Integrating Materials and Manufacturing Innovation最新文献

{"title":"Reconstructing Microstructures From Statistical Descriptors Using Neural Cellular Automata","authors":"Paul Seibert, Alexander Raßloff, Yichi Zhang, Karl Kalina, Paul Reck, Daniel Peterseim, Markus Kästner","doi":"10.1007/s40192-023-00335-1","DOIUrl":"https://doi.org/10.1007/s40192-023-00335-1","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The problem of generating microstructures of complex materials in silico has been approached from various directions including simulation, Markov, deep learning and descriptor-based approaches. This work presents a hybrid method that is inspired by all four categories and has interesting scalability properties. A neural cellular automaton is trained to evolve microstructures based on local information. Unlike most machine learning-based approaches, it does not directly require a data set of reference micrographs, but is trained from statistical microstructure descriptors that can stem from a single reference. This means that the training cost scales only with the complexity of the structure and associated descriptors. Since the size of the reconstructed structures can be set during inference, even extremely large structures can be efficiently generated. Similarly, the method is very efficient if many structures are to be reconstructed from the same descriptor for statistical evaluations. The method is formulated and discussed in detail by means of various numerical experiments, demonstrating its utility and scalability.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139495177","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":"PRISMS-Indentation: Multi-scale Elasto-Plastic Virtual Indentation Module","authors":"","doi":"10.1007/s40192-023-00332-4","DOIUrl":"https://doi.org/10.1007/s40192-023-00332-4","url":null,"abstract":"<h3>Abstract</h3> <p>Indentation testing has played a major role for many materials design processes as a convenient and relatively cheap experiment. However, extracting the data from indentation tests requires complex post-processing or an integrated simulation and experiment framework. Accordingly, the simulation of indentation has become a post-processing routine for indentation tests. Providing a highly efficient, computationally scalable, and open-source platform for indentation simulation provides invaluable machinery for materials design process. An open-source PRISMS-Indentation module is presented here as a multi-scale elasto-plastic virtual indentation framework. The module is implemented as a part of PRISMS-Plasticity software which covers length scales of macroscopic plasticity and crystal plasticity. The contact problem is handled using a primal–dual active set method. The framework is first tested against analytical solution of Hertzian theory for contact using an isotropic elasticity model. The robustness of the framework is then investigated in simulations of indentation of annealed Cu microstructures. Unstructured meshes with hexahedral elements and variable mesh density are used to demonstrate potential for speedup in indentation simulations.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139501435","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":"Phase Identification in Synchrotron X-ray Diffraction Patterns of Ti–6Al–4V Using Computer Vision and Deep Learning","authors":"Weiqi Yue, Pawan K. Tripathi, Gabriel Ponon, Zhuldyz Ualikhankyzy, Donald W. Brown, Bjorn Clausen, Maria Strantza, Darren C. Pagan, Matthew A. Willard, Frank Ernst, Erman Ayday, Vipin Chaudhary, Roger H. French","doi":"10.1007/s40192-023-00328-0","DOIUrl":"https://doi.org/10.1007/s40192-023-00328-0","url":null,"abstract":"<p>X-ray diffraction patterns contain information about the atomistic structure and microstructure (defect population) of materials, extracting detailed information from diffraction patterns is complex, demanding and relies on prior knowledge. We hypothesize that deep-learning techniques can help to perform an effective and accurate analysis with high throughput rates. To demonstrate this concept, we applied a novel deep learning framework to determine the evolution of the <span>(upbeta )</span>-phase volume fraction in a Ti–6Al–4V alloy during heat-treatment from video sequences of 2D diffraction patterns recorded in transmission and with highly monochromatic radiation in a synchrotron beamline. In particular, we studied the impact of <i>network design</i> on prediction reliability and computational performance. Networks of different architectures were trained using 3008 experimental 2D patterns. A well-tuned model was found to reproduce the phase fractions of another experimental data set, consisting of 1100 diffraction patterns, with a mean-square error as small as <span>(2.6 times 10^{-4})</span>. The average prediction error of <span>(upbeta )</span>-phase volume fraction was within <span>(1.6 times 10^{-2})</span> (in each diffraction pattern) of the values obtained by conventional methods. Our work demonstrates that convolutional neural networks can evaluate high energy X-ray diffraction patterns with a remarkable level of reliability. Furthermore, it demonstrates the significance of network design on the reliability of predictions and computational performance. The most complex models do not necessarily result in highest accuracy and may even fail to learn from the data.