Effect of Process Parameters on Mechanical Properties and Corrosion Resistance of Ti–6Al–4V Alloys Prepared by Selective Laser Melting
Journal of Materials Research and Technology(2025)
Abstract
Titanium alloys fabricated through additive manufacturing have garnered widespread attention in biomedical field, with their excellent mechanical and corrosion resistance properties serving as the prerequisites for such applications. This study thus investigates the influence of selective laser melting (SLM) process on the mechanical properties and corrosion resistance of Ti–6Al–4V (TC4) alloys. Results show that the microstructure of the prepared TC4 alloys is mainly composed of columnar β grains growing along the building direction and fine acicular α′ martensite within them. Alloy printed at low power (150 W) and high speed (1300 mm/s) exhibits weakened plasticity and corrosion resistance due to un-melted gaps and micro-void defects. Alloys prepared at medium power (200 W) show stable mechanical properties; however, their corrosion resistance fluctuates significantly with prolonged immersion time due to uneven formation rate and poor stability of the passivation film. Whereas, alloy printed at high power (250 W) and medium speed (1200 mm/s) demonstrates regularly grown columnar β grains and dispersed acicular α′ martensite, which endow it with optimal mechanical properties. Additionally, benefiting from the rapid formed passivation film, which is predominantly composed of dense and stable high-valent oxides (TiO2) on the outer layer, this alloy also exhibits the best corrosion resistance. This study indicates that TC4 alloy with excellent comprehensive properties can be achieved through SLM process regulation, making it a highly potential material for biomedical applications.
MoreTranslated text
Key words
Ti–6Al–4V alloy,Selective laser melting,Mechanical properties,Corrosion resistance,Electrochemical behavior
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
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
Summary is being generated by the instructions you defined