Szczegóły publikacji
Opis bibliograficzny
Advanced numerical modeling of powder bed fusion: from physics-based simulations to AI-augmented digital twins / Łukasz ŁACH, Dmytro SVYETLICHNYY // Materials [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1944 . — 2026 — vol. 19 iss. 2 art. no. 426, s. 1-75. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 57-75, Abstr. — Publikacja dostępna online od: 2026-01-21
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165836 |
|---|---|
| Data dodania do BaDAP | 2026-02-18 |
| Tekst źródłowy | URL |
| DOI | 10.3390/ma19020426 |
| Rok publikacji | 2026 |
| Typ publikacji | przegląd |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Materials |
Abstract
Powder bed fusion (PBF) is a widely adopted additive manufacturing (AM) process category that enables high-resolution fabrication across metals, polymers, ceramics, and composites. However, its inherent process complexity demands robust modeling to ensure quality, reliability, and scalability. This review provides a critical synthesis of advances in physics-based simulations, machine learning, and digital twin frameworks for PBF. We analyze progress across scales—from micro-scale melt pool dynamics and mesoscale track stability to part-scale residual stress predictions—while highlighting the growing role of hybrid physics–data-driven approaches in capturing process–structure–property (PSP) relationships. Special emphasis is given to the integration of real-time sensing, multi-scale modeling, and AI-enhanced optimization, which together form the foundation of emerging PBF digital twins. Key challenges—including computational cost, data scarcity, and model interoperability—are critically examined, alongside opportunities for scalable, interpretable, and industry-ready digital twin platforms. By outlining both the current state-of-the-art and future research priorities, this review positions digital twins as a transformative paradigm for advancing PBF toward reliable, high-quality, and industrially scalable manufacturing.
