Machine parts always work under different types of stresses or loading conditions. The sudden dimensional changes lead to sudden increase in stress on the critical section of the machine parts. This state is defined as stress concentration factor (SCF). The strength of the machine part decreases on the located point at maximum stress. In this study, SCFs of circular/elliptical holes with bead reinforcement in an infinite panel under uniaxial and biaxial stresses were modeled, simulated, calculated and verified using analytical, finite element analysis (FEA) and artificial neural networks (ANN) techniques. This study presents a new model for predicting SCF for panels using artificial intelligence techniques under uniaxial and biaxial loading conditions. Theoretical SCF (Kt) values for different shapes and loading conditions have been compiled and graphical forms of these experimental results were published by Peterson (Peterson’s stress concentration factors, 3rd edn. Wiley, Hoboken, 2007). There is a need to convert these curves into a numerical data set to be used in machine part design easily. Theoretical SCF (Kt) was obtained from the Peterson’s charts in numerical form according to dimensional parameter ratios using high-accuracy graphical computer software. Using these dimensional parameter ratios, a parametric finite element model (FEM) was created. Uniaxial and biaxial boundary conditions were applied to the FEM model. Mesh optimization was accomplished to the parametric FEM model. Mesh formation is compatible with the model according to dimensional sizes and ratios. Mesh optimization was provided to element size and per unit volume. Numerical and FEA results were tested, approved and confirmed with Peterson’s original data using statistical methods. A code was created for the improvement of the ANN model in the Matlab ANN Toolbox Editor. Different ANN model variations were tested and the best-performing ANN model was determined among the tried models. Numerical, FEA and ANN model results were compared and confirmed with one another. The developed model provides an easy method to predict and calculate the stress in defining the Kt according to dimensional ratios and applied loading states (uniaxial/biaxial).

机器零件始终在不同类型的压力或负载条件下工作。尺寸的突然变化导致在机器零件的关键部分上的应力突然增加。该状态定义为应力集中系数（SCF）。在最大应力下，机器零件的强度会在定位点处降低。在这项研究中，使用分析，有限元分析（FEA）和人工神经网络（ANN）技术对在单轴和双轴应力下无限面板中带有加强筋的圆形/椭圆孔的SCF进行建模，模拟，计算和验证。这项研究提出了一种新的模型，用于在单轴和双轴载荷条件下使用人工智能技术预测面板的SCF。理论SCF（Kt彼得森（Peterson's Stress Concentration Factors，3rd edn.Wiley，Hoboken，2007）收集了不同形状和载荷条件下的数值，并以图形形式发布了这些实验结果。需要将这些曲线转换为易于在机械零件设计中使用的数值数据集。理论SCF（Kt）是使用高精度图形计算机软件根据尺寸参数比率从Peterson图表中以数字形式获得的。使用这些尺寸参数比率，创建了参数化有限元模型（FEM）。将单轴和双轴边界条件应用于有限元模型。对参数有限元模型完成了网格优化。根据尺寸大小和比例，网格的形成与模型兼容。网格优化提供了元素大小和单位体积。使用统计方法，使用Peterson的原始数据对数字和FEA结果进行了测试，认可和确认。在Matlab ANN工具箱编辑器中创建了用于改进ANN模型的代码。测试了不同的ANN模型变体，并在尝试的模型中确定了性能最佳的ANN模型。数值，有限元分析和人工神经网络模型的结果进行了比较并相互确认。开发的模型提供了一种简单的方法来预测和计算根据尺寸比和所施加的载荷状态（单轴/双轴）定义Kt时的应力。