24小时热门版块排行榜

返回列表

huanying11863

金虫 (正式写手)

应助: 0 (幼儿园)
金币: 699.6
散金: 231
帖子: 350
在线: 251小时
虫号: 552727
注册: 2008-04-30
专业: 结构化学

[交流] 德国可生物吸收植入物腐蚀及缓蚀剂研究博士生机会（2个PhD全奖学金位置）

Project 1:

Influence of WAAM parameters on localized corrosion of Mg alloys for bioabsorbable implant applications.

Objectives

This work will focus on understanding the influence of Cold Metal Transfer (CMT) WAAM fabrication process parameters (e.g., wire composition, diameter, arc discharge voltage and time, cooling rate) on the degradation profile of produced metallic materials (rate and especially the extent of localized corrosion). The aim is to establish relevant process-structure-property relationship and identify the optimal fabrication parameters for uniformly degrading Mg alloys without compromising the mechanical properties. Samples produced will be tested using custom-made Robotic Testing Platform, including removal of corrosion products before accessing the extent of localized corrosion by 3D-profilometry. A data-driven machine-learning model will be developed to determine the impact of the individual process parameters on the selected set of target properties and validated for predicting the properties of untested material. Mechanistic understanding of the driving forces of localized corrosion will be achieved by performing model experiments on selected metallic scaffolds. A unique combination of spatially resolved electrochemical and surface characterization methods will characterize Mg scaffolds exposed to the simulated body fluid environment.

Collect a data set for correlating the fabrication process parameters with degradation profile and mechanical properties.
Develop a data-driven model using ML approach for establishing the process-structure-property relationships.
Identify the underlying driving forces of localized corrosion to gain the control over it.
Develop the optimal material (degradation profile and mechanical properties) by fine-tuning the process parameters

Project 2:

Development of corrosion resistant surfaces on AM metals by experimental and data-driven selection of corrosion inhibitors.

Objectives

The proposed project will provide the foundation to train quantitative structure-property relationship models to predict the corrosion inhibition performance of untested small organic molecule corrosion inhibitors. The objectives are:

A corresponding database of concentration-dependent corrosion inhibition efficiencies for AM-manufactured materials will be generated using a custom-made robotic testing platform.
Subsequently, the molecular structure of the used corrosion inhibitors will be encoded to generate a database of input features for training of a predictive quantitative structure-relationship model. Following an active learning approach, the training database and the robustness of the developed machine learning model will be improved throughout the implementation of the project.
Lastly, the most promising inhibitors will be impregnated into a coating matrix to evaluate their performance in a coating system.

项目的详细信息、奖学金内容及申请窗口：https://duramat-project.be/recruitment/

如需帮助或咨询，请联系：haijie.tong@hereon.de

回复此楼

» 猜你喜欢

1楼 2023-08-14 18:42:51

已阅回复此楼关注TA 给TA发消息送TA红花 TA的回帖

相关版块跳转我要订阅楼主 huanying11863 的主题更新

返回列表