Mechanical product design and manufacturing system based on CNN and server optimization algorithm

Abstract Mechanical optimization refers to the use of advanced mechanical equipment and physical processes to optimize raw materials, products, etc. , in production, reduce energy consumption, and improve production efficiency and product quality. In the cloud manufacturing mode of manufacturing enterprises, the design of mechanical products requires server optimization based on the demand side. The existing server optimization algorithms are not intelligent enough for server discovery and optimization in this mode. A mechanical product design service and manufacturing method based on convolutional neural network and server discovery and optimization architecture is proposed, which combines deep learning and optimal path algorithm to select better mechanical product services. Research outcomes showed that the training accuracy and testing accuracy of the research network were 98.89 and 96.88%, respectively, with training loss and testing loss of 0.04619 and 0.08921, respectively. In contrast, the training accuracy and testing accuracy of the comparative network were 93.95 and 85.31%, respectively, with training loss and testing loss of 0.3556 and 0.4872. The running time of the two neural networks is 105 and 73 min, respectively. Overall, the accuracy of the research network is high, and the loss is small. The training accuracy and loss of the new activation function proposed in the study have always been superior to other functions. The path and running time of the server optimization path algorithm have better performance. It can be seen that the research proposed methods have good performance and advantages in mechanical product design services and optimization, which can provide technical references and directions for the development of manufacturing service-oriented enterprises in cloud manufacturing mode.