LOFAMO Development of a local fatigue model of machining affected surface layers that includes surface integrity and mechanical properties

Lead Research Organisation: University of Manchester
Department Name: Materials

Abstract

The aim of LoFaMo is to develop a local fatigue model of machined surfaces of nickel alloys for aerospace based on the relationship of surface integrity (SI) and local mechanical properties of the machining-affected layer. Increasingly, aero-engines are required to operate at higher temperatures & efficiencies, and new heat resistant alloys with unprecedented mechanical properties have been developed to meet those needs. The last stage of the manufacturing chain is usually where the machining operations of critical aero-engine components are carried out. If machining parameters are not selected correctly, the SI of the machined components can be adversely affected and thus, their fatigue life can be dramatically reduced. Although many researchers have studied the SI of heat resistant alloys after machining, very limited studies have so far analysed the effect on fatigue life, and the links between SI parameters and fatigue behaviour are not widely understood and quantified.
In this project, specimens will be manufactured at three different surface conditions and the SI of them will be characterised using upfront high-resolution surface mapping using confocal and analytical electron microscopy. Then, innovative in-situ experimental techniques will be used to characterise the mechanical properties locally and fatigue crack-growth: i) in-situ mechanical tests and X-ray diffraction and ii) in-situ mechanical tests and X-ray imaging techniques. Finally, a local fatigue model will be developed integrating in a FEM model the SI and mechanical properties of the machining-affected layer. LoFaMo combines knowledge of manufacturing, material science and advanced experimental techniques to reach the goal. The results will be a step change in the machining of nickel alloys (increased productivity; reduction of scraps in 1%) and will enhance the accuracy of predictive models to produce more efficient designs (towards the European Green Deal Strategy) and extended service life.

Publications

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MADARIAGA A (2023) Correcting distortions of thin-walled machined parts by machine hammer peening in Chinese Journal of Aeronautics