Powder-based additive manufacturing has transformed the manufacturing industry over the last decade. In Laser Powder Bed Fusion, a specific part is built in an iterative manner in which two-dimensional cross-sections are formed on top of each other by melting and fusing the proper areas of the powder bed. In this process, the behavior of the melt pool and its thermal field has a very important role in predicting the quality of the manufactured part and its possible defects. However, the simulation of such a complex phenomenon is usually very time-consuming and requires huge computational resources. Flow-3D is one of the software packages capable of executing such simulations using iterative numerical solvers. In this work, we create three datasets of single-trail processes using Flow-3D and use them to train a convolutional neural network capable of predicting the behavior of the three-dimensional thermal field of the melt pool solely by taking three parameters as input: laser power, laser velocity, and time step. The CNN achieves a relative Root Mean Squared Error of 2% to 3% for the temperature field and an average Intersection over Union score of 80% to 90% in predicting the melt pool area. Moreover, since time is included as one of the inputs of the model, the thermal field can be instantly obtained for any arbitrary time step without the need to iterate and compute all the steps
翻译:在过去十年中,基于粉末的添加剂制造业改变了制造业。在激光粉末贝化聚合中,一个特定部分以迭接方式建成,通过熔化和阻燃粉粉床的适当区域,将两维交叉截面相互叠加在相互上方形成。在这一过程中,熔化池及其热场的行为在预测制造部件的质量及其可能的缺陷方面起着非常重要的作用。然而,模拟这种复杂现象通常非常耗时,需要巨大的计算资源。流程-3D是能够使用迭代数字解答器进行模拟的软件包之一。在这项工作中,我们用流-3D创建了三个单轨进程数据集,并用它们来训练一个能够预测熔化池三维热场行为及其可能缺陷的卷心网络。仅以三种参数作为投入:激光能力、激光速度和时间步骤。CNNC在温度场中实现了2%至3%的相对根平方错误,而平均截面在联盟内进行这种模拟。在这个过程中,我们用三维截面截面截面截面的一组数据集,使用三维至90%的直径直径直径直径直径直射场,因此可以预测一个磁场。