Integrating Materials and Manufacturing Innovation最新文献

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.

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.

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.

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.

Additive manufacturing (AM) can create net or near-net-shaped components while simultaneously building the material microstructure, therefore closely coupling forming the material and shaping the part in contrast to traditional manufacturing with distinction between the two processes. While there are well-heralded benefits to AM, the widespread adoption of AM in fatigue-limited applications is hindered by defects such as porosity resulting from off-nominal process conditions. The vast number of AM process parameters and conditions make it challenging to capture variability in porosity that drives fatigue design allowables during qualification. Furthermore, geometric features such as overhangs and thin walls influence local heat conductivity and thereby impact local defects and microstructure. Consequently, qualifying AM material within parts in terms of material properties is not always a straightforward task. This article presents an approach for rapid qualification of AM fatigue-limited parts and includes three main aspects: (1) seeding pore defects of specific size, distribution, and morphology into AM specimens, (2) combining non-destructive and destructive techniques for material characterization and mechanical fatigue testing, and (3) conducting microstructure-based simulations of fatigue behavior resulting from specific pore defect and microstructure combinations. The proposed approach enables simulated data to be generated to validate and/or augment experimental fatigue data sets with the intent to reduce the number of tests needed and promote a more rapid route to AM material qualification. Additionally, this work suggests a closer coupling between material qualification and part certification for determining material properties at distinct regions within an AM part.

Phase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network architecture, achieving 0.97 pixel-wise classification accuracy as a measure of the correctly classified pixels. The growth kinetics of isolated and impinged particles were tracked throughout time using these image sequences. The relationship between image sequences and spatiotemporal graph representations was explored to identify the proximity of crystallites from each other. The framework enables the analysis of all image sequences without the requirement of sampling for specific particles or timesteps for various materials systems.

The availability of large, diverse datasets has enabled transformative advances in a wide variety of technical fields by unlocking data scientific and machine learning techniques. In Materials Informatics for Heterogeneous Microstructures capitalization on these techniques has been limited due to the extreme complexity of generating or curating sizeable heterogeneous microstructure datasets. Historically, this difficulty can be attributed to two main hurdles: quantification (i.e., measuring microstructure diversity) and curation (i.e., generating diverse microstructures). In this paper, we present a framework for curating large, statistically diverse mesoscale microstructure datasets composed of 2-phase microstructures. The framework generates microstructures which are statistically diverse with respect to their n-point statistics—the primary emphasis is on diversity in their 2-point statistics. The framework’s foundation is a proposed set of algorithms for synthesizing salient 2-point statistics and neighborhood distributions. We generate statistically diverse microstructures by using the outputs of these algorithms as inputs to a statistically conditioned Local-Global Decomposition generation procedure. Finally, we demonstrate the proposed framework by curating MICRO2D, a diverse, large-scale, and open source heterogeneous microstructure dataset comprised of 87, 379 2-phase microstructures. The contained microstructures are periodic and (256 times 256) pixels. The dataset also contains salient homogenized elastic and thermal properties computed across a range of constituent contrast ratios for each microstructure. Using MICRO2D, we analyze the statistical and property diversity achievable via the proposed framework. We conclude by discussing important areas of future research in microstructure dataset curation.

Abstract

Integrated Computational Materials Engineering (ICME)-based tools and techniques have been identified as the best path forward for distortion mitigation in thin-plate steel construction at shipyards. ICME tools require temperature-dependent material properties—including specific heat, thermal conductivity, coefficient of thermal expansion, elastic modulus, yield strength, flow stress, and microstructural evolution—to achieve accurate computational results for distortion and residual stress. However, the required temperature-dependent material property databases of U.S. Navy-relevant steels are not available in the literature. Therefore, a comprehensive testing plan for some of the most common marine steels used in the construction of U.S. Naval vessels was completed. This testing plan included DH36, HSLA-65, HSLA-80, HSLA-100, HY-80, and HY-100 steel with a nominal thickness of 4.76 mm (3/16-in.). This report is the sixth part of a seven-part series detailing the pedigreed steel data. The first six reports will report the material properties for each of the individual steel grades, whereas the final report will compare and contrast the measured steel properties across all six steels. This report will focus specifically on the data associated with HY-100 steel.

Additive manufacturing (AM) leverages emerging technologies and well-adopted processes to produce near-net-shape products. The advancement of AM technology requires data management tools to collect, store, and share information through the product development lifecycle and across the material and machine value chain. To address the need for sharing data among AM developers and practitioners, an AM common data dictionary (AM-CDD) was first developed based on community consensus to provide a common lexicon for AM, and later standardized by ASTM International. Following the AM-CDD work, the development of a common data model (AM-CDM) defining the structure and relationships of the key concepts, and terms in the AM-CDD is being developed. These efforts have greatly facilitated system integrations and AM data exchanges among various organizations. This work outlines the effort to create the AM-CDD and AM-CDM, with a focus on the design of the AM-CDM. Two use cases are provided to demonstrate the adoption of these efforts and the interoperability enabled by the AM-CDM for different engineering applications managed by different types of database technology. In these case studies, the AM-CDM is implemented in two distinct formats to curate AM data from NIST—the first in XML from their additive manufacturing material database and the second in OWL from their 2022 AM bench database. These use cases present the power of the AM-CDM for data representation, querying, and seamless data exchange. Our implementation experiences and some challenges are highlighted that can assist others in future adoptions of the AM-CDM for data integration and data exchange applications.

Presently, material databases construction is a trending topic. We propose to adopt a collaborative and shared model to accelerate building a squeeze casting process database. To achieve co-construction and sharing of the databases, ensure the reliability of data, database operation security, and on-demand access control of data, a secure access control system has been established for squeeze casting process databases based on blockchain technology. The system saves the database data on a local server, implements automatic access control for users through smart contracts, stores user operation records on the blockchain, and ensures that the data is modifiable while the user operation records cannot be tampered with. Because of the inadequate security of traditional transaction processes where data is transmitted as source data, we use asymmetric encryption algorithm to encrypt the source data and transmit ciphertext to improve data sharing security. The system has been developed and implemented, and the security verification experiment has demonstrated the feasibility and effectiveness of the design.