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San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in...

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Updated: Jun 23, 2025

Laser-heating and Radiance Spectrometry for the Study of Nuclear Materials in Conditions Simulating a Nuclear Power Plant Accident

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Published on: December 14, 2017

Model-Based Sensitivity Analysis of the Temperature in Laser Powder Bed Fusion.

Zhihao Yang¹, Shiting Zhang¹, Xia Ji¹

¹School of Mechanical Engineering, Donghua University, Shanghai 201620, China.

Materials (Basel, Switzerland)

|June 19, 2024

Summary

This study quantifies how process parameters and material properties affect temperature in laser powder bed fusion (LPBF). Laser power and scanning speed significantly influence molten pool size and maximum temperature, with laser power having the largest impact.

Keywords:

analytical model laser powder bed fusion (LPBF)molten pool temperature history

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Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy

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Published on: June 10, 2019

Area of Science:

Materials Science
Manufacturing Engineering
Thermal Analysis

Background:

Laser Powder Bed Fusion (LPBF) is a critical additive manufacturing process.
Understanding the thermal behavior during LPBF is essential for process optimization and material property control.
Existing models require validation and sensitivity analysis to accurately predict thermal outcomes.

Purpose of the Study:

To quantitatively evaluate the impact of process parameters and material properties on temperature in LPBF.
To perform a sensitivity analysis of temperature predictions using a validated model.
To compare the accuracy of different heat source models in predicting molten pool dimensions.

Main Methods:

Development and validation of a prediction model for LPBF thermal analysis.
Introduction and comparison of three heat source modes: point, Gaussian surface, and Gaussian body.
Case study using Ti6Al4V to determine suitable heat source density ranges.
Quantitative analysis of parameter effects on temperature field and molten pool size using the Gaussian surface heat source model.

Main Results:

Gaussian surface and body heat source models show higher molten pool width prediction accuracy than the point heat source model.
The Gaussian body heat source model offers superior prediction accuracy at higher laser energy densities (70-90 J/mm³).
Laser power and scanning speed are the most influential factors, significantly impacting temperature, molten pool width, and depth.

Conclusions:

The choice of heat source model significantly affects prediction accuracy for molten pool dimensions.
Laser power is the dominant parameter influencing thermal profiles and molten pool characteristics in LPBF.
Scanning speed is the second most critical parameter affecting thermal outcomes in LPBF.