In the effort to accurately model complex physical processes such as additive manufacturing (AM), numerical models must be validated against trusted, high quality physical measurements. This approach is particularly important for integrating AM modelling into industrial AM product design or development and for formal qualification and certification of AM-built components. This invited session will include select speakers who are involved in AM model validation measurements, validated AM simulations, and AM data management. Many of these speakers will have participated in the Additive Manufacturing Benchmark test series (AM-Bench, [1]) spearheaded by the National Institute of Standards and Technology (NIST) in 2022, which incorporated validation measurement data from over a dozen different institutions across the United States. In addition, AM Bench 2022 received 138 challenge problem submissions from the international modelling community providing an excellent pool of potential speakers on validated AM simulations. Lastly, AM Bench partners have made significant investments in AM data management, emphasizing the Findability, Accessibility, Interoperability, and Reusability (FAIR) data principles. The objective of this session is to highlight the efforts involved in developing, disseminating, and using high-quality measurements and associated data for the purpose of AM model development and validation. Additionally, lessons learned are reviewed regarding what works and what doesn’t work for AM model validation, and what is needed moving forward to accelerate the use and trust of numerical modelling in industrial AM product development.