Pub Date : 2023-11-15DOI: 10.1007/s40192-023-00319-1
Luis M. Reig Buades, Martin P. Persson
Developing accurate process–structure–property models for metal additive manufacturing is crucial due to the numerous process parameters, extended build times, and high material costs which make it impractical to rely solely on an experimental trial and error approach when optimizing the process. In this work, a multiscale digital approach to estimate tensile anisotropy along selective laser melted titanium meta-stable (beta ) alloys is presented. The approach uses a component scale thermal FEA model of the process to calculate temperature, a meso-scale phase field model to calculate microstructure evolution, and a microscale crystal plasticity model to calculate the effect of texture on the tensile properties in different directions. The model has predicted isotropic yield strength for this material, which could guide designers to choose orientations freely. However, anisotropy in hardening behavior could be expected but is caused by porosity and cracking, which are not considered in the presented models. We believe the presented approach, which relies solely on easy to use commercial simulation tools, lays a good foundation for the development of process–structure–property models to optimize process parameters. The modeling approach should be applicable to other mechanical properties and materials with appropriate considerations.
{"title":"Process–Structure–Property Simulation Approach to the Estimation of Tensile Anisotropy in 3D Printed Meta-stable $$beta $$ Titanium Alloy","authors":"Luis M. Reig Buades, Martin P. Persson","doi":"10.1007/s40192-023-00319-1","DOIUrl":"https://doi.org/10.1007/s40192-023-00319-1","url":null,"abstract":"<p>Developing accurate process–structure–property models for metal additive manufacturing is crucial due to the numerous process parameters, extended build times, and high material costs which make it impractical to rely solely on an experimental trial and error approach when optimizing the process. In this work, a multiscale digital approach to estimate tensile anisotropy along selective laser melted titanium meta-stable <span>(beta )</span> alloys is presented. The approach uses a component scale thermal FEA model of the process to calculate temperature, a meso-scale phase field model to calculate microstructure evolution, and a microscale crystal plasticity model to calculate the effect of texture on the tensile properties in different directions. The model has predicted isotropic yield strength for this material, which could guide designers to choose orientations freely. However, anisotropy in hardening behavior could be expected but is caused by porosity and cracking, which are not considered in the presented models. We believe the presented approach, which relies solely on easy to use commercial simulation tools, lays a good foundation for the development of process–structure–property models to optimize process parameters. The modeling approach should be applicable to other mechanical properties and materials with appropriate considerations.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"17 8","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138512556","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}
Pub Date : 2023-11-10DOI: 10.1007/s40192-023-00318-2
Lucia Scotti, Hector Basoalto, James Moffat, Daniel Cogswell
Abstract 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 regarding the design or optimization of new products and manufacturing processes. At the same time, it reduces product cost and development time. However, highlighting the revenue brought by using such tools is not trivial, especially because they mainly affect the complex activities such as the innovation process, whose return only becomes available in the long run and it is difficult to measure. This means that the materials modeling field is often overlooked in an industry setting, where it is not integrated in the company workflow. In some cases, modeling provides the potential to capture tacit knowledge preventing the loss of capability in an aging specialist community, that why its industrial integration is important. This paper explores the reason behind this dichotomy, presenting first what it is intended for the modeling process, and the main types used in materials application. The current industrial adoption is reviewed by outlining success stories, economic impact, business uptake, and barriers. Past and current approaches and strategies are also presented and discussed. In prospective, materials modeling plays a key role in developing material-centric industry for sustainable economy, providing physical understating (physics-based models) and fast approaches (data-driven solutions). Digitalization is the mean for the green economy and it needs to push for a more integration at the core of the business of materials modeling.
