基于尺度和物料浓度效应的LPBF工艺尺寸偏差预测模型

S. Ben Amor, Floriane Zongo, B. Louhichi, Antoine Tahan, V. Brailovski
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引用次数: 1

摘要

增材制造(AM)工艺在不使用成型工具的情况下逐层生成零件。由此产生的优势突出了增材制造成为产品开发固有部分的能力。然而,工艺方面的挑战,如高表面粗糙度、阶梯效应或尺寸偏差,阻碍了AM在工业规模上的建立。因此,增材制造零件通常需要使用既定的制造工艺进行后处理。在增材制造中,许多工艺参数和几何因素影响着尺寸精度。与这些偏差相关的已发表结果也难以比较,因为它们是基于使用不同工艺、材料和机器设置制造的几种几何形状。激光粉末床融合(LPBF)越来越受欢迎,但面临其更大的工业应用的障碍之一是有限的知识,其尺寸和几何性能。因此,使用它需要研究工艺并提高所涉及零件的精度。本文提出了一种预测LPBF零件尺寸偏差的新方法。在项目中,尺度和材料浓度相关的现象在一个新的图像分析模型中实现,并应用于建成部分。我们将所提出的模型的结果与数值分析和实验结果进行了比较。该模型不采用有限元分析,计算时间短,预测精度合理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dimensional Deviation Prediction Model Based on Scale and Material Concentration Effects for LPBF Process
Additive Manufacturing (AM) processes generate parts layer-by-layer without using formative tools. The resulting advantages highlight the capability of AM to become an inherent part of product development. However, process-specific challenges such as high surface roughness, the stair-stepping effect, or dimensional deviations inhibit the establishment of AM at the industrial scale. Thus, AM parts often need to be post-processed using established manufacturing processes. Many process parameters and geometrical factors influence the dimensional accuracy in AM. Published results relating to these deviations are also difficult to compare because they are based on several geometries that are manufactured using different processes, materials, and machine settings. Laser Powder Bed Fusion (LPBF) is gaining in popularity, but one of the obstacles facing its larger industrial use is the limited knowledge of its dimensional and geometrical performances. Therefore, using it requires studying the process and improving the accuracy of the parts involved. This paper represents a new attempt to predict dimensional deviations of LPBF parts. During the project, the scale- and material concentration-related phenomena were implemented in a new image analysis model and applied to the as-built part. We carried out a comparison between the results of the proposed model with those obtained from numerical analyses and experiments. The model does not use finite element analysis, takes less time to compute, and provides reasonable prediction accuracy.
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