Modeling the influence of the composition of refractory elements on the heat resistance of nickel alloys by a deep learning artificial neural network

Nickel alloys are widely used in the manufacture of gas turbine parts. The alloys show resistance to mechanical and chemical degradation under prolonged loads and high temperatures. The properties of an alloy are determined by its composition and are subject to careful modeling. A set of basic refractory elements makes a special contribution to the parameters of the alloy. One of the main mechanical properties of the alloys is the high-temperature tensile strength. Determining the influence of certain elements on certain properties of an alloy is a complex scientific and engineering problem that affects the time and cost of developing new materials. Simulation is a great chance to cut costs. In this article, we predict high temperature strength based on the composition of refractory elements in alloys using a specially designed deep learning artificial neural network. We build the model based on prior knowledge of alloy composition, information on the role of alloying elements, type of crystallization, test temperature and time, and tensile strength. Successful simulation results show the applicability of this method in practice. © 2021 John Wiley & Sons, Ltd

Quantitative electron microscopy for microstructural characterisation

Development of materials for high-performance applications requires accurate and useful analysis tools. In parallel with advances in electron microscopy hardware, we require analysis approaches to better understand microstructural behaviour. Such improvements in characterisation capability permit informed alloy design. New approaches to the characterisation of metallic materials are presented, primarily using signals collected from electron microscopy experiments. Electron backscatter diffraction is regularly used to investigate crystallography in the scanning electron microscope, and combined with energy-dispersive X-ray spectroscopy to simultaneusly investigate chemistry. New algorithms and analysis pipelines are developed to permit accurate and routine microstructural evaluation, leveraging a variety of machine learning approaches. This thesis investigates the structure and behaviour of Co/Ni-base superalloys, derived from V208C. Use of the presently developed techniques permits informed development of a new generation of advanced gas turbine engine materials.Open Acces

Gas Turbines

This book is intended to provide valuable information for the analysis and design of various gas turbine engines for different applications. The target audience for this book is design, maintenance, materials, aerospace and mechanical engineers. The design and maintenance engineers in the gas turbine and aircraft industry will benefit immensely from the integration and system discussions in the book. The chapters are of high relevance and interest to manufacturers, researchers and academicians as well

Light Alloys and High-Temperature Alloys

Light alloys and high-temperature alloys are widely used as key engineering materials in both the civil and military industries due to their excellent comprehensive properties and performance. Over recent decades, huge amounts of theoretical and/or experimental efforts have been devoted to this field and great achievements have been made. In this book, entitled "Light Alloys and High-Temperature Alloys", there are 14 research papers contributed by 85 authors at 27 universities/institutes/companies from 9 countries, including China, the USA, the UK, Germany, Spain, Australia, Ukraine, Poland, and Romania. The topics cover different types of light alloys, including Al-, Mg-, and Ti-based ones (also Ti-based metal matrix composites), and high-temperature alloys, including Ni-, Fe-, Nb-, and Ta-based ones. Two new types of alloys, i.e., complex concentrated alloys (CCAs) and phase change materials, are also included. Moreover, in this book, a variety of multi-scale theoretical methods, ranging from first-principles calculations, first-principles molecular dynamic simulations, and Calculation of Phase Diagram (CALPHAD) modeling to crystal plasticity finite element simulations coupled with knowledge graph, as well as experimental techniques, e.g., casting, powder metallurgy, additive manufacturing, etc. are discussed. Consequently, the diverse topics and state-of-the-art theoretical/experimental techniques will attract broad interest from materials researchers worldwide

The Application of Additive Manufacturing to Nickel-Base Superalloys for Turbocharger Applications

Metal additive manufacturing (AM) is a disruptive technology which has the potential to deliver numerous benefits over conventional manufacturing including design freedom, increased innovation and shorter lead times. However, adoption of the technology by the automotive industry is currently restricted by the cost, limited availability of suitable engineering alloys and the lack of robust process control. These issues are all relevant in the present discussion on the application of laser powder bed fusion (LPBF) to the nickel-base superalloy IN713C. This alloy, along with other precipitation strengthened nickel-base superalloys, is considered to be “un-weldable” due to its susceptibility to cracking; an issue which makes it similarly challenging to process via LPBF. This thesis addresses both the business case for LPBF in terms of turbocharger componentry, and the behaviour of IN713C under LPBF in terms of understanding the defect response. Statistical design of experiments (DOE), advanced material characterisation and analysis techniques, analytical melt pool modelling, thought experiments and the application of literature models have all been employed, facilitating the development of a process map for LPBF of IN713C. The process map illustrates the thresholds for the onset of defect formation and can be used to direct future work on the design of processing strategies for complex components. Use of the process map alongside statistical response surface methodology enabled the identification of process settings for which porosity in test cube specimens was minimised. The application of literature models for solidification cracking provided insight into the relationships between process settings, solidification conditions and crack susceptibility

Digitisation of metal AM for part microstructure and property control

Metal additive manufacturing, which uses a layer-by-layer approach to fabricate parts, has many potential advantages over conventional techniques, including the ability to produced complex geometries, fast new design part production, personalised production, have lower cost and produce less material waste. While these advantages make AM an attractive option for industry, determining process parameters which result in specific properties, such as the level of porosity and tensile strength, can be a long and costly endeavour. In this review, the state-of-the-art in the control of part properties in AM is examined, including the effect of microstructure on part properties. The simulation of microstructure formation via numerical simulation and machine learning is examined which can provide process quality control and has the potential to aid in rapid process optimisation via closed loop control. In-situ monitoring of the AM process, is also discussed as a route to enable first time right production in the AM process, along with the hybrid approach of AM fabrication with post-processing steps such as shock peening, heat treatment and rolling. At the end of the paper, an outlook is presented with a view towards potential avenues for further research required in the field of metal AM