An electric motor driven by the electromechanical system of the Internet of Things is attractive because of its long life capability of the propulsion system. In this paper, the application of collaborative design and manufacturing in the design automation of IOT electromechanical system is reviewed, and the application of collaborative design and manufacturing in robots, a typical IOT electromechanical system, is described in detail. In this paper, we explain five aspects including the construction of a multiangle unified modeling method for the electromechanical system of the Internet of Things; the constraint processing mechanism for the optimization problem of the electromechanical system of the Internet of Things; the constraint multiobjective optimization methods; design methods that integrate constraint multipurpose evolutionary algorithms and knowledge extraction; and design automation of visual perception systems for electromechanical systems based on the Internet of Things and deep neural networks. The research shows that under the control of a conventional radial basis function neural network controller and the control of a radial basis function neural network controller based on the electromechanical system of the Internet of Things, the system will be affected to a certain extent when there is interference. Under the control of a traditional RBF neural network controller, the system requires 0.18 seconds to restore stability. When using the RBF neural network controller based on the electromechanical system of the Internet of Things, the system returns to a stable state after 0.09 s, and the peak time is reduced by 59% compared with the conventional RBF neural network controller.
A design for manufacturing methodology using defects knowledge for aerospace product manufacturing
