Integrating Materials and Manufacturing Innovation最新文献

Understanding the large deformation behavior of materials under external forces is crucial for reliable engineering applications. The mechanical properties of materials depend on their underlying microstructures, which change over time during deformation. Experimental observation of these processes is time-consuming and influenced by various conditions. Therefore, we developed MicroProcSim, a physics-based simulation tool to replicate the deformation process of cubic microstructures. MicroProcSim can predict the evolution of texture, represented by the orientation distribution function (ODF), over time under various loads and strain rates. This software package can be run on both Windows and Linux operating systems. Unlike conventional crystal plasticity finite element software, MicroProcSim offers a distinct advantage by rapidly generating deformed textures, as it bypasses incorporating grain morphology. Furthermore, comparisons with existing experimental and computational studies on texture evolution have demonstrated that this software seamlessly replicates real-world material processing conditions through a simple modification of a single input matrix.

Editor’s video summary: The online version of this article (10.1007/s40192-025-00405-6) contains an Editor's Video Summary, which is available to authorized users.

To meet the increasing demands of next generation high performance aircraft and propulsion system requirements, multidisciplinary model based materials engineering (MBME) approaches that utilize physics-based, quantitative process–structure–property–performance (PSPP) relationships are being developed and implemented. Traditional empirically based material property development resulted in underutilized component capabilities, and hinder MBME based methods that would allow the optimization of inter-related technologies of materials, manufacturing processes, and component design. A model-based materials engineering framework provides a means to enhanced materials and process definitions, and the rapid development of optimal designs with respect to cost, weight, performance, and qualification. Several key elements have been identified for the successful establishment of a model-based material definition (MBMD) infrastructure. These include individual or sets of specific computational model and data tools that work together in a cross-disciplinary engineering workflow. These infrastructural elements include robust, validated, scalable, fit for purpose models with the appropriate level of accuracy; toolsets for the automated linking of materials, manufacturing, and design models; enhanced data capture and management system to enable model calibration, validation and capture of materials and process variability; and multi-scale materials characterization tools and methods. This paper will review examples of industrial MBMD frameworks for titanium and titanium component design that utilizes validated manufacturing process, microstructure evolution, mechanical property and component/system performance modeling tools that have been developed to support robust PSPP relationships that enable high performance location specific component designs.

Establishing a quantitative composition–microstructure–property relationship is crucial in material design and process optimization. With the advent of big data technology, deep learning models, as a machine learning method that can automatically extract information from images, have been widely used in microstructure image identification and property prediction. However, most deep learning models only use single-scale images for property prediction, ignoring the multi-scale microstructure information of materials. In this study, an explainable deep learning model was developed based on a multi-modal and multi-scale dataset for predicting the tensile properties of aluminum alloys. Three different kinds of aluminum alloys, each incorporating various trace elements, were prepared to evaluate the adaptation of the model. The predicted results demonstrate that the integration of multi-scale microstructure information significantly improves the model’s prediction ability. Furthermore, the intrinsic mechanisms of the deep learning model were elucidated through the application of a visualization technique, greatly improving the explicability of the model. In addition, the effect of data redundancy on model performance was analyzed. The proposed deep learning model breaks the traditional deep learning strategy with the single-scale image as input and effectively establishes the composition–microstructure–property relationship.

Crystal plasticity-based digital twins are an alternative to expensive and time-consuming experiments for the investigation of micro-mechanical material behavior. However, before using simulations as an alternative for experiments, the capabilities and limitations of the modeling approach need to be known. This is best done by juxtaposing the predictions of digital twins against experimental data. The present work assesses the capabilities of full-field crystal plasticity simulations in an additively manufactured (AM) nickel-based superalloy that was characterized in situ by high-energy X-ray diffraction microscopy and electron backscatter diffraction as part of challenge 4 of air force research laboratory’s AM modeling challenge series. To ensure that the grains of interest are initialized with the measured eigenstrains, a novel scheme is proposed and its performance is evaluated. The overall agreement between simulation and experiment is assessed and compared to previous studies using the same dataset and aspects for which a systematic disagreement is seen are discussed.