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139475614","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":"AM Bench 2022 Macroscale Tensile Challenge at Different Orientations (CHAL-AMB2022-04-MaTTO) and Summary of Predictions","authors":"Newell Moser, Jake Benzing, Orion L. Kafka, Jordan Weaver, Nicholas Derimow, Ross Rentz, Nikolas Hrabe","doi":"10.1007/s40192-023-00333-3","DOIUrl":"https://doi.org/10.1007/s40192-023-00333-3","url":null,"abstract":"<p>The additive manufacturing benchmarking challenge described in this work was aimed at the prediction of average stress–strain properties for tensile specimens that were excised from blocks of non-heat-treated IN625 manufactured by laser powder bed fusion. Two different laser scan strategies were considered: an X-only raster and an XY raster, which involved a 90<span>(^circ )</span> rotation in the scan direction between subsequent layers. To measure anisotropy, multiple tensile orientations with respect to the build direction were investigated (e.g., parallel, perpendicular, and intervals in between). Benchmark participants were provided grain structure information via electron backscatter diffraction measurements, as well as the stress–strain response for tensile specimens manufactured parallel to the build direction and produced by the XY scan strategy. Then, participants were asked to predict tensile properties, like the ultimate tensile strength, for the remaining specimens and orientations. Interestingly, the measured mechanical properties did not vary linearly as a function of tensile orientation. Moreover, specimens manufactured with the XY scan strategy exhibited greater yield strength than those corresponding to the X-only scan strategy, regardless of orientation. The benchmark data have been made publicly available for anyone that is interested [1]. For the modeling aspect of the challenge, five teams participated in this benchmark. While most of the models incorporated a crystal plasticity framework, one team chose to use a more semiempirical approach and to great success. However, no team excelled at all the predictions, and all teams were seemingly challenged with the predictions associated with the X-only scan strategy.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139475578","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":"Beyond Combinatorial Materials Science: The 100 Prisoners Problem","authors":"","doi":"10.1007/s40192-023-00330-6","DOIUrl":"https://doi.org/10.1007/s40192-023-00330-6","url":null,"abstract":"<h3>Abstract</h3> <p>Advancements in high-throughput data generation and physics-informed artificial intelligence and machine-learning algorithms are rapidly challenging the status quo for how materials data is collected, analyzed, and communicated with the world. Machine-learning algorithms can be executed in just a few lines of code by researchers with minimal data science expertise. This perspective addresses the reality that the ecosystems which have been constructed to nurture new materials discovery and development are not yet well equipped to take advantage of the radically more powerful and accessible computational and algorithmic tools which have the immediate potential to enhance the pace of scientific advancement in this field. A novel architecture for managing materials data is proposed and discussed from the standpoint of how historical and emerging subfields of materials science could have been or might still significantly improve the impact of materials discoveries to the many human societal needs for new materials.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139397262","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":"Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy","authors":"Shoieb Ahmed Chowdhury, M. F. N. Taufique, Jing Wang, Marissa Masden, Madison Wenzlick, Ram Devanathan, Alan L. Schemer-Kohrn, Keerti S. Kappagantula","doi":"10.1007/s40192-023-00305-7","DOIUrl":"https://doi.org/10.1007/s40192-023-00305-7","url":null,"abstract":"<p>Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN)-based deep learning models is a powerful technique to detect features from material micrographs in an automated manner. In contrast to microstructural classification, supervised CNN models for segmentation tasks require pixel-wise annotation labels. However, manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. Microstructural characterization especially needs to be expedited for faster material discovery by changing alloy compositions. In this study, we attempt to overcome such limitations by utilizing multimodal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy images of 347H stainless steel as training data and electron backscatter diffraction micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. The viability of our method is evaluated by considering a set of deep CNN architectures. