Monitoring framework for physical knowledge exploration in wire-based directed energy additive manufacturing (w-DEDAM)

Abstract

Monitoring in wire-based directed energy deposition additive manufacturing (w-DEDAM) is crucial for ensuring part quality and process stability. It aids in optimising process control, enhancing efficiency, and minimising material waste. Additionally, monitoring provides valuable documentation for regulatory compliance and supports maintenance by tracking equipment performance. Effective monitoring and relevant control enhance process reliability, reduce scrap, and ensure the production of high-quality components. However, current monitoring systems in w-DEDAM face significant challenges. Key issues include a lack of standardisation in monitored variables, leading to inconsistent data and interpretation. Many existing monitoring systems often focus on the adjustable “knob” variable measurements rather than critical factors that more accurately represent physical process conditions. This narrow focus can fail to capture essential aspects of process explanation and part quality. Furthermore, there is a deficiency in integrating monitoring data with digital modelling and qualification methods, limiting the ability to use real-time data for comprehensive process optimisation and predictive analysis. Addressing these gaps is crucial for advancing system effectiveness. In this research, key process variables will be defined and explained to enhance process understanding. A knowledge-driven monitoring framework will be proposed to tackle the issues of standardisation and relevance of monitored parameters, ensuring that critical variables are accurately captured and utilised. Additionally, the study will introduce a commercial industry monitoring software based on this framework, which is currently used in the industry. This software integrates the proposed approach, offering a robust solution for real-time monitoring and process control, thus addressing existing limitations and supporting improved process optimisation and digital modelling.