多源信息融合,加强激光粉末床熔融快速成型制造的过程质量监控

IF 10.3 1区 工程技术 Q1 ENGINEERING, MANUFACTURING
Tao Shen , Bo Li , Jianrui Zhang , Fuzhen Xuan
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引用次数: 0

摘要

激光粉末床熔融(L-PBF)快速成型制造过程中产生的缺陷(如熔融不足、气孔和锁孔)是一项挑战,因为缺乏有效的方法来预测过程中产生的缺陷和成型质量。要评估和提高 L-PBF 过程中的质量,在线监测势在必行。在此,介绍一种使用残差网络(ResNet)的多源信息融合策略,用于 L-PBF 期间的过程监控。该方法整合了逐层捕获的熔池红外(IR)图像、熔道俯视照片、熔道数值模拟图、L-PBF 工艺参数以及凝固后熔池横截面形态特征参数,从而实现了对 L-PBF 加工质量的监控。为了评估缺陷严重程度,提出了一种基于缺陷特定特征的定量缺陷评估方法。该方法通过提取与孔隙率和变形相关的缺陷指标,促进了缺陷的定量评估。此外,本研究还引入了两种残余物理混合网络(ResPHN)和两种残余物理融合监督网络(ResPFSN)。对这四种网络模型的性能进行了细致的比较和评估。研究结果表明,ResPFSN-type2 是最有效的特征融合监控模型,其准确率高达 99.4%,并且在输入图像大小和训练数据量不同的情况下表现出一致的性能。这凸显了其在实时过程控制应用中的潜力。此外,还对模型的可解释性进行了仔细研究,结果表明 ResPFSN-type2 模型能够很好地识别激光诱导熔池的轮廓纹理和局部特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-source information fusion for enhanced in-process quality monitoring of laser powder bed fusion additive manufacturing
Defects such as lack of fusion, porosity, and keyhole generated during the laser powder bed fusion (L-PBF) additive manufacturing process pose a challenge, with the absence of effective prediction methods for the process-induced defects and as-printed quality. On-line monitoring becomes imperative to evaluate and enhance the L-PBF in-process quality. Here, a multi-source information fusion strategy using a residual network (ResNet) is introduced for the in-process monitoring during the L-PBF. This approach integrates the melt-pool infrared (IR) images captured layer-by-layer, melt-track top-view photographs, melt-track numerical simulation diagrams, L-PBF process parameters, and characteristic parameters of melt-pool cross-sectional morphology after solidification to enable quality monitoring of the L-PBF processing. To assess the defect severity, a quantitative defect evaluation method based on the defect-specific characteristics is proposed. This method facilitates the quantitative evaluation of defects by extracting pertinent defect indicators related to porosity and deformation. Additionally, two types of residual physical hybrid networks (ResPHN) and two types of residual physical fusion supervisory networks (ResPFSN) are introduced in this study. The performance of these four network models is meticulously compared and evaluated. The findings reveal that the most effective feature fusion monitoring model is the ResPFSN-type2, achieving an impressive accuracy of 99.4 % and displaying consistent performance across varying input image sizes and training data volumes. It underscores its potential for real-time process control applications. Furthermore, the interpretability of the model is scrutinized, with results indicating that the ResPFSN-type2 model adeptly identifies the contour texture and local features of the laser-induced melt pools.
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来源期刊
Additive manufacturing
Additive manufacturing Materials Science-General Materials Science
CiteScore
19.80
自引率
12.70%
发文量
648
审稿时长
35 days
期刊介绍: Additive Manufacturing stands as a peer-reviewed journal dedicated to delivering high-quality research papers and reviews in the field of additive manufacturing, serving both academia and industry leaders. The journal's objective is to recognize the innovative essence of additive manufacturing and its diverse applications, providing a comprehensive overview of current developments and future prospects. The transformative potential of additive manufacturing technologies in product design and manufacturing is poised to disrupt traditional approaches. In response to this paradigm shift, a distinctive and comprehensive publication outlet was essential. Additive Manufacturing fulfills this need, offering a platform for engineers, materials scientists, and practitioners across academia and various industries to document and share innovations in these evolving technologies.
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