Laser Powder Bed Fusion Process and Structure Data Set for Process Model Validations

IF 2.4 3区 材料科学 Q3 ENGINEERING, MANUFACTURING
Nathaniel Wood, Edwin Schwalbach, Andrew Gillman, David J. Hoelzle
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引用次数: 0

Abstract

This work reports the measurement of laser powder bed fusion (PBF) process input signals, output signals, and structural data for a set of eight IN 718 samples. Data from multiple samples imparts statistical replicability to the measurements. The input signals are the real-time PBF laser position commands, power commands, and the beam radius set point. The output signals are thermographic videos from coaxial and off-axis infrared cameras, and temperature measurements from thermocouples embedded in the samples. The structural data are optical micrographs of all built surfaces. Data are collected for three testing regimes: First, the laser rasters over the samples under conditions that do not induce melting. Second, the laser rasters over the samples with conditions that induce melting. Lastly, five layers of IN 718 are built atop the samples. The main result is an open and comprehensive data set, comprising both raw and processed signal data, for validating PBF process and structure models.

Abstract Image

用于工艺模型验证的激光粉末床融合工艺和结构数据集
本文报道了激光粉末床熔融(PBF)过程输入信号、输出信号和一组8个IN 718样品的结构数据的测量。来自多个样本的数据赋予了测量的统计可重复性。输入信号为实时PBF激光位置命令、功率命令和光束半径设定点。输出信号是来自同轴和离轴红外摄像机的热成像视频,以及嵌入样品中的热电偶的温度测量。结构数据是所有建筑表面的光学显微照片。收集的数据用于三个测试机制:首先,激光光栅在不诱导熔化的条件下对样品进行检测。其次,激光光栅在诱导熔化的条件下照射样品。最后,在样品上构建了五层IN 718。主要结果是一个开放和全面的数据集,包括原始和处理过的信号数据,用于验证PBF过程和结构模型。
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来源期刊
Integrating Materials and Manufacturing Innovation
Integrating Materials and Manufacturing Innovation Engineering-Industrial and Manufacturing Engineering
CiteScore
5.30
自引率
9.10%
发文量
42
审稿时长
39 days
期刊介绍: The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.
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