In more recent times, research on various aspects of the Wire Arc Additive Manufacturing (WAAM) process has been conducted, and efforts into monitoring the WAAM process for defect identification have increased. Rapid and reliable monitoring of the WAAM process is a key development for the technology as a whole, as it will enable components produced by the process to be qualified to relevant standards and hence be deemed fit for use in applications such as those found in the aerospace or naval sectors. Intelligent algorithms provide inbuilt advantages in processing and analysing data, especially for the large data sets generated during the long manufacturing cycles. Interdisciplinary engineering (IDE) furnishes a concept integrating computer science and industrial system manufacturing engineering together to treat large amounts of process monitoring data. In this work, a WAAM process monitoring and defect detection system integrating intelligent algorithms is presented. The system monitors welding arc current and voltage signals produced by the WAAM process and makes use of a support vector machine (SVM) learning method to identify disturbances to the welding signal which indicate the presence of potential defects. The incremental machine learning models developed in this work are trained via statistical feature analysis of the welding signals and a novel quality metric that improves detection rates is also presented. The incremental learning approach provides an efficient means of detecting welding-based defects, as it does not require large quantities of data to be trained to an operational level (addressing a major drawback of other machine learning methods). A case study is presented to validate the developed system, results show that it was able to detect a set of defects with a success rate greater than 90% F1-score.

最近，人们对电弧增材制造 (WAAM) 工艺的各个方面进行了研究，并加大了监控 WAAM 工艺以识别缺陷的工作。WAAM 过程的快速可靠监控是整个技术的关键发展，因为它将使该过程生产的组件符合相关标准，因此被认为适合用于航空航天等应用或海军部门。智能算法在处理和分析数据方面具有内在优势，特别是对于长制造周期中生成的大型数据集。跨学科工程 (IDE) 提供了一种将计算机科学和工业系统制造工程结合在一起的概念，以处理大量过程监控数据。在这项工作中，提出了一种集成智能算法的 WAAM 过程监控和缺陷检测系统。该系统监测由 WAAM 过程产生的焊接电弧电流和电压信号，并利用支持向量机 (SVM) 学习方法来识别焊接信号的干扰，这些干扰表明存在潜在缺陷。在这项工作中开发的增量机器学习模型通过焊接信号的统计特征分析进行训练，并且还提出了一种提高检测率的新颖质量指标。增量学习方法提供了一种检测基于焊接的缺陷的有效方法，因为它不需要将大量数据训练到操作级别（解决其他机器学习方法的主要缺点）。提供了一个案例研究来验证开发的系统，结果表明它能够以大于 90% F1 分数的成功率检测一组缺陷。