[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process

Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and opticalemissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here多材料激光粉末床熔融（Laser Powder Bed Fusion, LPBF）技术的市场需求日益增长，但该技术面临工艺控制与质量监测的挑战，尤以保障材料成分精准性为甚。本研究针对多材料LPBF工艺中的光发射与声发射信号展开探索，旨在解决工艺控制与确保材料成分精度的相关难题。本研究通过商用LPBF设备搭载定制化监测系统，采集了五种粉末成分加工过程中的实验数据。研究团队对不同成分LPBF加工过程中的信号进行分类，并通过融合光发射与声发射数据、采用对比学习训练卷积神经网络，提升了预测精度。采用两种对比损失函数训练得到的模型隐空间，可基于相似度对声发射与光发射信号进行聚类，且聚类结果与五种粉末成分一一对应。研究表明，对比学习与传感器融合技术对于多材料LPBF工艺的监测至关重要。本研究加深了学界对多材料LPBF技术的认知，并凸显了传感器融合策略在提升增材制造质量控制水平方面的应用潜力。本研究的数据集托管于此。