Integrating Materials and Manufacturing Innovation最新文献

{"title":"Annotating Materials Science Text: A Semi-automated Approach for Crafting Outputs with Gemini Pro","authors":"Hasan M. Sayeed, Trupti Mohanty, Taylor D. Sparks","doi":"10.1007/s40192-024-00356-4","DOIUrl":"https://doi.org/10.1007/s40192-024-00356-4","url":null,"abstract":"<p>Recent advancements in large language models (LLMs) have paved the way for automated information extraction in the materials science domain. However, fine-tuning these models, crucial for effective machine learning pipelines in materials science, is hindered by a lack of pre-annotated data. Manual annotation, a laborious process, exacerbates the challenge. To address this, we introduce a tailored semi-automated annotation process, using Google’s Gemini Pro language model. Our approach focuses on two key tasks: extracting information in structured JSON format and generating abstractive summaries from materials science texts. The collaborative process, a symbiotic effort between human annotators and the LLM, driven by structured prompts and user-guided examples, enhances the annotation quality and augments the LLM’s capacity to comprehend materials science intricacies. Importantly, it streamlines human annotation efforts by leveraging the LLM’s proficient starting point.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140940691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Sectional Melt Pool Geometry of Laser Scanned Tracks and Pads on Nickel Alloy 718 for the 2022 Additive Manufacturing Benchmark Challenges","authors":"Jordan S. Weaver, David Deisenroth, Sergey Mekhontsev, Brandon M. Lane, Lyle E. Levine, Ho Yeung","doi":"10.1007/s40192-024-00355-5","DOIUrl":"https://doi.org/10.1007/s40192-024-00355-5","url":null,"abstract":"<p>The Additive Manufacturing Benchmark Series (AM Bench) is a NIST-led organization that provides a continuing series of additive manufacturing benchmark measurements, challenge problems, and conferences with the primary goal of enabling modelers to test their simulations against rigorous, highly controlled additive manufacturing benchmark measurement data. To this end, single-track (1D) and pad (2D) scans on bare plate nickel alloy 718 were completed with thermography, cross-sectional grain orientation and local chemical composition maps, and cross-sectional melt pool size measurements. The laser power, scan speed, and laser spot size were varied for single tracks, and the scan direction was varied for pads. This article focuses on the cross-sectional melt pool size measurements and presents the predictions from challenge problems. Single-track depth correlated with volumetric energy density while width did not (within the studied parameters). The melt pool size for pad scans was greater than single tracks due to heat buildup. Pad scan melt pool depth was reduced when the laser scan direction and gas flow direction were parallel. The melt pool size in pad scans showed little to no trend against position within the pads. Uncertainty budgets for cross-sectional melt pool size from optical micrographs are provided for the purpose of model validation.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140940611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective Optimization-Oriented Generative Adversarial Design for Multi-principal Element Alloys","authors":"Z. Li, N. Birbilis","doi":"10.1007/s40192-024-00354-6","DOIUrl":"https://doi.org/10.1007/s40192-024-00354-6","url":null,"abstract":"<p>The discovery of novel alloys, such as multi-principal element alloys (MPEAs)—inclusive of the so-called high-entropy alloys—remains essential for technological advancement. Multi-principal element alloys can manifest uniquely favorable mechanical properties, but the complexity of their compositions results in their design and performance being challenging to understand. With the emergence of the materials genome concept, there is potential to pursue novel materials using computational design approaches. However, the complexity of such design often requires immense computational power and sophisticated data analysis. In an attempt to address this, we introduce the application of a new framework, the non-dominant sorting optimization-based generative adversarial networks (NSGAN) in the discovery and exploration of novel MPEAs. By harnessing the power of genetic algorithms and generative adversarial networks (GANs), NSGANs offer an effective solution for high-dimensional multi-objective optimization challenges in alloy design. The framework is demonstrated to generate MPEAs according to specific alloy properties. Furthermore, an online web tool/software applies the NSGAN framework to disseminate the methodology to the broader scientific arena (along with the supporting code made available).