A defect detection system for wire arc additive manufacturing using incremental learning

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. The fourth industrial revolution (Industrial 4.0) [1] is moving towards intelligent manufacturing. The conventional manufacturing skills integrating novel information technologies play significant roles in this unprecedented revolution. Cyber-physical system (CPS), an embranchment of Industrial 4.0, integrates heterogeneous data with real physical systems to improve manufacturing productivity and efficiency. Correspondingly, a complex and advanced manufacturing system is expected in real manufacturing cycles. However, conventional technologies in manufacturing are inadequate for the development of advanced manufacturing systems. Cooperation from other disciplines, especially knowledge from computer science and engineering, is essential. Industrial information integration engineering (IIIE) [2] comprising different disciplines, including computer science and engineering, industrial systems engineering, information systems engineering, provides an accessible method to design an advanced intelligent manufacturing system.