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that naïve pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139409452","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":"Robust, Co-design Exploration of Multilevel Product, Material, and Manufacturing Process Systems","authors":"","doi":"10.1007/s40192-023-00324-4","DOIUrl":"https://doi.org/10.1007/s40192-023-00324-4","url":null,"abstract":"<h3>Abstract</h3> <p>Achieving targeted product performance requires the integrated exploration of design spaces across multiple levels of decision-making in systems comprising products, materials, and manufacturing processes—product-material-manufacturing process (PMMP) systems. This demands the capability to co-design PMMP systems, that is, share ranged sets of design solutions among distributed product, material, and manufacturing process designers. PMMP systems are subject to uncertainties in processing, microstructure, and models employed. Facilitating co-design requires support for simultaneously exploring high-dimensional design spaces across multiple levels under uncertainty. In this paper, we present the Co-Design Exploration of Multilevel PMMP systems under Uncertainty (CoDE-MU) framework to facilitate the simultaneous exploration of high-dimensional design spaces across multiple levels under uncertainty. The CoDE-MU framework is a machine learning-enhanced, robust co-design exploration framework that integrates robust, coupled compromise Decision Support Problem (rc-cDSP) construct with interpretable Self-Organizing Maps (iSOM). The framework supports multidisciplinary designers to (i) understand the multilevel interactions, (ii) identify the process mechanisms that affect material and product responses, and (iii) provide decision support for problems involving many goals with different behaviors across multiple levels and uncertainty. We use an industry-inspired hot rod rolling (HRR) steel manufacturing process chain problem to showcase the CoDE-MU framework’s efficacy in facilitating the simultaneous exploration of the product, material, and manufacturing process design spaces across multiple levels under uncertainty. The framework is generic and facilitates the co-design of multilevel PMMP systems characterized by hierarchical product-material-manufacturing process relations and many goals with different behaviors that must be realized simultaneously at individual levels.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139064138","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":"Automated Segmentation and Chord Length Distribution of Melt Pools in Complex 3D Printed Metal Artifacts","authors":"","doi":"10.1007/s40192-023-00329-z","DOIUrl":"https://doi.org/10.1007/s40192-023-00329-z","url":null,"abstract":"<h3>Abstract</h3> <p>We present a new computational approach for large-scale segmentation and spatially-resolved analysis of melt pools in complex 3D printed parts and qualification artifacts. Our hybrid segmentation includes human-in-the-loop image processing of a few representative optical images of melt pools that are then used for training machine learning models for automated segmentation of melt pool boundaries in large parts. Our approach specifically targets minimizing the need for manual annotation. Considering imperfect segmentation and errors unavoidable with most algorithms, we further propose chord length distribution as a statistical description of melt pool sizes relatively tolerant to segmentation errors. We first show and validate our new approach on optical images of melt pools in a simple 3D printed plate sample (IN718 alloy) as well as selected regions of a complex qualification artifact (AlSi10Mg alloy). We then demonstrate the application of our approach on an entire cross section of the artifact.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139057019","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":"Temperature-Dependent Material Property Databases for Marine Steels—Part 5: HY-80","authors":"Jennifer K. Semple, D. H. Bechetti, Wei Zhang, J. E. Norkett, Charles R. Fisher","doi":"10.1007/s40192-023-00326-2","DOIUrl":"https://doi.org/10.1007/s40192-023-00326-2","url":null,"abstract":"","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138952660","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":"An Integrated Multiscale Model to Study the Marangoni Effect on Molten Pool and Microstructure Evolution","authors":"Chuanzhen Ma, Ruijie Zhang, Zixin Li, Xue Jiang, Yongwei Wang, Cong Zhang, Haiqing Yin, Xuanhui Qu","doi":"10.1007/s40192-023-00327-1","DOIUrl":"https://doi.org/10.1007/s40192-023-00327-1","url":null,"abstract":"<p>Microstructure plays a crucial role in predicting the properties of parts by additive manufacturing. Fluid flow and temperature gradient are always recognized as key factors influencing the final microstructure. However, the effects of flow field were often ignored during microstructure simulation inside the molten pool. In this study, the Marangoni flow is firstly calculated using the finite element method. Fluid flow increases the temperature gradient and the cooling rate at the solid front. Subsequently, the temperature field and flow field are input to phase-field model to simulate the microstructure inside the molten pool. This integrated model is then applied to study the solidification behavior of IN718 alloy during additive manufacturing. The microstructure evolutions are analyzed in detail under different processing parameters. The simulation results demonstrate that the Marangoni flow has great effects on both molten pool and solidification microstructure. The integrated model developed in this work can predict the molten pool and solidification microstructure more accurately by combining the thermal, flow and microstructure models together.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138632719","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}