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A machine vision system (MVS) is a technology that can analyze and recognize still or moving pictures using a computer. It is a branch of computer vision that looks like a security camera but can automatically capture, evaluate, and analyze images. The drawbacks are obvious. In the event of a computer vision system failure, firms must have a team of highly trained people with a thorough understanding of the distinctions. Artificial neural networks with numerous layers between the input and output layers are deep neural networks (DNN). Neurons, synapses, weights, biases, and functions are all part of any neural network, regardless of the kind. Many of the challenges in computer vision revolve around using convolutional neural networks (CNN) to categorize images into predefined categories. Convolutional and pooling layers were utilized to decrease the image’s size before feeding the reduced data to fully connected layers. According to the paper, the MVS-CNN algorithm can analyze a picture and determine the value of various characteristics and objects inside it. It is called convolution when combining two functions to create a third function. It is a fusion of two different datasets. A CNN performs convolution on the input data to build a feature map using a filter or kernel. Using a convolutional neural network, an inverted residual block is introduced as the basic block to balance identification accuracy and processing efficiency. The suggested method’s higher inspection performance is achieved with a huge dataset of photos of faulty and defect-free bottles. The result is obtained from the proposed method, the standard deviation ratio is 83.56%, absolute error ratio is 77.26%, trajectory length difference ratio is 82.35%, source pattern radiation amplitude ratio is 86.25%, classification of accuracy ratio is 83.25%, and finally, overall percentage performance ratio is 90.26%.

Abstract-The crank-group driving mechanism includes a group of redundant constraints. This mechanism can move as a driving mechanism under ideal conditions, but it may be stuck because of the processing errors in practical applications. So, some clearances are reserved between the pin hole and the pin to ensure that the crank-group driving mechanism can achieve normal movement, but these clearances inevitably affect the dynamic performance of the output member. In this paper, the dynamic simulation model of the crank-group driving mechanism is established by NX/Motion Simulation which is based on the 3-D model of planar link mechanism. The comparative analysis is made about the dynamic characteristics of the mechanism which is influenced by different clearances, different angular velocity, different damping, and make a qualitative analysis how the clearances affects the dynamic performance of the output member. The simulation results also show that appropriately increasing the angular velocity, increasing stiffness and damping of the components can effectively inhibit the adverse influences of clearance on the dynamic characteristics of the mechanism.

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