11 15 22 LIFT Off Presented by Thermo Calc

Summary

Key Points

• Lyft is a non‑profit public‑private partnership that serves as a national manufacturing innovation institute to advance U.S. advanced manufacturing through technology and talent development. 1:38
• Lyft’s four technology pillars—ICME, advanced alloy and process development, multi‑material joining, and agile manufacturing such as additive manufacturing and cold spray—guide its research and industry collaborations. 5:02
• The organization runs talent programs, including the Ignite Manufacturing curriculum for high‑school students and certified training for adults, to develop a skilled manufacturing workforce. 6:15
• Thermo‑Calc databases are built from binary and ternary subsystem data, allowing accurate extrapolation to higher‑order alloys, though missing entries can reduce confidence in predictions. 22:50
• Integrating CALPHAD predictions into ICME workflows enables engineers to simulate additive manufacturing, heat treatment, welding, and other processes without costly experiments, accelerating design cycles by up to five times. 30:39
• Case studies demonstrated predicting temperature‑dependent specific heat for stainless steel in laser additive builds, modeling precipitation evolution and yield strength in Inconel 718, and optimizing hypersonic ceramic composites by raising liquidus temperatures. 38:51
• The platform provides Python and MATLAB APIs (with legacy Fortran support) to couple thermodynamic calculations with finite‑element, CFD, or machine‑learning tools for comprehensive material‑process‑performance modeling. 44:10
• Thermo‑Calc can compute location‑specific phase diagrams, predict yield stress, thermal expansion, density, and phase fractions for alloys like Al 6063 when supplied with exact chemistry and processing history. 53:39
• Thermo‑Calc’s CALPHAD software fills material property data gaps by using phase‑based thermodynamic models to predict temperatures, phase fractions, and thermophysical properties for any alloy composition. 54:00
• Hardness prediction is feasible for precipitation‑strengthened alloys using a yield‑strength model, but it does not apply to bainitic or martensitic structures, and users are encouraged to contact Lyft for support. 56:01