Improving manufacturing quality using integrated computational materials engineering

Abstract

The prediction of materials properties and their variation within a specification or design space is key in ensuring reliable production uniformity. To capture the complex mechanisms that underpin materials’ performance, processing-structure-properties links are established using a “systems design” approach. QuesTek Innovations LLC has previously utilized multi-scale ICME modeling methodologies and tools (e.g., CALPHAD thermodynamic and kinetic databases, property models, etc.) and advanced characterization techniques to design advanced materials with improved performance. This work focuses on building an ICME infrastructure to predictively model properties of critical materials for energy and defense applications by optimizing existing materials, performing calculations to quantify uncertainty in material properties, and defining target specification ranges and processing parameters necessary to ensure design allowables. Focusing on two material case studies, 304L austenitic stainless steel and glass-ceramic-to-metal seals, we show how these ICME techniques can be used to better understand process-structure and structure-property relationships. These efforts provide pathways to novel, fully optimized alloys and production processes using the Accelerated Insertion of Materials (AIM) methodology within ICME. The AIM method is used for probabilistic properties forecasting to enable rapid and cost-efficient process optimization and material qualification.