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This study investigated the three-dimensional (3D) printing of tubular tissue, especially vascular tissue, using a self-developed 3D bioprinter platform and tubular tissue support frame system based on machine vision technology. A 3D printing quality inspection scheme for tubular tissue based on machine vision was proposed by combining the current advanced image acquisition sensor device and theoretical and experimental analysis to measure the printing area in real time. A quantitative relationship between the quality of the tissue profile and the angle and brightness of tissue printed by hydrogel was established by changing the process parameters. A mathematical model for the visual inspection of tissue contour quality was established to realize its visual inspection and evaluation. This method can monitor the quality status of the printing target in real time and provide a basis for improving the accuracy of 3D bioprinting of tubular tissue and shortening the printing time.

Ultrasonic Welding is a popular welding procedure that uses high-frequency energy to heat joints. It is a complicated process involving a number of variable parameters that can each greatly modify the final weld product. A number of Artificial Intelligence (AI) technologies have thus been employed to regress and classify results such as weld parameters such as failure load, weld quality and joint strength on the basis of different parameters including power output, annealing temperature and vibration amplitude. Artificial neural network models are the most popular and adept at weld modeling on varying materials and composites. This paper reviews and compares the materials, feature extraction techniques and AI architectures and their performances on predicting a host of welding objectives.

Aiming at the key quality indexes xbt in spinning processing is caused by many complex and interactions factors. A xbt prediction model is put forward based on the PSO-BP neural network, which adjusts weights of BP neural network using particle swarm optimization (PSO) rather than the traditional gradient descent method, is used to improve the convergence speed of neural network and the ability of getting the global optimal solution. As the object of a large number of field detection data in a spinning workshop, the results show that, compared with the traditional BP algorithm and GA-BP algorithm, the PSO-BP neural network can obvious improve yarn quality prediction model precision and stability.