Dr. Ryan Dehoff

Ryan R Dehoff

Director, Manufacturing Demonstration Facility

Contact

DEHOFFRR@ORNL.GOV

All Publications

A visco-plastic constitutive model for accurate densification and shape predictions in powder metallurgy hot isostatic pressing

Qualification of an Additively Manufactured Irradiation Capsule for the High Flux Isotope Reactor

Relative phase stability of L12 and DO22/DO23 structures in Al3Nb, Al3Zr and Al3V compounds

Design and Demonstration of an Additively Manufactured Integrated Pressure Limiting Structure for Insertion into a Nuclear Research Reactor

Impact of droplet oxidation on mechanical properties of an Al-7Si-0.4Mg alloy fabricated with liquid metal jetting

Design of Silicide-Strengthened Nb–Si–Cr–(Mo) alloys for additive manufacturing

IN-SITU PROCESS MONITORING EVALUATION AND DEMONSTRATION USING ADVANCED CHARACTERIZATION WITH LASER POWDER BED SYSTEMS

High-Throughput Characterization Tools/Algorithms To Outline Porosity Variability in AM Samples as a Function of Processing Conditions

Hybrid Directed Energy Deposition of Geometrically-Complex Pressure Vessels for Advanced HIP Canning and Digitally-Driven Powder Metallurgy

Dual X-ray computed tomography-aided classification of melt pool boundaries and flaws in crept additively manufactured parts...

ASSESSMENT AND USAGE OF IN-SITU MONITORING DATA FOR ASME PART QUALIFICATION

FY23 Multi-Dimensional Data Correlation Platform: Unified Software Architecture for AMMT Data Management and Processing

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Ability of x-ray computed tomography to resolve critical flaw size in laser-based, paste stereolithography ceramic printing of alumina

Investigation of large-scale AM + PM parts for Nuclear Application

Fabrication of a Liner Assembly for the MARVEL Microreactor

Creep deformation and cavitation in an additively manufactured Al-8.6Cu-0.4Mn-0.9Zr (wt%) alloy

High Strength Aluminum Additive Manufacturing

A Data-Driven Framework for Direct Local Tensile Property Prediction of Laser Powder Bed Fusion Parts

Deep Learning Based Workflow for Accelerated Industrial X-Ray Computed Tomography

Neural network-based single material beam-hardening correction for X-ray CT in Additive Manufacturing

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

Oak Ridge National Laboratory Compilation of AMMT Quality Assurance Procedures

Report Outlining Computed Tomography Strategy and Microscopy Approach to Qualifying AM 316 Materials

3D Printed eutectic aluminum alloy has facility for site-specific properties