Experimental techniques and identification of dynamic properties of flexible polyurethane foam used in automotive seats are described here. In the experimental fixture a rigid block is mounted on a 3 inch cube of foam material, which serves as the only flexible component. The dynamic system is modeled as a single degree-of -freedom system. The experimental fixture is excited by a small impulse at the top of the block and by a shaker on the basement. For the first situation the foam is simplified as a linear viscoelastic material. By exciting with the shaker the nonlinear and viscoelastic properties of foam materials are considered. For these two different cases not only the model response and the impact of model parameters are analyzed but also the corresponding system parameters are identified during the experimental data.

Accurate polyp image segmentation is of great significance, because it can help in the detection of polyps. Convolutional neural network (CNN) is a common automatic segmentation method, but its main disadvantage is the long training time. Transformer is another method that can be adapted to the automatic segmentation method by employing a self-attention mechanism, which essentially assigns different importance weights to each piece of information, thus achieving high computational efficiency during segmentation. However, a potential drawback with Transformer is the risk of information loss. The study reported in this paper employed the well-known hybridization principle to propose a method to combine CNN and Transformer to retain the strengths of both. Specifically, this study applied this method to the early detection of colonic polyps and to implement a model called MugenNet for colonic polyp image segmentation. We conducted a comprehensive experiment to compare MugenNet with other CNN models on five publicly available datasets. An ablation experiment on MugenNet was conducted as well. The experimental results showed that MugenNet can achieve a mean Dice of 0.714 on the ETIS dataset, which is the optimal performance on this dataset compared to other models, with an inference speed of 56 FPS. The overall outcome of this study is a method to optimally combine two methods of machine learning which are complementary to each other.

In a Metal-to-Metal Contact (MMC) bolted flange joint the stress of sealing surfaces is constant. It canwithstand higherbolt load. So, under the operating conditions of high temperature, high pressure or their fluctuations, the sealing effect of MMC bolted flange joints is better than of floating (FLT) bolted flange joints. According to the structure characteristics of MMC bolted flange joints, a new tightening method (SH-Method) was recommended in this work.

Threaded fastening operations are widely used in assembly and are typically time-consuming and costly. In low-volume, high-value manufacturing, fastening operations are carried out manually by skilled workers. The existing approaches are found to be less flexible and robust for performing assembly in a less structured industrial environment. This paper introduces a novel algorithm for detecting the position and orientation of threaded holes and a new method for tightening bolts. First, the elliptic arc fitting method and the three-point method are used to estimate the initial position and orientation of the threaded hole, and the force impact caused by switching from the free space to the constrained space during bolt tightening is solved. Second, by monitoring the deformation of passive compliance, the position information is introduced into the control process to better control the radial force between the bolt and the threaded hole in the tightening process. The constant force controller and orientation compliance controller are designed according to the adaptive control theory. A series of experiments are carried out. The results show that the proposed method can estimate the initial position and orientation of an M24 bolt with an average position error of 0.36 mm, 0.43 mm and 0.46 mm and an orientation error of 0.65°, 0.46° and 0.59°, and it can tighten the bolt with a success rate of 98.5%.

The sealing performance of a brush seal is studied in this article. At present, the mostly used model to analyze the performance of a brush seal is porous medium model in which the effect of bristle deformation is not considered. Here, a combined numerical method is proposed. First, the deformation of bristle is calculated in a fluid–solid coupling model with a simplified bristle model, and then the results of the bristle deformation is imported to a porous media model as the boundary conditions. More accurate media flow and leakage variation law of the brush seal are obtained with this calculation model.

Backlash is a commonly non-linear phenomenon, which can directly degrade the control accuracy of a pneumatic control valve. To explain the cause and law of backlash error, and to propose an effective method, many research works on the modeling of a pneumatic control valve system have been carried out. The currently model of a control valve system can be classified as a physical model, data-driven model, and semi-physical model. However, most models only consider the force-displacement conversion process of a pneumatic diagram actuator in a pneumatic control valve system. A physical model based on the whole workflow of the pneumatic control valve system is established and a control method to eliminate the backlash error is proposed in this paper. Firstly, the physical model of the pneumatic control valve system is established, which is composed of three parts: pneumatic diaphragm actuator model, nozzle-flapper structure model and electromagnetic model. After that, the input–output relationship of the pneumatic control valve system can be calculated according to the established physical model, and the calculation results are consistent with the experimental result. Lastly, a self-calibration PID (SC-PID) control method is proposed for backlash error elimination. The proposed method can solve valve stem oscillation caused by backlash during valve control.