Abstract Ultrasonic welding is a joining technology suitable for carbon-fiber-reinforced thermoplastic (CFRTP) components because of its high throughput, and ease of automation. An effective online weld-quality inspection technology can promote the industrial application of ultrasonic composite welding. Literature focused on the quality inspection of ultrasonic composite welding is scarce. To address this, the present study proposes an online weld-quality inspection method for ultrasonic composite welding by combining artificial intelligence (AI) technologies with welding process signatures. The failure load in a tensile-shear test and the weld quality level (i.e., under weld, normal weld, and over weld) are predicted simultaneously using artificial neural network (ANN) and random forest (RF) models. Eight features consisting of the duration and energy at each welding stage are extracted from the process signatures as independent variables. The results indicate that both the ANN and RF models exhibit high prediction accuracies. The weld quality can be assessed comprehensively and reasonably by considering both the failure load and weld quality level. The findings of this study demonstrate the feasibility of online weld-quality inspection for ultrasonic composite welding.

中文翻译：

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人工智能技术与焊接工艺特征相结合的超声波复合焊接在线质量检测

摘要 超声波焊接是一种适用于碳纤维增强热塑性塑料 (CFRTP) 部件的连接技术，因为它具有高吞吐量和易于自动化的特点。一种有效的在线焊缝质量检测技术可以促进超声波复合焊接的工业应用。专注于超声波复合焊接质量检测的文献很少。为了解决这个问题，本研究提出了一种通过将人工智能 (AI) 技术与焊接过程特征相结合的超声波复合焊接在线焊接质量检测方法。使用人工神经网络 (ANN) 和随机森林 (RF) 模型同时预测拉伸剪切试验中的失效载荷和焊缝质量水平（即焊缝下、正常焊缝和焊缝过度）。八个特征包括每个焊接阶段的持续时间和能量，作为独立变量从过程特征中提取。结果表明，ANN 和 RF 模型都表现出很高的预测精度。综合考虑失效载荷和焊缝质量水平，可以对焊缝质量进行全面、合理的评估。本研究的结果证明了超声波复合焊接在线焊缝质量检测的可行性。