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With the transformation and upgrading of my country’s industrial structure, the level of manufacturing automation has gradually improved. According to research, the design of mechanical products is mostly completed by improvement or innovation on the basis of existing design knowledge. Knowledge reuse is a technique to ensure the maximization of design resource utilization by reusing design knowledge. This article applies knowledge reuse technology to the development and design of mechanical products. By integrating the technical logic of the functional analysis system with the development of quality functions, the transformation of customer demand information and product function design is realized, and the task of the product design plan analysis phase is completed. This paper uses the finite element analysis software ANSYS to explore a new nonlinear finite element modeling method and conducts simulation experiments. At the same time, this paper improves the genetic algorithm, which effectively improves the optimization efficiency and completes the parameter optimization under multiobjective and multistructure conditions. From the experimental results, it takes 328.64 seconds for the basic genetic algorithm to search for the optimal solution of the complex problem. The improved time is shortened to 86.31 seconds, and the efficiency is increased by 73.74%. This shows that the improved genetic algorithm has better robustness and can find the optimal solution in a shorter calculation time. For complex problems such as the optimization of the overall structure of special machinery, the improved genetic algorithm obviously helps to improve the optimization efficiency and improves the effectiveness and pertinence of product design schemes.

Diabetes is a chronic disease that is characterized by insufficient production or utilization of insulin and a consequent high increase in blood sugar. Diagnosis of diabetes is a complex process and requires a high level of expertise. The disease is characterized by a set of signs and symptoms. Some of these symptoms are obtained through laboratory analysis. Creation of a knowledge base and automation of disease diagnosis are important and allow fast detection and treatment. Various techniques have been used to develop a high-accuracy system for the diagnosis of diabetes. Fuzzy logic is one of the appropriate methodologies for the development of such medical diagnostic systems. Several research studies have used fuzzy models to diagnose medical diseases due to the imprecision and uncertainty associated with medical data. Moreover, a high level of uncertainty in medical data requires a type-2 fuzzy system to handle these uncertainties and diagnose diabetes. The paper proposes the integration of a type-2 fuzzy system and neural networks for the diagnosis of diabetes. Using the structure of type-2 fuzzy neural network (T2FNN) and statistical data, the system’s design for the diagnosis of diabetes is performed. A number of simulations have been done in order to evaluate the performance of the designed system. The comparative results demonstrated the efficiency of using the T2FNN system in the diagnosis of diabetes. The physician can use the system for diabetes’ diagnosis.