Additive manufacturing (AM) technology is becoming one of the demanding manufacturing methods for various applications. Various AM methods have been developed to produce the near net shape components with complex geometry to increase productivity. On the other hand, standards for processing, testing, and quality control are under development. Wire arc additive manufacturing (WAAM) can produce large-size components using different welding processes. Quality is a significant concern for AM components, and mainly the variations in product quality cause a major barrier to the general application of WAAM in a production environment. To solve this issue, welding arc stability, voltage, arc length, and bead geometry formation sensors have been widely used to detect defects of WAAM deposits. The present study introduces an acquiring data system from the welding arc to identify the defects in WAAM and make a new wave of WAAM-related data available. The experimental findings and predicted data provide a valuable resource for achieving unique insight into WAAM processes and decision-making. Machine learning (ML) offers the opportunity to advance this insight by learning fundamental knowledge about WAAM processes and identifying predictive and actionable suggestions to optimize part quality and process design. The analysis of the defects of the first layer and successive layers data has been used to train the ML models and predict the WAAM process defects of further layers. The predictive models successfully developed the new prediction algorithms to detect the defects in WAAM deposited layers. The validation test was suitable for predicting the defects during the deposition process.

增材制造 (AM) 技术正在成为各种应用中要求苛刻的制造方法之一。已经开发了各种增材制造方法来生产具有复杂几何形状的近净形部件，以提高生产率。另一方面，加工、测试和质量控制的标准正在制定中。电弧增材制造 (WAAM) 可以使用不同的焊接工艺生产大尺寸部件。质量是增材制造组件的一个重要问题，主要是产品质量的变化导致了 WAAM 在生产环境中的一般应用的主要障碍。为了解决这个问题，焊接电弧稳定性、电压、弧长和焊道几何形状传感器已被广泛用于检测 WAAM 沉积物的缺陷。本研究引入了一种从焊接电弧中获取数据的系统，以识别 WAAM 中的缺陷，并提供新一波的 WAAM 相关数据。实验结果和预测数据为获得对 WAAM 流程和决策的独特见解提供了宝贵的资源。机器学习 (ML) 提供了通过学习有关 WAAM 流程的基础知识并确定可预测和可操作的建议以优化零件质量和流程设计来推进这种洞察力的机会。第一层和后续层数据的缺陷分析已用于训练 ML 模型并预测进一步层的 WAAM 工艺缺陷。预测模型成功地开发了新的预测算法来检测 WAAM 沉积层中的缺陷。