沟槽(工程)
人工神经网络
有限元法
结构工程
压力(语言学)
半径
应力集中
工程类
机械工程
计算机科学
算法
人工智能
语言学
哲学
计算机安全
作者
Hakan Kaplan,İhsan Toktaş,Metin Özkan
摘要
The stress concentration factor (SCF) is important for design of machine components. Especially designing machine components, some geometrical shapes may occur such as grooves, notches, holes and curves. Working components of machine are deformed. Thus, SCF should be thought when designing the machine equipment. Material selection is so important for stable system design which deals with the stress concentration. Generally, the reasons of SCF depend on the design features. In this research, a deep hyperbolic groove is analyzed in terms of SCF. There are several methods to examine the SCF. These methods can be named such as Numerical Analysis, Finite Element Analysis (FEA), Experimental Analysis, Artificial Neural Network (shortly ANN) Methods etc. One of them is known as ANN model which is used in this research. ANN model is used for determination of SCF for an infinite three-dimensional groove. Groove shape types and dimensions can be varied. This is impossible to do experiment or do numerical analysis for every each design shape. ANN is economical and useful method for determination of SCF. While use the some of data of numerical or experimental results, the other types of grooves SCF can be determined with high reliability. The ANN works with different learning algorithms. Neuron is the main element of ANN. The infinite plate is exposed to torsional loading. Meanwhile groove radius and differences of dimensions between two grooves are changed. Analytical, empirical (Peterson’s) and ANN model results have been obtained, and error calculations have been analyzed with using these results. ANN model demonstrates that, fast and accurate results can be obtained for SCF of hyperbolic groove.
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