Materials modeling technologies are fundamental to explore, understand, and ultimately predict materials behavior. They are essential to solve challenges posed by the need to reduce human impact on the environment. Modeling and simulation of materials behavior have been recognized over the years as fundamental as an asset in industrial R & D, guiding the decision-making process...
A smooth particle hydrodynamic (SPH) simulation of an additive friction stir deposition (AFSD) repair was used to inform a multi-physics approach to predict the fatigue life of a high strength aluminum alloy. The AFSD process is a solid-state layer-by-layer additive manufacturing approach in which a hollow tool containing feedstock is used to deposit material. While an...
Future machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables accelerated multimodal characterization that simultaneously minimizes human errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time to...
High-throughput screening (HTS) can significantly accelerate the design of new materials, allowing for automatic testing of a large number of material compositions and process parameters. Using HTS in Integrated Computational Materials Engineering (ICME), the computational evaluation of multiple combinations can be performed before empirical testing, thus reducing the use of...
Metal additive manufacturing (AM) processes produce heterogeneous microstructures, leading to significant uncertainty in mechanical behavior. Process-induced defects cause additional uncertainty and can degrade performance, particularly for local processes like fatigue. However, time and monetary costs impose constraints on using repeated experiments to quantify this uncertainty...
Control of calcium treatment in steel is challenging due to the reactivity of Ca and difficulty of measuring total oxygen of steel in-process to make actionable decisions. In this work, a method combining statistics and process engineering are developed using partial least squares regression (PLS) to predict non-metallic inclusion content (oxides and CaS) and composition at the...
Artificial intelligence is now extensively being used to optimize and discover novel materials through data-driven search. The search space for the material to be discovered is usually so large, that it renders manual optimization impractical. This is where data-driven search and optimization enables us to resourcefully locate an optimal or acceptable material configuration with...
One of the challenges in computational design of pre-alloyed powders for sintering is the absence of predictive, efficient, and fast acting models that enable the design space of alloys to be tractable. This study presents an efficient and predictive model to simulate the densification as well as shape distortion of pre-alloyed powder compacts during supersolidus liquid phase...
A challenging opportunity in structural health monitoring of composite materials is using machine learning (ML) methods to classify acoustic emissions according to the damage mechanism that emitted the signal. A wide variety of ML frameworks have been developed; however, lack of ground truth datasets in addition to limited overlap between experimental configurations has precluded...
In this paper, a part-scale simulation study on the effects of bi-directional scanning patterns (BDSP) on residual stress and distortion formation in additively manufactured Nitinol (NiTi) parts is presented. The additive manufacturing technique of focus is powder bed fusion using a laser beam (PBF-LB), and simulation was performed using Ansys Additive Print software. The...
The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying...
Powder bed fusion of metals using a laser beam (PBF-LB/M) is a process that enables the fabrication of geometrically complex parts. In this process, a laser beam melts a metallic powder locally to build the desired geometry. The melt pool solidifies rapidly, which results in high cooling rates. These rates vary during the process in line with the geometric characteristics of the...
The strength of a Ni-base superalloy depends strongly on its microstructure consisting of cuboidal $${\gamma }^{^{\prime}}$$ precipitates surrounded by narrow channels of $$\gamma $$ matrix. According to the theory of Orowan, a moving dislocation has to crimp through the minimal inter-precipitate spacing to admit the plastic deformation. We present a novel approach to evaluate...
Materials scientists are facing increasingly challenging multi-objective performance requirements to meet the needs of modern systems such as lighter-weight and more fuel-efficient aircraft engines, and higher heat and oxidation-resistant steam turbines. While so-called second wave statistical machine learning techniques are beginning to accelerate the materials development cycle...
There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly...
Measured intensity in high-energy monochromatic X-ray diffraction (HEXD) experiments provides information regarding the microstructure of the crystalline material under study. The location of intensity on an areal detector is determined by the lattice spacing and orientation of crystals so that changes in the heterogeneity of these quantities are reflected in the spreading of...
In recent years, the design and development of materials are strongly interconnected with the development of digital technologies. In this respect, efficient data management is the building block of material digitization and, in the field of materials science and engineering (MSE), effective solutions for data standardization and sharing of different digital resources are needed...
Microstructure characterization and reconstruction (MCR) is an important prerequisite for empowering and accelerating integrated computational materials engineering. Much progress has been made in MCR recently; however, in the absence of a flexible software platform it is difficult to use ideas from other researchers and to develop them further. To address this issue, this work...
In this paper, phenomenological relationships are presented that permit the prediction of the plastic regime of stress–strain curves using a limited number of parameters. These relationships were obtained from both conventional (wrought + β annealed) and additively manufactured (i.e., “3D printed”) Ti-6Al-4V. Three different methods of additive manufacturing have been exploited...
This work presents a supervised machine learning (ML) model to detect race-tracking disturbances during the liquid moulding manufacturing of structural composites. Race-tracking is generated by unexpected resin channels at mould edges that may induce dry spots and porosity formation. The ML model uses the pressure signals recorded by a sensor network as input, providing a...
Computational microstructure design aims to fully exploit the precipitate strengthening potential of an alloy system. The development of accurate models to describe the temporal evolution of precipitate shapes and sizes is of great technological relevance. The experimental investigation of the precipitate microstructure is mostly based on two-dimensional micrographic images...
Two methods used to construct a microstructural representative volume element (RVE) were evaluated for their accuracy when used in a crystal plasticity-based finite element (CP-FE) model. The RVE-based CP-FE model has been shown to accurately predict the complete tensile stress–strain response of a Ti–6Al–4V alloy manufactured by laser powder bed fusion. Each method utilized a...
An integrated computational materials engineering approach to the design of alloys for supersolidus liquid phase sintering has been developed. The method aims to minimize the sensitivity of the alloys to variabilities in material (e.g., composition) and process parameters (e.g., temperature) during sintering while also maximizing mechanical properties. This is achieved by...
Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation. While established methods for...