Finite element analysis-based study of 3D-printed TC4 bolt cellular and void structures

Selective Laser Melting (SLM) 3D printing technology has witnessed an increasingly extensive application within manufacturing. In this study, TC4 M64 bolts were selected as the research objects. Finite element simulation was employed to dissect the stress distribution under varying filling densities. Moreover, in combination with the filling structure, the actual performance was evaluated through a series of performance tests, including tensile, compressive, bending and fastening performance tests. Simultaneously, attention was focused on the cellular structure and void structure of the 3D-printed TC4 bolts. An accurate model was constructed, with mesh units being carefully divided and boundary as well as material parameters being precisely set. Subsequently, the internal stress and strain of the bolts under different loading conditions were simulated. The comprehensive simulation and experimental results demonstrated that when the filling density in 3D printing reached 90%, the internal grains of the bolts were refined, the stress distribution became uniform, the consumption of printing materials and time was effectively curtailed, and the stress concentration during the pretightening process was alleviated, thereby achieving the key parameters for the optimisation of mechanical performance. This, in turn, offers a significant reference for the application of emergency repair engineering within the manufacturing field.

Authors

Institutions

Publication Details

Published
2025-05-09
DOI
https://doi.org/10.1145/3756423.3756473
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

Finite element analysis-based study of 3D-printed TC4 bolt cellular and void structures

Zhiwei Chen
Additive Manufacturing and 3D Printing Technologies
article

Finite element analysis-based study of 3D-printed TC4 bolt cellular and void structures

Zhiwei Chen
article en

Abstract

Selective Laser Melting (SLM) 3D printing technology has witnessed an increasingly extensive application within manufacturing. In this study, TC4 M64 bolts were selected as the research objects. Finite element simulation was employed to dissect the stress distribution under varying filling densities. Moreover, in combination with the filling structure, the actual performance was evaluated through a series of performance tests, including tensile, compressive, bending and fastening performance tests. Simultaneously, attention was focused on the cellular structure and void structure of the 3D-printed TC4 bolts. An accurate model was constructed, with mesh units being carefully divided and boundary as well as material parameters being precisely set. Subsequently, the internal stress and strain of the bolts under different loading conditions were simulated. The comprehensive simulation and experimental results demonstrated that when the filling density in 3D printing reached 90%, the internal grains of the bolts were refined, the stress distribution became uniform, the consumption of printing materials and time was effectively curtailed, and the stress concentration during the pretightening process was alleviated, thereby achieving the key parameters for the optimisation of mechanical performance. This, in turn, offers a significant reference for the application of emergency repair engineering within the manufacturing field.

Yango University (CN)
Openalex Percentile: Top 72%
Additive Manufacturing and 3D Printing Technologies
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.