Data-Driven Sensitivity Analysis for Static Mechanical Properties of Additively Manufactured Ti–6Al–4V

IF 1.8 Q2 ENGINEERING, MULTIDISCIPLINARY
Antriksh Sharma, Jie Chen, Evan Diewald, A. Imanian, J. Beuth, Yongming Liu
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引用次数: 5

Abstract

Additive manufacturing (AM) has been extensively investigated in recent years to explore its application in a wide range of engineering functionalities, such as mechanical, acoustic, thermal, and electrical properties. A data-driven approach is proposed to investigate the influence of major fabrication parameters in the laser-based additively manufactured Ti–6Al–4V. Two separate laser-based powder bed fusion techniques, i.e., selective laser melting (SLM) and direct metal laser sintering (DMLS), have been investigated and several data regarding the tensile properties of Ti–6Al–4V alloy with their corresponding fabrication parameters are collected from open literature. Statistical data analysis is performed for four fabrication parameters (scanning speed, laser power, hatch spacing, and powder layer thickness) and three postfabrication parameters (heating temperature, heating time, and hot isostatically pressed or not) which are major influencing factors and have been investigated by several researchers to identify their behavior on the static mechanical properties (i.e., yielding strength, ultimate tensile strength, and elongation). To identify the behavior of the relationship between the input and output parameters, both linear regression analysis and artificial neural network (ANN) models are developed using 53 and 100 datasets for SLM and DMLS processes, respectively. The linear regression model resulted in an average R squared value of 0.351 and 0.507 compared to 0.908 and 0.833 in the case of nonlinear ANN modeling for SLM and DMLS based modeling, respectively. Both local and global sensitivity analyses are carried out to identify the important factors for future optimal design. Based on the current study, local sensitivity analysis (SA) suggests that SLM is most sensitive to laser power, scanning speed, and heat treatment temperature while DMLS is most sensitive to heat treatment temperature, hatch spacing, and laser power. In the case of DMLS fabricated Ti–6Al–4V alloy, laser power, and scan speed are found to be the most impactful input parameters for tensile properties of the alloy while heating time turned out to be the least affecting parameter. The global sensitivity analysis results can be used to tailor the alloy's static properties as per the requirement while results from local sensitivity analysis could be useful to optimize the already tailored design properties. Sobol's global sensitivity analysis implicates laser power, heating temperature, and hatch spacing to be the most influential parameters for alloy strength while powder layer thickness followed by scanning speed to be the prominent parameters for elongation for SLM fabricated Ti–6Al–4V alloy. Future work would still be needed to eradicate some of the limitations of this study related to limited dataset availability.
增材制造Ti-6Al-4V静态力学性能的数据驱动灵敏度分析
近年来,人们对增材制造(AM)进行了广泛的研究,以探索其在机械、声学、热学和电学等广泛工程功能中的应用。提出了一种数据驱动的方法来研究激光增材制造Ti-6Al-4V过程中主要工艺参数的影响。研究了两种不同的激光粉末床熔合技术,即选择性激光熔化(SLM)和直接金属激光烧结(DMLS),并从公开文献中收集了有关Ti-6Al-4V合金的拉伸性能及其相应的制造参数的数据。统计数据分析了四个制造参数(扫描速度、激光功率、缝隙间距和粉末层厚度)和三个制造后参数(加热温度、加热时间和热等静压与否),这是主要的影响因素,并由几位研究人员进行了研究,以确定它们对静态力学性能(即屈服强度、极限拉伸强度和伸长率)的行为。为了确定输入和输出参数之间的关系,分别使用53个和100个数据集对SLM和DMLS过程进行了线性回归分析和人工神经网络(ANN)模型。线性回归模型的平均R平方值分别为0.351和0.507,而基于SLM和DMLS的非线性神经网络模型的平均R平方值分别为0.908和0.833。进行了局部和全局敏感性分析,以确定未来优化设计的重要因素。基于目前的研究,局部灵敏度分析(SA)表明,SLM对激光功率、扫描速度和热处理温度最为敏感,而DMLS对热处理温度、舱口间距和激光功率最为敏感。在DMLS制备的Ti-6Al-4V合金中,激光功率和扫描速度是影响合金拉伸性能最大的输入参数,而加热时间是影响合金拉伸性能最小的输入参数。整体灵敏度分析结果可用于根据要求定制合金的静态性能,而局部灵敏度分析结果可用于优化已经定制的设计性能。Sobol的全局灵敏度分析表明,激光功率、加热温度和舱口间距是影响合金强度的最重要参数,而粉末层厚度、扫描速度是影响SLM制备Ti-6Al-4V合金伸长率的主要参数。未来的工作仍然需要消除本研究与有限的数据集可用性相关的一些局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.20
自引率
13.60%
发文量
34
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