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An integrated FEM-ANN model for laser bending process with inverse estimation of absorptivity

机译:具有吸收率反估计的激光弯曲过程的集成FEM-ANN模型

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Abstract Background Absorption of laser energy into the worksheet surface during laser bending process is an important and critical factor for accurate computation of the bend angle. This paper presents an integrated FEM-ANN approach to compute accurate value of bend angle during laser bending process. Methods Initially, a finite element method (FEM) based three-dimensional nonlinear transient thermo-mechanical numerical model is developed using ABAQUS package. Using FEM model and data obtained in actual experiments, the proper values of absorptivity for various sets of process conditions are computed by inverse analysis technique. Based on the proper values of absorptivity, an artificial neural network (ANN) model is developed for accurate and quick prediction of absorptivity for given input process conditions. The predicted absorptivity is then employed in the FEM model for accurate computation of bend angle. Results The performance of the integrated approach is verified by conducting experiments. Conclusion The verification results showed that the proposed approach is able to compute the bend angle with a very good accuracy (average prediction error of 4.14?%). The proposed approach can also be suitable for the numerical simulations of other laser based manufacturing processes.
机译:摘要背景激光弯曲过程中激光能量吸收到工件表面是准确计算弯曲角度的重要和关键因素。本文提出了一种集成的FEM-ANN方法来计算激光弯曲过程中弯曲角度的准确值。方法最初,使用ABAQUS软件包开发了基于有限元方法(FEM)的三维非线性瞬态热力学数值模型。利用有限元模型和在实际实验中获得的数据,通过逆分析技术计算出各种工艺条件下的吸收率的适当值。基于适当的吸收率值,开发了一个人工神经网络(ANN)模型,用于在给定的输入过程条件下准确,快速地预测吸收率。然后将预测的吸收率用于FEM模型中,以精确计算弯曲角度。结果通过实验验证了集成方法的性能。结论验证结果表明,该方法能够很好地计算出弯角(平均预测误差为4.14%)。所提出的方法也可以适用于其他基于激光的制造过程的数值模拟。

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