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首页> 外文期刊>Journal of Manufacturing Processes >Hatch pattern based inherent strain prediction using neural networks for powder bed fusion additive manufacturing
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Hatch pattern based inherent strain prediction using neural networks for powder bed fusion additive manufacturing

机译：基于孵化床融合添加剂制造的神经网络的固有应变预测

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摘要

Additive Manufacturing has recently emerged as an important industrial process that is capable of manufacturing parts with complex geometry. One of the drawbacks of metal additive manufacturing processes is the thermo-mechanical distortion of the parts during and after build due to heat effects. Inherent strain is widely adopted by researchers as the basis to predict part distortions during Metal Powder Bed Fusion Additive Manufacturing (PBFAM) process and is highly dependent on the laser hatch pattern sintering on each layer during the printing process. There is a clear need to predict inherent strains for a given arbitrary hatch pattern for a part model so that hatch patterns can be optimized for achieving part quality. In this paper, we propose a neural network based method to predict inherent strain for any given hatch pattern that is adopted during the part build. The authors assumed that the temperature profile inside the heat affected zone within each layer is the same if the part model is reasonably large. To start with, inherent strains of two hatch pattern pools with different hatch angles were obtained by thermo-mechanical simulation with temperature profiles obtained through translation and rotation of a single layer of simulation. A feedforward backpropagation neural network was created and trained with data obtained from an initial hatch pattern pool for predicting inherent strains. The data from a second hatch pattern pool was then utilized to validate the network and test the efficacy of the prediction of the trained neural network. The results show that the trained neural network is capable of predicting the inherent strain of any arbitrary hatch pattern within an acceptable error. Since the trained neural network can predict inherent strain quickly for any given hatch pattern, this could provide the basis for hatch pattern optimization of any part model to increase part build accuracy and achieve part GD&T callouts.
机译：添加剂制造最近被出现为一个重要的工业过程，能够制造具有复杂几何形状的部件。金属添加剂制造工艺的缺点之一是由于热效应而在构建期间和之后的部件的热机械变形。研究人员被广泛采用固有的菌株作为预测金属粉末融合添加剂制造（PBFAM）工艺期间的部件畸变的基础，并且在印刷过程中高度依赖于每层的激光舱口图案烧结。有明确需要预测部分模型的给定任意舱口图案的固有菌株，从而可以优化舱口图案以实现部分质量。在本文中，我们提出了一种基于神经网络的方法来预测部分构建过程中采用的任何给定的舱口图案的固有菌株。作者认为，如果部件模型相当大，则每个层内的热影响区域内的温度曲线是相同的。为了开始，通过通过平移和旋转通过平移和旋转，通过平移和旋转单一模拟来获得具有不同舱口角的两个具有不同舱口角度的舱口型池的固有菌株。使用从初始舱口图案池获得的数据创建和培训前馈回来神经网络，用于预测固有菌株。然后利用来自第二个舱口图案池的数据来验证网络并测试培训的神经网络的预测的效果。结果表明，训练有素的神经网络能够在可接受的误差中预测任何任意舱口图案的固有菌株。由于训练有素的神经网络可以快速预测任何给定的舱口模式，这可以为任何部件模型提供舱口舱模式优化的基础，以增加部分构建精度并实现部分GD＆T标注。

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