Optimization of Process Parameters and Performances of Invar-Alloy-Alloy Lattice Structures Manufactured Via Selective Laser Melting
Laser & Optoelectronics Progress(2024)SCI 4区
China Acad Engn Phys
Abstract
The lattice structure of invar-alloy offers the advantages of low thermal expansion coefficient and low density,thus rendering it extremely suitable for the aerospace industry.Selective laser melting(SLM),also known as laser powder bed fusion(L-PBF),is the most widely used metal additive-manufacturing technology and offers significant advantages in manufacturing complex lattice structures.However,our current understanding regarding factors that affect the performance of invar-alloy lattice structures fabricated via SLM is inadequate.Hence,a three-factor,three-level orthogonal experimental design was employed to optimize the SLM process parameters of invar-alloy.Using tensile strength and yield strength as indicators,we propose the following optimal parameters:laser power,280 W;scanning speed,1000 mm/s;and scanning spacing,0.12 mm.Tensile samples prepared under these parameters indicate yield and tensile strengths of 340 MPa and 419 MPa,respectively.Based on the optimized parameters,we investigated the effect of scanning speed on the geometric and mechanical properties of the invar-alloy lattice structure fabricated via SLM.The result shows that the lattice structure fabricated under a laser power of 280 W,a scanning speed of 1000 mm/s,and a scanning spacing of 0.12 mm exhibits both favorable mechanical and geometric performances.
MoreTranslated text
Key words
laser technology,selective laser melting,laser- based powder bed fusion,invar-alloy,lattice structure,optimization of process parameters,optimization of process parameters
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2011
被引用414 | 浏览
2016
被引用35 | 浏览
2018
被引用79 | 浏览
2018
被引用77 | 浏览
2018
被引用151 | 浏览
2019
被引用207 | 浏览
2021
被引用52 | 浏览
2021
被引用44 | 浏览
2021
被引用393 | 浏览
2022
被引用2 | 浏览
2022
被引用9 | 浏览
2023
被引用1 | 浏览
2023
被引用11 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper