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德國可生物吸收植入物腐蝕及緩蝕劑研究博士生機(jī)會(huì)（2個(gè)PhD全獎(jiǎng)學(xué)金位置）
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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. 項(xiàng)目的詳細(xì)信息、獎(jiǎng)學(xué)金內(nèi)容及申請窗口：https://duramat-project.be/recruitment/ 如需幫助或咨詢，請聯(lián)系：haijie.tong@hereon.de |
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