FATA-ResNet Network for CAD/CAM Integration in Cloud Manufacturing

This paper focuses on the application of mechanical engineering CAD/CAM integration technology under the cloud manufacturing framework, aiming at solving the current technical integration problems in manufacturing informatization. The study analyzes the demand and current situation of 3D CAD/CAM integration in a cloud manufacturing environment, combines the mirage optimization algorithm (FATA) and residual neural network (ResNet), and proposes a CAD/CAM integration application analysis model based on the FATA-ResNet network. Firstly, the functional requirements of CAD/CAM technology integration in a cloud manufacturing platform are clarified, including 3D model uploading and downloading, process file generation, and cross-platform data sharing. Then, the hyperparameters of the ResNet network are optimized by the FATA algorithm to improve the accuracy and efficiency of the model in integration application analysis. The experimental results show that the FATA-ResNet model outperforms the traditional model in terms of accuracy, recall, and F1 score while possessing faster convergence speed and higher computational efficiency. In addition, the operation modules in the cloud platform, including the task management interface and 3D process editing function, were designed and validated, further demonstrating the practicality of the method. Future research will focus on the validation of multi-scene data, model resource optimization, and real-time collaborative operation to promote the in-depth application of CAD/CAM technology in intelligent manufacturing and provide support for the digital and intelligent development of manufacturing.