Abstract

Material shrinkage commonly occurs in additive manufacturing and compromises the fabrication quality by causing unwanted distortions or residual stresses in fabricated parts. Even though it is known that the resulting deformations and stresses are highly dependent on the writing trajectory, no effective strategy for choosing suitable trajectories has been reported to date. Here, we present a path to achieve this goal in direct laser writing, an additive manufacturing method based on photopolymerization that commonly suffers from strong shrinkage-induced effects. First, we introduce a method for measuring the shrinkage of distinct direct laser written lines. We then introduce a semi-empirical numerical model to capture the interplay of sequentially polymerized material and the resulting macroscopic effects. Finally, we implement an artificial neural network to evaluate given laser trajectories in terms of the resulting part quality. The presented approach proves feasibility of using artificial neural networks to assess the quality of 3D printing trajectories and thereby demonstrates a potential route for reducing the impact of material shrinkage on 3D printed parts.

中文翻译：

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神经网络在直接激光笔迹中进行轨迹评估

摘要

材料收缩通常发生在增材制造中，并通过在制造的零件中引起不必要的变形或残余应力而损害制造质量。尽管已知所产生的变形和应力高度依赖于书写轨迹，但迄今为止，尚未有任何有效的策略来选择合适的轨迹。在这里，我们提出了在直接激光写入中实现此目标的途径，这是一种基于光聚合的增材制造方法，通常会遭受强烈的收缩诱导效应。首先，我们介绍一种测量不同的直接激光笔迹线收缩率的方法。然后，我们引入一个半经验数值模型来捕获顺序聚合的材料之间的相互作用以及由此产生的宏观效应。最后，我们实施了人工神经网络，根据零件质量评估给定的激光轨迹。提出的方法证明了使用人工神经网络评估3D打印轨迹质量的可行性，从而证明了减少材料收缩对3D打印零件的影响的潜在途径。