工艺与制造英文2023被引 7
用于预测热塑性复合材料超声焊接质量的机器学习方法的新输入因素
New Input Factors for Machine Learning Approaches to Predict the Weld Quality of Ultrasonically Welded Thermoplastic Composite Materials
Dominik Görick, Alfons Schuster, Lars Larsen, Jonas Welsch, Tobias Karrasch, Michael Kupke · Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
摘要整理
热塑性复合材料（TCs）在工程领域应用广泛，对快速高效的连接工艺需求日益增长。超声焊接因其高效性而得到广泛应用。为确保焊接质量的可靠性，需要建立相应的质量保证体系。虽然超声点焊已有成熟的质量监测和预测方法，但这些关键参数在连续超声焊接过程中难以直接测量。本研究探索新的参数以改进热塑性复合材料超声焊接质量的预测能力。研究发现热成像和声发射数据与焊接质量存在相关性，将这些数据输入不同的机器学习算法进行处理。尽管数据集规模相对较小，训练后的算法二分类准确率仍超过90%，表明新发现的参数具有改进热塑性复合材料超声焊接质量保证的潜力，有望推动超声焊接技术在热塑性复合材料制造中的应用。
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