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

EPSRC产品组合领域：加法制造、后处理、数据驱动建模、表面完整性、机械性能和机械性能；加法制造(AM)为制造商提供了令人兴奋的新机会，使以前无法用传统制造工艺实现的设计得以实现。尽管对这一领域的兴趣与日俱增，但目前在预测使用AM生产的对象的性能方面还存在着知识空白虽然可以准确地预测通过传统工艺制造的零件的性能，但关键的AM工艺参数和最终产品/材料性能之间的相互关系尚未完全实现，导致目前广泛的实验测试需求。本项目旨在通过建立AM制造参数和后处理条件与在混合加减工艺链中生产的成品零件的表面、机械和材料性能之间的关联来改善这种理解。该项目将通过实验和计算两个主要阶段进行。第一阶段将利用实验测试方案评估关键AM(激光粉床熔化)参数对目标输出量(QOI)的影响，这些参数包括成品零件的表面粗糙度/质量、残余应力、拉伸性能、疲劳、蠕变、腐蚀、显微硬度和微观结构。随后将进行后处理(激光毛化/抛光、机械微机械加工、热处理等)。项目的最后阶段将使用随机替代建模技术对AM过程和后处理参数进行数据驱动的分层建模，以测量QOI。这将在高维参数空间中创建QOI响应面，从而使得AM和后处理条件的稳健最优逆设计能够获得设计者指定的QOI的值/分布。将利用贝叶斯推理框架进行稳健的逆向设计。研究将导致引入具有集成功能的部件，用于广泛的工业应用，如航空航天、汽车、铁路、电子和生物医学部门。

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.