POSTECH
Develops an AI-based data-selective machine learning (DSML) framework that predicts the mechanical strength of metal LPBF parts in seconds, accounting for internal defects, to accelerate qualification for aerospace and automotive applications.
- CEO / Founder
- Moo Hwan Kim
- Team Size
- 1001-5000
- Stage
- Active
Technology & Products
Key Products
Data-Selective Machine Learning (DSML) framework for defect-aware yield-strength prediction
Technological Advantage
Proprietary DSML framework achieves a mean prediction error of 9.51 MPa for AlSi10Mg, reported as four times more accurate than existing methods, validated in research published in Acta Materialia. The software-layer innovation is deployable across existing LPBF systems without hardware changes, offering a protectable advantage through algorithmic IP and research publications.
Differentiation
Value Proposition
Reduces the experimental burden and time for metal LPBF part qualification by providing interpretable, defect-aware yield-strength predictions with a mean error of 9.51 MPa, compressing the iterative test-build-test cycle from months to seconds.
How They Differentiate
POSTECH's DSML framework focuses specifically on interpretable, defect-aware strength prediction for metal LPBF, unlike general simulation software (ANSYS) or broader design-for-AM platforms (nTopology). It provides human-readable equations for traceable certification decisions, whereas competitors often rely on black-box AI or broader process parameter databases (Senvol).
Market & Competition
Target Customers
Aerospace and automotive manufacturers, metal AM part producers, and research institutions requiring rapid, interpretable qualification of LPBF components.
Industry Verticals
Aerospace; Automotive; Industrial Manufacturing
Competitors
ANSYS; nTopology; Senvol
Growth & Milestones
Major Milestones
Publication of DSML framework research in Acta Materialia; Validation on AlSi10Mg alloy with reported 4x accuracy improvement over existing methods