Toward reliable prediction of the mechanical strength of 3D printed parts using machine learning

Publication Details

Journal
SPIRE - Sciences Po Institutional REpository
Published
2025-10-16
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Toward reliable prediction of the mechanical strength of 3D printed parts using machine learning

SPIRE - Sciences Po Institutional REpository
Additive Manufacturing and 3D Printing Technologies
article

Toward reliable prediction of the mechanical strength of 3D printed parts using machine learning

article en

Abstract

No abstract available for this paper.
SPIRE - Sciences Po Institutional REpository
Openalex Percentile: Top 50%
Additive Manufacturing and 3D Printing Technologies
OpenAlex
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.

Toward reliable prediction of the mechanical strength of 3D printed parts using machine learning · SPIRE - Sciences Po Institutional REpository (2025) | TGRS Research Map | TGRS