Integrating Materials and Manufacturing Innovation

https://link.springer.com/journal/40192

List of Papers (Total 113)

Review of Material Modeling and Digitalization in Industry: Barriers and Perspectives

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...

Multi-physics Approach to Predict Fatigue Behavior of High Strength Aluminum Alloy Repaired via Additive Friction Stir Deposition

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...

A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films

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...

CAROUSEL: An Open-Source Framework for High-Throughput Microstructure Simulations

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...

A Process-Structure-Property Simulation Framework for Quantifying Uncertainty in Additive Manufacturing: Application to Fatigue in Ti-6Al-4V

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...

Calcium-Treated Steel Cleanliness Prediction Using High-Dimensional Steelmaking Process Data

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...

Faux-Data Injection Optimization for Accelerating Data-Driven Discovery of Materials

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...

Computational Efficient Modeling of Supersolidus Liquid Phase Sintering in Multi-component Alloys for ICME Applications

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...

Quantitative Benchmarking of Acoustic Emission Machine Learning Frameworks for Damage Mechanism Identification

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...

Bi-directional Scan Pattern Effects on Residual Stresses and Distortion in As-built Nitinol Parts: A Trend Analysis Simulation Study

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...

Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Toward ML-Assisted Advanced Manufacturing

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...

A Novel Methodology for the Thermographic Cooling Rate Measurement during Powder Bed Fusion of Metals Using a Laser Beam

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...

3D Minimum Channel Width Distribution in a Ni-Base Superalloy

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...

Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery

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...

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

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...

Quantifying Dynamic Signal Spread in Real-Time High-Energy X-ray Diffraction

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...

Ontopanel: A Tool for Domain Experts Facilitating Visual Ontology Development and Mapping for FAIR Data Sharing in Materials Testing

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 in Python: MCRpy

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...

On the Prediction of Uniaxial Tensile Behavior Beyond the Yield Point of Wrought and Additively Manufactured Ti-6Al-4V

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...

A Machine Learning Strategy for Race-Tracking Detection During Manufacturing of Composites by Liquid Moulding

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...

Consistent Quantification of Precipitate Shapes and Sizes in Two and Three Dimensions Using Central Moments

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...

A Comparison of Statistically Equivalent and Realistic Microstructural Representative Volume Elements for Crystal Plasticity Models

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...

Computational Alloy Design for Process-Related Uncertainties in Powder Metallurgy

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...

CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry

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...