High-energy diffraction microscopy (HEDM) combined with in situ mechanical testing is a powerful nondestructive technique for tracking the evolving microstructure within polycrystalline materials during deformation. This technique relies on a sophisticated analysis of X-ray diffraction patterns to produce a three-dimensional reconstruction of grains and other microstructural features within the interrogated volume. However, it is known that HEDM can fail to identify certain microstructural features, particularly smaller grains or twinned regions. Characterization of the identical sample volume using high-resolution surface-specific techniques, particularly electron backscatter diffraction (EBSD), can not only provide additional microstructure information about the interrogated volume but also highlight opportunities for improvement of the HEDM reconstruction algorithms. In this study, a sample fabricated from undeformed “low solvus, high refractory” nickel-based superalloy was scanned using HEDM. The volume interrogated by HEDM was then carefully characterized using a combination of surface-specific techniques, including epi-illumination optical microscopy, zero-tilt secondary and backscattered electron imaging, scanning white light interferometry, and high-precision EBSD. Custom data fusion protocols were developed to integrate and align the microstructure maps captured by these surface-specific techniques and HEDM. The raw and processed data from HEDM and serial sectioning have been made available via the Materials Data Facility (MDF) at https://doi.org/10.18126/4y0p-v604 for further investigation.

Advances in materials science require leveraging past findings and data from the vast published literature. While some materials data repositories are being built, they typically rely on newly created data in narrow domains because extracting detailed data and metadata from the enormous wealth of publications is immensely challenging. The advent of large language models (LLMs) presents a new opportunity to rapidly and accurately extract data and insights from the published literature and transform it into structured data formats for easy query and reuse. In this paper, we build on initial strategies for using LLMs for rapid and autonomous data extraction from materials science articles in a format curatable by materials databases. We presented the subdomain of polymer composites as our example use case and demonstrated the success and challenges of LLMs on extracting tabular data. We explored different table representations for use with LLMs, finding that a multimodal model with an image input yielded the most promising results. This model achieved an accuracy score of 0.910 for composition information extraction and an F(_1) score of 0.863 for property name information extraction. With the most conservative evaluation for the property extraction requiring exact match in all the details, we obtained an F(_1) score of 0.419. We observed that by allowing varying degrees of flexibility in the evaluation, the score can increase to 0.769. We envision that the results and analysis from this study will promote further research directions in developing information extraction strategies from materials information sources.

Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm² and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

The Additive Manufacturing Benchmark Test Series (AM Bench) provides rigorous measurement data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM Bench includes extensive in situ and ex situ measurements, simulation challenges for the AM modeling community, and a corresponding conference series. In 2022, the second round of AM Bench measurements, challenge problems, and conference were completed, focusing primarily upon laser powder bed fusion (LPBF) processing of metals, and both material extrusion processing and vat photopolymerization of polymers. In all, more than 100 people from 10 National Institute of Standards and Technology (NIST) divisions and 21 additional organizations were directly involved in the AM Bench 2022 measurements, data management, and conference organization. The international AM community submitted 138 sets of blind modeling simulations for comparison with the in situ and ex situ measurements, up from 46 submissions for the first round of AM Bench in 2018. Analysis of these submissions provides valuable insight into current AM modeling capabilities. The AM Bench data are permanently archived and freely accessible online. The AM Bench conference also hosted an embedded workshop on qualification and certification of AM materials and components.

An additively manufactured titanium alloy sample has been characterized by X-ray computed tomography and optical microscopy serial sectioning to enable a correlative analysis of internal porosity. Titanium alloy ball bearings were adhered to the surface of the cylindrical sample to aid the registration of the datasets. The characterization data includes five X-ray computed tomography scans from four different instruments and optical microscopy serial sectioning images. The methods and parameters used for collecting these multiple datasets, and reconstructed data for each dataset‘s selected volume of interest are provided. Raw projection data from each computed tomography scan are also offered. Unanticipated artifacts within the serial sectioning experiment are highlighted, and the potential impact of these artifacts is discussed.

There is an urgent need for ready access to published data for advances in materials design, and natural language processing (NLP) techniques offer a promising solution for extracting relevant information from scientific publications. In this paper, we present a domain-specific approach utilizing a Transformer-based model, T5, to automate the generation of sample lists in the field of polymer nanocomposites (PNCs). Leveraging large-scale corpora, we employ advanced NLP techniques including named entity recognition and relation extraction to accurately extract sample codes, compositions, group references, and properties from PNC papers. The T5 model demonstrates competitive performance in relation extraction using a TANL framework and an EM-style input sequence. Furthermore, we explore multi-task learning and joint-entity-relation extraction to enhance efficiency and address deployment concerns. Our proposed methodology, from corpora generation to model training, showcases the potential of structured knowledge extraction from publications in PNC research and beyond.