# title
Uncertainty-aware Process-Property Mapping in Laser Directed Energy Deposition of 316L stainless steel

# abstract
Over more than a decade of research and development, laser directed energy deposition (L-DED) has matured considerably. However, efficiently assessing printability and establishing reliable process-property relationships continue to be major barriers toward truly achieving consistent build quality and elevating its manufacturing readiness. Existing process optimization approaches still depend on extensive experiments that are labor-intensive, sensitive to machine transients, and difficult to generalize across systems. Unlike previous works that rely primarily on empirical parameter studies, this study introduces an uncertainty-quantified response surface modeling framework that enables data-efficient optimization of L-DED processes. Using a limited number of designed experiments, the framework constructs printability and process-property maps that identify stable processing windows and quantify statistical confidence in predicted outcomes. Demonstrated with 316L stainless steel, the approach captures the achievable range of mechanical properties and their dependence on process parameters, providing a statistically robust and transferable pathway for property tailoring and process optimization across alloy systems.

# authors[].organizations[].label
Institute for Engineering Education and Innovation

# authors[].organizations[].label
Texas A&M Engineering Experiment Station (TEES)

# authors[].organizations[].label
Texas A&M Engineering Experiment Station (TEES)

# tags[]
9 Industry, Innovation and Infrastructure

# authorList[]
Ye, J., Vaughan, T. W., Rodriguez, R., Foster, T. G., Arroyave, R., Karaman, I., & Elwany, A.