当前位置: X-MOL 学术J. Eng. Des. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Ontological model-based optimal determination of geometric tolerances in an assembly using the hybridised neural network and Genetic algorithm
Journal of Engineering Design ( IF 2.7 ) Pub Date : 2019-04-15 , DOI: 10.1080/09544828.2019.1605585
A. Saravanan 1 , J. Jerald 1
Affiliation  

Traditional Geometric tolerance allocation method assumes the parts of the assembly are purely rigid. Upon assembly and function, it is realised that the rigidity of the parts is non-ideal which leads to severe variations. Hence the performance of an assembly declines and is associated with the parts performance in the assembly. An assembly always experiences deformations due to internal and external forces/loads and drop in efficiency is critically observed. This paper proposes a methodology to predict such variations in assembly and integrate geometric tolerance design to it. First, the Ontological model of the assembly is predicted through Finite Element Analysis, and the near net shape of the assembly is obtained. Second, a set of features of an assembly which plays a vital role is selected, to determine the optimal geometric tolerances through the hybridised neural network and Genetic algorithm. Finally, a gear pump assembly is chosen, the proposed method is demonstrated. This method will be useful for design and new product development engineers in reducing the assembly variations and associated manufacturing cost.

中文翻译:

使用混合神经网络和遗传算法,基于本体模型优化确定装配中的几何公差

传统的几何公差分配方法假设装配的零件是纯刚性的。在组装和运行时,人们意识到零件的刚度不理想,这会导致严重的变化。因此,组件的性能下降并与组件中的零件性能相关。由于内部和外部力/负载,组件总是会发生变形,并且严格观察到效率下降。本文提出了一种方法来预测装配中的此类变化并将几何公差设计整合到其中。首先,通过有限元分析预测装配体的本体模型,得到装配体的近净形状。其次,选择起重要作用的组件的一组特征,通过混合神经网络和遗传算法确定最佳几何公差。最后,选择了齿轮泵组件,并演示了所提出的方法。这种方法将有助于设计和新产品开发工程师减少装配变化和相关的制造成本。
更新日期:2019-04-15
down
wechat
bug