EVALUATION OF THE INTERRELATION BETWEEN KEY PROCESS PARAMETERS AND PRODUCT/MATERIAL PROPERTIES IN HYBRID ADDITIVE-SUBTRACTIVE PROCESS CHAINS

评估混合增减工艺链中关键工艺参数与产品/材料特性之间的相互关系

• 批准号: 2278921
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2278921
• 关键词: EVALUATION; INTERRELATION; BETWEEN; KEY; PROCESS

EPSRC Portfolio Areas: Additive manufacturing, post-processing, data driven modelling, surface integrity, mechanical propertiesAdditive manufacturing (AM) presents exciting new opportunities for manufacturers, enabling the realisation of designs which have previously been impossible to achieve with traditional manufacturing processes. Interest has developed in numerous industries, including aerospace, transportation, and biomedical engineering, where AM has the potential to improve system efficiencies, foster weight reduction and improve the biocompatibility and levels of customisation available.Despite the growing interest in this field, there is currently a knowledge gap in predicting the properties of objects produced using AM. Whereas the properties of a part manufactured through traditional processes can be accurately predicted, the interrelation between the key AM process parameters and the final product/material properties is yet to be fully realised, leading to extensive experimental testing requirements at present.This project aims to improve this understanding by establishing the correlation between AM manufacturing parameters, and post-processing conditions, with the surface, mechanical and material properties of finished parts produced in a hybrid additive-subtractive process chain, both experimentally and computationally.The project will be conducted in three major phases:The first phase will deal with evaluating the effects of key AM (laser-based powder bed fusion) parameters on the output quantities of interest (qoi) which include surface roughness/quality, residual stress, tensile properties, fatigue, creep, corrosion, microhardness and microstructure of the as-built parts, using an experimental test regime.This will be followed by post-processing (laser texturing/polishing, mechanical micromachining, heat treatment etc.) of the AM parts depending on their functional requirements to determine how such processes further affect the properties of the component.The final phase of the project will involve a data-driven hierarchical modelling of the AM process and post-processing parameters to the measured qoi using a stochastic surrogate modelling techniques. This will render the creation of a qoi response surface in a high dimensional parameter space, thus enabling the robust optimal inverse design of the AM and post-processing conditions to obtain designer-specified values/distributions of the qoi. A Bayesian inference framework would be utilised to perform the robust inverse design.The research will lead to the introduction of components with integrated functionalities for a broad range of industrial applications, such as aerospace, automotive, railway, electronic and biomedical sectors.

EPSRC投资组合领域：添加剂制造，后处理，数据驱动建模，表面完整性，机械性能漆黑制造（AM）为制造商带来了令人兴奋的新机会，从而实现了以前无法通过传统制造工艺实现的设计。在包括航空航天，运输和生物医学工程在内的许多行业中都有兴趣发展，在该行业中，AM有可能提高系统效率，促进体重降低并改善生物相容性和可用的自定义水平。尽管可以准确地预测通过传统过程制造的部分的特性，但关键AM过程参数与最终产品/材料属性之间的相互关系尚未充分实现，从而导致目前广泛的实验测试要求。该项目旨在通过建立一种与制造参数和材料的属性，机制，机制，机制的属性之间的相关性来改善这种理解，以提高这种理解 additive-subtractive process chain, both experimentally and computationally.The project will be conducted in three major phases:The first phase will deal with evaluating the effects of key AM (laser-based powder bed fusion) parameters on the output quantities of interest (qoi) which include surface roughness/quality, residual stress, tensile properties, fatigue, creep, corrosion, microhardness and microstructure of the as-built parts, using an experimental test regime.This will be followed by post-processing (laser texturing/polishing, mechanical micromachining, heat treatment etc.) of the AM parts depending on their functional requirements to determine how such processes further affect the properties of the component.The final phase of the project will involve a data-driven hierarchical modelling of the AM process and post-processing parameters to the measured qoi using a stochastic surrogate modelling techniques.这将使在高维参数空间中创建QOI响应表面，从而实现AM的可靠最佳逆设计和后处理条件，以获得QOI的设计师指定值/分布。贝叶斯推理框架将用于执行稳健的逆设计。该研究将导致引入具有集成功能的组件，以用于广泛的工业应用，例如航空航天，汽车，铁路，电子，电子和生物医学领域。