Localized microstructural characterization of a dissimilar metal electron beam weld joint from an aerospace component

Abstract

Hydrogen induced cold cracking (HICC) and hydrogen embrittlement (HE) are influenced by the microstructural evolution, residual plastic strain (i.e. local misorientation), recrystallization of grains and the resultant grain boundary characteristic distribution (GBCD) brought about by welding processes. HICC and HE are known to cause failures in aerospace components and it is vitally important to quantify the microstructural evolution, degree of residual plastic strain and determine the GBCD across dissimilar weld joints in order to assess the susceptibility of the weld joint to these phenomena. In this investigation a full a microstructural characterization study was carried out at various locations within and around a dissimilar weld joint of Pulse-plated Nickel (PP-Ni) and Inconel 718 (IN718), taken from an aerospace component. Areas examined included the base metals, weld fusion zone and heat affected zones on both sides of the weld joint, formed via electron beam welding. Scanning electron microscopy (SEM) in combination with electron backscatter diffraction (EBSD) was employed to measure the residual plastic strain, grain structure, grain size distribution, crystal orientation distribution, grain boundary misorientation distribution and GBCD of the dissimilar metal weld joint. Finally a metallurgical examination was carried out using SEM on the IN718 HAZ in order to investigate the secondary phase precipitation arising from the welding process. The results shows large variety of GBCD, crystallographic orientation distribution, local plastic strain distribution and grain size, shape and structure distribution across dissimilar weld joint. And these localized microstructural characterized data sets need to be carefully transferred using data-driven approach in order to develop predictive multiscale material modelling for hydrogen induced cracking and hydrogen embrittlement.

An investigation of micro-mechanisms in hydrogen induced cracking in nickel-based superalloy 718

“…A decrease in strain rate increases the time duration of the test, which will increase the amount and depth of hydrogen diffusion into the sample specimen and this corresponds to the amount of subsurface cracks observed. and more details about it can be found elsewhere ( Jothi et. al.…”

Hydrogen diffusivity and tensile properties degradation in SLMed Inconel 718

“…Inconel 718 alloy is a widespread equiaxed nickel-based alloy characterized by high strength and excellent corrosion resistance, within a working temperature range between -250 and 800 . However, it is known to be susceptible to hydrogen embrittlement effects when exposed to elevated hydrogen concentrations [1,2,3,4,5]. Hydrogen can penetrate into the material during manufacturing processes or during service exposure to hydrogen-rich environments the surrounding environment during service exposure.…”

“…Hydrogen can penetrate into the material during manufacturing processes or during service exposure to hydrogen-rich environments the surrounding environment during service exposure. In the case of aerospace applications, hydrogen can arise from fuel in rocket engines and its embrittlement effects were deeply studied [5].…”

Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks

“…Panda put forward a hybrid optimization technique namely Neuro-Grey Modeling which carries out the parameter design and optimization of multi-machining characteristics of WEDM process and concluded that the model was reliable and can be implemented fast when compared with genetic algorithm [32]. It has been reported that the localized microstructural parameters such as phase distribution, grain size, distribution & grain structure also plays an important role on the effect of process parameters [35][36][37][38][39][40][41][42]. It also has been reported that the machine learning based ANN model has been used to predict the mechanical properties of metals & alloys [33,34,43].…”