Many people know Additive Manufacturing with metals today - but how do you transform from rapid prototyping into end-use applications? Primarily materials that have been designed for other processes are being adapted for Additive Manufacturing - it is not guaranteed that desirable properties can be achieved. Anyway, there are some major challenges when designing new materials specially for Additive Manufacturing – like thousands of different processing parameters and rapid material characterization. ZEISS AM parameter significantly reduces time and cost of new alloy development by reducing the number of builds, rapid reproducible analysis and comprehensive parallel parameter optimization.
Very quickly, we can go from having a large number of specimens to data that can be actionable, that we can make decisions on, by having this automated process that takes all of that data and digests it for us.
ZEISS and Oak Ridge National Laboratory (ORNL) currently have a cooperative research and development agreement (CRADA) to gain a deeper understanding of additive manufacturing processes and materials. The aim is to advance characterization of additively manufactured parts using X-ray computed tomography and artificial intelligence.
The project was chosen to receive funding through the U.S. Department of Energy’s (DOE’s) Technology Commercialization Fund (TFC) project solicitation. The new methodology is designed to rapidly qualify new alloys, powder materials and processes for printed parts and enable rapid certification and qualification of additively manufactured components.