[论文解读] Influence of Spattering on In-process Layer Surface Roughness during Laser Powder Bed Fusion

Haolin Zhang, Chaitanya Krishna Prasad Vallabh+1 more|arXiv (Cornell University)|Mar 1, 2023

Additive Manufacturing Materials and Processes被引用 4

一句话总结

本研究提出了一种原位监测框架，结合光栅投影轮廓术与偏置成像技术，量化激光粉末床熔融（LPBF）过程中熔池飞溅与成形层表面粗糙度之间的相关性。通过机器学习提取飞溅特征（如飞溅数量、喷射角度和熔池中心位置），并结合回归建模，作者证明，与仅依赖工艺参数的传统模型相比，基于飞溅信息的预测可将表面粗糙度误差降低50%以上。

ABSTRACT

Laser powder bed fusion (LPBF) holds promise to efficiently produce metal parts. However, LPBF incurs stochastic melt pool (MP) spattering, which would roughen workpiece in-process surface, thus weakening inter-layer bonding and causing issues like porosity, powder contamination, and recoater intervention. Understanding the consequential effect of MP spattering on layer surface remains difficult due to the lack of process monitoring capability for concurrently tracking MP spatters and characterizing layer surfaces. In this work, using our lab-designed LPBF-specific fringe projection profilometry and an off-axis camera, we quantify the correlation between MP spattering and in-process layer surface roughness for the first time to reveal the influence of MP spatters on process anomaly and part defects. A method of extracting and registering MP spattering metrics is developed by machine learning of the in-situ imaging data. Each image is analyzed to obtain the MP center location and the spatter count and ejection angle. These MP spatter signatures are registered for each monitored MP across each layer. Regression modeling is used to correlate registered MP spatter signature and its processing parameters with layer surface topography measured by the in-situ FPP. We find that the attained MP spatter feature profile can help predict the layer surface roughness more accurately (> 50% less error), in contrast to the conventional approaches that would only use nominal process setting. This is because the spatter information can reflect key process changes including the deviations in actual laser scan parameters and their effects. The results corroborate the importance of spatter monitoring and the distinct influence of spattering on layer surface roughness. Our work paves a foundation to thoroughly elucidate and effectively control the role of MP spattering in defect formation during LPBF.

研究动机与目标

• 为解决LPBF过程中熔池飞溅与层表面形貌缺乏同步监测的问题。
• 研究飞溅事件如何影响实时表面粗糙度，并导致孔隙率和层间结合不良等缺陷。
• 开发一种实时、数据驱动的方法，从原位图像中提取并注册飞溅特征。
• 通过整合飞溅指标与工艺参数，而非仅依赖名义设置，提升表面粗糙度预测的准确性。

提出的方法

• 采用自研的LPBF兼容型光栅投影轮廓术（FPP）系统，以高分辨率测量实时层表面形貌。
• 使用偏置高速相机捕捉激光扫描过程中原位的熔池动态与飞溅事件。
• 应用机器学习分析每一帧图像，提取关键飞溅特征：熔池中心位置、飞溅数量和喷射角度。
• 开发一种配准方法，实现对多层和扫描路径中单个熔池及其飞溅特性的追踪。
• 构建回归模型，将注册后的飞溅特征与工艺参数（如激光功率、扫描速度）关联至FPP测得的表面粗糙度。
• 将基于飞溅信息的模型预测性能与仅依赖名义工艺设置的传统模型进行对比验证。

实验结果

研究问题

• RQ1 在LPBF过程中，熔池飞溅与实时层表面粗糙度之间存在何种相关性？
• RQ2 与名义工艺参数相比，从原位成像中提取的飞溅特征在多大程度上可提升表面粗糙度预测的准确性？
• RQ3 哪些特定飞溅特征（如数量、喷射角度、位置）对表面粗糙度变化最具预测性？
• RQ4 实时监测飞溅是否能够实现对导致缺陷的过程异常的早期检测？
• RQ5 实际激光扫描参数的偏差在飞溅行为和表面质量上如何体现？

主要发现

• 将原位飞溅特征与工艺参数相结合，相比仅使用名义工艺设置的模型，表面粗糙度预测误差降低了50%以上。
• 飞溅数量和喷射角度被确定为工艺不稳定性与表面质量下降的关键指标。
• 熔池中心位置与飞溅分布模式始终与层表面的局部粗糙度变化相关。
• 基于机器学习的飞溅特征提取方法实现了对多层中飞溅事件的可靠、逐帧追踪。
• 本研究发现，飞溅并非随机副产物，而是一种可测量的过程指标，反映了激光扫描行为的实际偏差。
• 结果表明，实时飞溅监测可作为评估表面完整性与预测LPBF中缺陷形成的可靠代理。