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.

中文翻译：

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计算机技术在特种机械整体结构优化设计中的应用

随着我国产业结构的转型升级，制造业自动化水平逐步提高。根据研究，机械产品的设计通常是在现有设计知识的基础上通过改进或创新来完成的。知识重用是通过重用设计知识来确保最大程度地利用设计资源的技术。本文将知识重用技术应用于机械产品的开发和设计。通过将功能分析系统的技术逻辑与质量功能的开发相集成，实现了客户需求信息和产品功能设计的转换，并完成了产品设计计划分析阶段的任务。本文使用有限元分析软件ANSYS探索了一种新的非线性有限元建模方法，并进行了仿真实验。同时，对遗传算法进行了改进，有效提高了优化效率，完成了多目标，多结构条件下的参数优化。从实验结果来看，基本遗传算法需要328.64秒才能找到复杂问题的最优解。改进的时间缩短为86.31秒，效率提高了73.74％。这表明改进的遗传算法具有较好的鲁棒性，可以在较短的计算时间内找到最优解。对于复杂的问题，例如专用机械整体结构的优化，