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

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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.