A Physics-Informed dimensionless framework for densification in laser powder bed fusion

Purpose This study aims to propose a physics-informed framework to describe densification behavior in Laser Powder Bed Fusion (LPBF). Design/methodology/approach A new dimensionless number, Π, combines absorbed laser energy, track and layer overlap and heat-transfer effects. The framework is evaluated using experimental data from several alloys. Findings The relative-density data follow a common relative density (RD)–Π trend and reveal clear densification regions, including lack of fusion, rapid densification, near-full density and excessive energy input. Machine learning results confirm that Π alone captures the dominant mechanisms governing densification. Originality/value The proposed framework provides a simple, physically meaningful and transferable tool for LPBF process design. It offers an alternative to conventional energy-density measures and purely data-driven models.

Authors

Institutions

Publication Details

Journal
Rapid Prototyping Journal
Published
2026-10-29
DOI
https://doi.org/10.1108/rpj-03-2026-0157
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Physics-Informed dimensionless framework for densification in laser powder bed fusion

Zahabul Islam
2 links
Rapid Prototyping Journal
Additive Manufacturing Materials and Processes
article

A Physics-Informed dimensionless framework for densification in laser powder bed fusion

Zahabul Islam
article en

Abstract

Purpose This study aims to propose a physics-informed framework to describe densification behavior in Laser Powder Bed Fusion (LPBF). Design/methodology/approach A new dimensionless number, Π, combines absorbed laser energy, track and layer overlap and heat-transfer effects. The framework is evaluated using experimental data from several alloys. Findings The relative-density data follow a common relative density (RD)–Π trend and reveal clear densification regions, including lack of fusion, rapid densification, near-full density and excessive energy input. Machine learning results confirm that Π alone captures the dominant mechanisms governing densification. Originality/value The proposed framework provides a simple, physically meaningful and transferable tool for LPBF process design. It offers an alternative to conventional energy-density measures and purely data-driven models.

Rapid Prototyping Journal
Bowling Green State University (US)
Bowling Green State University
Openalex Percentile: Top 24%
Additive Manufacturing Materials and Processes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon