The number and types of sensors used to monitor additive manufacturing (AM) processes and parts in real time are growing. The emerging metaverse and digital twins (DTs) associated with the data collected by those sensors and the functions that use that data as inputs is becoming an increasingly important research topic. Growing demands across several industry sectors accelerates development of software tools that can create, fuse, and measure both AM DTs across the entire AM part lifecycle. While some tools are available, they are not well correlated to the functions in that lifecycle for AM Metaverse. The goal of this presentation is to identify the data requirements and technical barriers that are limiting the ability to qualify real AM parts. The benefit of understanding both can help us to (1) create digital twins that can mirror selected aspects of the real process and the real parts and (2) use both in the metaverse space to improve the quality of AM physical parts. Creating the digital twins will be based on the sensor data that is used to monitor both the process and the part.
Currently, there are no digital twins of the actual AM process. Nevertheless, we can create digital threads by fusing digital twins and using the results as inputs to available physics-based data analytics and artificial intelligence (AI) tools. We can use the outputs of those tools to monitor the process and improve the process control in real time. While some such tools are available to create AM digital twins and AM digital threads in the metaverse, the accuracy of those tools and the fidelity of their inputs and outputs are still open research topics. Laser-based powder bed fusion AM for metals is used to help identify those topics and address the existing technical barriers.
Learning Objectives:
- Upon completion, participants will be able to describe the need of using digital twins to ensure fabricated parts for qualification.
- Upon completion, participant will be able to list key digital twins for additive
- Upon completion, participant will be able to use the described additive manufacturing Metaverse to align multi-modal sensor data for part qualification based on existing AIAA, SAE, and NASA standards.
