University of Galway, Ireland
Majid Kavousi
Biography
Dr. Majid Kavousi is a Postdoctoral Researcher at the University of Galway and the I-Form Centre for Advanced Manufacturing, Ireland. He recently completed his PhD in Mechanical Engineering at the University of Galway, titled “Computationally Efficient Micromechanical Process–Structure–Property Modelling for Biomedical Materials.” His doctoral research developed efficient micromechanical frameworks linking process, structure, and performance in additively manufactured 316L stainless steel and bioresorbable magnesium alloys through geometrically based process–structure (GBPS) and cellular automata–crystal plasticity (CA–CPFE) methodologies. Currently, Dr. Kavousi works on multi-scale process–structure–property modelling for laser powder bed fusion (PBF) of metallic systems, including Ti-6Al-4V spinal cages and NiTi aerospace actuators. He is also involved in a collaborative project with NSAI, ISO, and ASTM to support the development of new international standards for modelling in additive manufacturing. His broader interests include predictive simulation of AM materials, fatigue modelling, and process-structure-property-performance modelling for PBF.
Conferences
Room |
Date |
Hour |
Subject |
|---|---|---|---|
| Room 9 |
25-03-2026 |
11:35 am – 11:55 am |
97 A computationally efficient 3D process-structure-property modelling methodology for LB-PBF of 316L |
Conferences Details
97 A computationally efficient 3D process-structure-property modelling methodology for LB-PBF of 316L
This work presents a computationally efficient 3D geometrically based process–structure (GBPS) framework integrated with crystal plasticity finite element (CPFE) modelling for additive manufacturing of 316L stainless steel. The model reconstructs melt-pool-driven grain morphologies directly from process parameters, enabling texture and anisotropy prediction without EBSD input. Through systematic optimization, the framework achieves ~90% reduction in computational cost while accurately predicting ≈9% higher yield and ≈12% higher tensile strength in horizontal loading relative to the build direction, consistent with experiments. Ongoing work extends this methodology to cyclic loading for fatigue modelling of AM 316L.
Keywords: additive manufacturing; 316L; crystal plasticity; process–structure-property
