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科学家在德克萨斯农工大学已经确定了一个跨兼容的参数集，用于一致激光粉末床融合（LPBF）3D打印无裂纹的金属组件。
使用单轨打印数据和机器学习（ML），研究人员已经能够发现理想的系统和材料，以反复创建具有均匀属性的完美零件。与现有的参数优化方法相比，团队说他们的价格更便宜，耗时，而且通常更简单，可能会借用其航空航天，汽车或国防应用。
德克萨斯州A＆M的Raiyan Seede说：“我们最初的挑战是确保印刷零件中没有毛孔，因为这显然是创建具有增强机械性能的物体的杀手。”“在这项研究中，我们深入研究了合金的微观结构，因此对最终印刷对象的性质的控制权比以前要大得多。”
LPBF中的“微膜片”
而越来越多的LPBF-compatible合金is great for manufacturers seeking to create complex parts with specific properties, the diversity of these metals also makes identifying an ideal parameter set for them difficult. Such powders often include a mixture of nickel, aluminum or magnesium, that once printed, cool at different rates, which can cause ‘microsegregation’ to occur.
“想象一下将盐倒在水中，” Seede解释说。“当盐量很小时，它立即溶解，但是当您倒更多的盐时，不溶解的多余盐颗粒开始以晶体的形式沉淀出来。从本质上讲，当印刷后快速冷却时，这就是它们在我们的金属合金中的情况。”
When this happens during solidification, flaws sometimes emerge in the resulting parts that impact on their mechanical properties, and ultimately limit their applications. In the past, heat treatments have been used to combat such microsegregation, but these can result in coarse grain structures, and their compatibility with nickel-based superalloys is often limited.
To get around this, previous researchers have sought to tailor the composition of affected metals and identify an optimized processing parameter set. However, this too can be an expensive task, and yield complex results that can’t be easily converted into inputs, thus to create a more systemic approach to processing alloys of all compositions, the Texas A&M team decided to turn to ML technologies.
德克萨斯A＆M的ML LED方法
在确定固化速度是避免缺陷的关键之后，意识到在某些情况下可能不可行扫描速度可能是不可行的，因此团队选择映射合金如何发展“控制”该过程的手段。
为了收集所需的数据，研究人员研究了印刷过程中四种基于镍合金的行为，每种合金包括不同浓度的锌，锆和铝。然后总共进行了46个单轨激光测试，在不同的温度下对金属的物理状态进行监测，产生可以转换为详细相图的数据。
Once the team had determined how chemical composition could be optimized to achieve minimal microsegregation, they carried out further experiments to find out how laser settings affect part porosity too. Interestingly, alloys with higher melting temperatures were found to be more susceptible to keyhole defects, as they exhibited shallower melt pool structures, which resulted in a lack of fusion.
后来，为了在收集的数据中找到其他趋势，研究人员将其送入了机器学习算法，该算法经过培训，可以计算给定零件的潜在错误率和准确性水平。结果表明，正如预测的那样，温度，激光功率，分区系数和冰冻范围都对结果模型产生了影响，但是扫描速度是最重要的输入。
由United States Army Research Office和国家科学基金会, the team say that their research has yielded a simplified method of 3D printing crack-free parts with any alloy, that could now be adopted within a wide variety of industries.
“Our methodology eases the successful use of alloys of different compositions for AM without the concern of introducing defects, even at the microscale,” added Ibrahim Karaman, Head of Texas A&M’s Materials Science and Engineering Department. “This work will be of great benefit to the aerospace, automotive and defense industries, that are constantly looking for better ways to build custom metal parts.”
争取无缺陷的AM
仅在过去的一年中，就可以确切地评估如何调整LPBF系统的参数以实现最佳结果来确切进展。使用模拟和高速视频，一个团队Lawrence Livermore National Laboratory最近设法精确地确定了causes of microcracking in tungsten。
就像德克萨斯A＆M团队一样，中国和美国研究人员的合作者也发现速度是金属3D打印的重要变量。通过X射线成像，科学家评估了在印刷物体上形成的J形气泡的原因，并建立了一个LPBF ‘speed limit’该部分缺陷不太可能发生。
In August 2020, another group of researchers from Texas A&M deployed an ML approach alongsideArgonne国家实验室to develop a means of预测3D打印零件中的缺陷。通过在热历史和地下缺陷的形成之间绘制链接，该团队认为可以在生产过程中检测空隙，并放弃容易发生结构故障的任何印刷品。
研究人员的发现在其论文中详细介绍了“组成和相图特征对激光粉末床融合中的可打印性和微观结构的影响：合金系统跨合金图的加工图的开发和比较。”
这项研究由Raiya Seede，Jiahui Ye，Austin Whitt，William Trehern，Alaa Elwany，Raymundo Arroyave和Ibrahim Karaman合着。
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特色图像显示了团队研究中使用的镍粉合金的色彩电子显微照片。图片通过德克萨斯A＆M大学。
