke-based straightening model has been validated by finite element method (FEM) simulation analysis and bending experiments. The results show that the proposed model can improve the accuracy and efficiency of the D-type shaft straightening process. Furthermore, it provides a novel method for the modelling of the straightening process regarding the special shaped bar stock.

Finding a reliable quality inspection system of resistance spot welding (RSW) has become a very important issue in the automobile industry. In this study, improvement in the quality estimation of the weld nugget’s surface on the car underbody is introduced using image processing methods and training a fuzzy inference system. Image segmentation, mathematical morphology (dilation and erosion), flood fill operation, least-squares fitting curve and some other new techniques such as location and value based selection of pixels are used to extract new geometrical characteristics from the weld nugget’s surface such as size and location, shape, and the numbers and areas of all side expulsions, peaks and troughs inside and outside the fusion zone. Topography of the weld nugget’s surface is created and shown as a 3D model based on the extracted geometrical characteristics from each spot. Extracted data is used to define input fuzzy functions for training a fuzzy logic inference system. Fuzzy logic rules are adopted based on knowledge database. The experiments are conducted on a 6 degree of freedom (DOF) robotic arm with a charge-coupled device (CCD) camera to collect pictures of various RSW locations on car underbodies. The results conclude that the estimation of the 3D model of the weld’s surface and weld’s quality can reach higher accuracy based on our proposed methods.

No abstract

No abstract

The high temperature deformation law of nitriding steel 25Cr5MoA over the strain rate range 0.001S-1~20S-1and temperature range 850°C to 1150°C was studied in the thermal simulation testing machine Gleeble-1500. Under a certain strain rate and a certain deformation degree, the flow stress decreased with the increase of deformation temperature. Work hardening of nitriding steel 25Cr5MoA was strong when the true strain was less than 0.2, otherwise the flow stress increased slowly, even dropped. High temperature deformation flow stress of nitriding steel 25Cr5MoA was influenced by the deformation temperature and strain rate. When the strain rate was 0.1S-1, true stress-true strain curve exhibited a dynamic recrystallization model, and with the increase of deformation temperature, peak flow stress shift left. When deformation degree was 0.69, the strain rate was 1S-1, and when deformation temperature was in the region of 850°C~1050°C, true stress-true strain curve exhibited a dynamic recovery model. And when the deformation temperature was in the region of 1100°C~1150°C, it showed a dynamic recrystallization model. Dynamic recrystallization diagrams of nitriding steel 25Cr5MoA were also established.