</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140834192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Material Property Prediction Using Graphs Based on Generically Complete Isometry Invariants","authors":"Jonathan Balasingham, Viktor Zamaraev, Vitaliy Kurlin","doi":"10.1007/s40192-024-00351-9","DOIUrl":"https://doi.org/10.1007/s40192-024-00351-9","url":null,"abstract":"<p>The structure–property hypothesis says that the properties of all materials are determined by an underlying crystal structure. The main obstacle was the ambiguity of conventional crystal representations based on incomplete or discontinuous descriptors that allow false negatives or false positives. This ambiguity was resolved by the ultra-fast pointwise distance distribution, which distinguished all periodic structures in the world’s largest collection of real materials (Cambridge structural database). State-of-the-art results in property prediction were previously achieved by graph neural networks based on various graph representations of periodic crystals, including the Crystal Graph with vertices at all atoms in a crystal unit cell. This work adapts the pointwise distance distribution for a simpler graph whose vertex set is not larger than the asymmetric unit of a crystal structure. The new Distribution Graph reduces mean absolute error by 0.6–12% while having 44–88% of the number of vertices when compared to the Crystal Graph when applied on the Materials Project and Jarvis-DFT datasets using CGCNN and ALIGNN. Methods for hyper-parameters selection for the graph are backed by the theoretical results of the pointwise distance distribution and are then experimentally justified.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140597365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantics-Enabled Data Federation: Bringing Materials Scientists Closer to FAIR Data","authors":"Kareem S. Aggour, Vijay S. Kumar, Vipul K. Gupta, Alfredo Gabaldon, Paul Cuddihy, Varish Mulwad","doi":"10.1007/s40192-024-00348-4","DOIUrl":"https://doi.org/10.1007/s40192-024-00348-4","url":null,"abstract":"<p>The development and discovery of new materials can be significantly enhanced through the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and the establishment of a robust data infrastructure in support of materials informatics. A FAIR data infrastructure and associated best practices empower materials scientists to access and make the most of a wealth of information on materials properties, structures, and behaviors, allowing them to collaborate effectively, and enable data-driven approaches to material discovery. To make data findable, accessible, interoperable, and reusable to materials scientists, we developed and are in the process of expanding a materials data infrastructure to capture, store, and link data to enable a variety of analytics and visualizations. Our infrastructure follows three key architectural design philosophies: (i) capture data across a federated storage layer to minimize the storage footprint and maximize the query performance for each data type, (ii) use a knowledge graph-based data fusion layer to provide a single logical interface above the federated data repositories, and (iii) provide an ensemble of FAIR data access and reuse services atop the knowledge graph to make it easy for materials scientists and other domain experts to explore, use, and derive value from the data. This paper details our architectural approach, open-source technologies used to build the capabilities and services, and describes two applications through which we have successfully demonstrated its use. In the first use case, we created a system to enable additive manufacturing data storage and process parameter optimization with a range of user-friendly visualizations. In the second use case, we created a system for exploring data from cathodic arc deposition experiments to develop a new steam turbine coating material, fusing a combination of materials data with physics-based equations to enable advanced reasoning over the combined knowledge using a natural language chatbot-like user interface.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Measurement for Parameter Discovery in Fused Filament Fabrication","authors":"","doi":"10.1007/s40192-024-00350-w","DOIUrl":"https://doi.org/10.1007/s40192-024-00350-w","url":null,"abstract":"<h3>Abstract</h3> <p>To describe a new method for the automatic generation of process parameters for fused filament fabrication (FFF) across varying machines and materials. We use an instrumented extruder to fit a function that maps nozzle pressures across varying flow rates and temperatures for a given machine and material configuration. We then develop a method to extract real parameters for flow rate and temperature using relative pressures and temperature offsets. Our method allows us to successfully find process parameters, using one set of input parameters, across all of the machine and material configurations that we tested, even in materials that we had never printed before. Rather than using direct parameters in FFF printing, which is time-consuming to tune and modify, it is possible to deploy machine-generated data that captures the fundamental phenomenology of FFF to automatically select parameters.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-Dimensional Prediction of Lack-of-Fusion Porosity Volume Fraction and Morphology for Powder Bed Fusion Additively Manufactured Ti–6Al–4V","authors":"Vamsi Subraveti, Brodan Richter, Saikumar R. Yeratapally, Caglar Oskay","doi":"10.1007/s40192-024-00347-5","DOIUrl":"https://doi.org/10.1007/s40192-024-00347-5","url":null,"abstract":"<p>Powder bed fusion (PBF) is an additive manufacturing technique that has experienced widespread growth in recent years due to various process advantages. However, defects such as porosity and the effects that porosity have on the mechanical performance remain a concern for parts manufactured using PBF. This work develops a three-dimensional framework to simulate lack-of-fusion (LoF) porosity during powder bed fusion using the voxel-based lack-of-fusion model. The framework is calibrated