Abstract Scope |
Characterization of additively-manufactured parts is often serial, where different processing steps and measurements are executed in a non-colocated and non-automated fashion. Furthermore, these activities require sustained manual oversight and intervention. Thus, the development cycle (e.g. part specification, fabrication, and qualification) is subject to bottlenecks, making part repeatability difficult and costly to achieve, quantify, and optimize. To address these issues, our team is standing up hardware and software to achieve “Autonomous Multimodal Manufacturing Optimization," as demonstrated in a modular manufacturing cell wherein cradle-to-grave data is collected from sensing, simulation, and inspection. We present some of our early stage modules, in terms of data archiving, digital twins of fabrication and inspection of fused deposition modeling parts, and analyzing part files to leverage experiential knowledge in a formulaic and process general way.
This work was performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344. |