and validated against previously reported LoF porosity measurements and maximum equivalent pore diameter. The framework is used to study the influence of laser power, velocity, hatch spacing, and layer thickness on porosity volume fraction and morphology. Power and velocity have a linear relationship to porosity, and power has a stronger effect than velocity on changing porosity. This stronger effect of power versus velocity contributes to high variability when relating energy density to porosity, and a modified energy density metric that weighs power heavier is shown to reduce variability. In contrast to power and velocity, hatch spacing and layer thickness have a more complicated relationship with porosity, especially at their extrema. The influence of hatch spacing and layer thickness on pore equivalent diameter and sphericity is also explored, and four distinct morphological regimes are characterized. A LoF criteria proposed in a previous work are also confirmed. Overall, the framework offers a methodology to simulate porosity quantity and morphology and interfaces with other process–structure–property prediction techniques to support the design and development of reduced-defect powder bed fusion parts.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Study of Clustering Methods for Nanoindentation Mapping Data","authors":"Mehrnoush Alizade, Rushabh Kheni, Stephen Price, Bryer C. Sousa, Danielle L. Cote, Rodica Neamtu","doi":"10.1007/s40192-024-00349-3","DOIUrl":"https://doi.org/10.1007/s40192-024-00349-3","url":null,"abstract":"<p>Nanoindentation testing and instrumented indentation remain regularly utilized techniques for the assessment of multi-scale mechanical characteristics from load–displacement data analysis, which is central to twenty first century material characterization. The advent of high-resolution nanoindentation-based property mapping has, however, presented challenges in data interpretation, especially when applying proper clustering methodologies to quantify and interpret data as well as draw appropriate conclusions. In this research, we utilized the scikit-learn library in Python to assess the performance of various clustering algorithms, with a focus on nanoindentation-based hardness and elastic modulus measurements, and their synergistic effects. Clustering parameters were meticulously optimized, and in conjunction with domain expert recommendations, the total number of clusters was set to three. The evaluation was grounded in established clustering performance metrics such as the Davies–Bouldin Index, Calinski–Harabasz Index, and the Silhouette score, aiming to ascertain the optimal clustering approach. Among the eight evaluated clustering algorithms, K-means, Agglomerative and FCM emerged as the most effective, while the OPTICS algorithm consistently underperformed for the considered datasets. Augmenting this study, we introduce an intuitive interface, negating the necessity for prior coding or machine learning familiarity, and offering effortless model fine-tuning, visualization, and comparison. This innovation empowers material science and engineering experts, technical staff, and instrumentalists and facilitates the selection of ideal models across varied datasets. The insights and tools presented herein not only enrich material science and engineering research but also lay a robust foundation for sophisticated and dependable analyses in subsequent studies.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community","authors":"Sarah I. Allec, Eric S. Muckley, Nathan S. Johnson, Christopher K. H. Borg, Dylan J. Kirsch, Joshua Martin, Rohit Pant, Ichiro Takeuchi, Andrew S. Lee, James E. Saal, Logan Ward, Apurva Mehta","doi":"10.1007/s40192-024-00345-7","DOIUrl":"https://doi.org/10.1007/s40192-024-00345-7","url":null,"abstract":"<p>Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. Here, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140200296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Throughput Extraction of Phase–Property Relationships from Literature Using Natural Language Processing and Large Language Models","authors":"Luca Montanelli, Vineeth Venugopal, Elsa A. Olivetti, Marat I. Latypov","doi":"10.1007/s40192-024-00344-8","DOIUrl":"https://doi.org/10.1007/s40192-024-00344-8","url":null,"abstract":"<p>Consolidating published research on aluminum alloys into insights about microstructure–property relationships can simplify and reduce the costs involved in alloy design. One critical design consideration for many heat-treatable alloys deriving superior properties from precipitation are phases as key microstructure constituents because they can have a decisive impact on the engineering properties of alloys. Here, we present a computational framework for high-throughput extraction of phases and their impact on properties from scientific papers. Our framework includes transformer-based and large language models to identify sentences with phase-property information in papers, recognize phase and property entities, and extract phase-property relationships and their “sentiment.” We demonstrate the application of our framework on aluminum alloys, for which we build a database of 7,675 phase–property relationships extracted from a corpus of almost 5000 full-text papers. We comment on the extracted relationships based on common metallurgical knowledge.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140165864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}