{"title":"Review of Material Modeling and Digitalization in Industry: Barriers and Perspectives","authors":"Lucia Scotti, Hector Basoalto, James Moffat, Daniel Cogswell","doi":"10.1007/s40192-023-00318-2","DOIUrl":"https://doi.org/10.1007/s40192-023-00318-2","url":null,"abstract":"Abstract 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 regarding the design or optimization of new products and manufacturing processes. At the same time, it reduces product cost and development time. However, highlighting the revenue brought by using such tools is not trivial, especially because they mainly affect the complex activities such as the innovation process, whose return only becomes available in the long run and it is difficult to measure. This means that the materials modeling field is often overlooked in an industry setting, where it is not integrated in the company workflow. In some cases, modeling provides the potential to capture tacit knowledge preventing the loss of capability in an aging specialist community, that why its industrial integration is important. This paper explores the reason behind this dichotomy, presenting first what it is intended for the modeling process, and the main types used in materials application. The current industrial adoption is reviewed by outlining success stories, economic impact, business uptake, and barriers. Past and current approaches and strategies are also presented and discussed. In prospective, materials modeling plays a key role in developing material-centric industry for sustainable economy, providing physical understating (physics-based models) and fast approaches (data-driven solutions). Digitalization is the mean for the green economy and it needs to push for a more integration at the core of the business of materials modeling.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"106 32","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136525","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}
Pub Date : 2023-11-07DOI: 10.1007/s40192-023-00317-3
Shambhu Kushwaha, M. Agilan, M. R. Rahul, Gandham Phanikumar
{"title":"Study of TIG Weld Microstructure Formation in Inconel 718 Alloy Using ICME Approach","authors":"Shambhu Kushwaha, M. Agilan, M. R. Rahul, Gandham Phanikumar","doi":"10.1007/s40192-023-00317-3","DOIUrl":"https://doi.org/10.1007/s40192-023-00317-3","url":null,"abstract":"","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"40 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135432248","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}
Pub Date : 2023-11-03DOI: 10.1007/s40192-023-00316-4
Yagnik Kalariya, Soban Babu Beemaraj, Amit Salvi
{"title":"Design and Optimization of Manufacturing Process of Polymer Composites Through Multiscale Cure Analysis and NSGA-II","authors":"Yagnik Kalariya, Soban Babu Beemaraj, Amit Salvi","doi":"10.1007/s40192-023-00316-4","DOIUrl":"https://doi.org/10.1007/s40192-023-00316-4","url":null,"abstract":"","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"1 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135873623","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}
Pub Date : 2023-11-01DOI: 10.1007/s40192-023-00309-3
N. I. Palya, K. A. Fraser, Y. Hong, N. Zhu, M. B. Williams, K. Doherty, P. G. Allison, J. B. Jordon
Abstract 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 understanding of the evolving microstructures is necessary to predict material performance, the elevated temperatures and strain rates associated with severe plastic deformation processes (SPDP) make accurate collection of experimental data within AFSD difficult. Without the ability to experimentally determine material history within the AFSD process, an SPH model was employed to predict the thermomechanical history. The SPH simulation of an AFSD repair was used to inform several microstructural models to predict material history during and after processing with AFSD and a post-processing heat treatment. These microstructure models are then used to inform a mechanistic microstructure and performance model to predict the fatigue life of an AFSD repair in AA7075.
{"title":"Multi-physics Approach to Predict Fatigue Behavior of High Strength Aluminum Alloy Repaired via Additive Friction Stir Deposition","authors":"N. I. Palya, K. A. Fraser, Y. Hong, N. Zhu, M. B. Williams, K. Doherty, P. G. Allison, J. B. Jordon","doi":"10.1007/s40192-023-00309-3","DOIUrl":"https://doi.org/10.1007/s40192-023-00309-3","url":null,"abstract":"Abstract 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 understanding of the evolving microstructures is necessary to predict material performance, the elevated temperatures and strain rates associated with severe plastic deformation processes (SPDP) make accurate collection of experimental data within AFSD difficult. Without the ability to experimentally determine material history within the AFSD process, an SPH model was employed to predict the thermomechanical history. The SPH simulation of an AFSD repair was used to inform several microstructural models to predict material history during and after processing with AFSD and a post-processing heat treatment. These microstructure models are then used to inform a mechanistic microstructure and performance model to predict the fatigue life of an AFSD repair in AA7075.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"18 7-8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135326243","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}
Pub Date : 2023-10-26DOI: 10.1007/s40192-023-00315-5
Kimberly L. Bassett, Tylan Watkins, Jonathan Coleman, Nathan Bianco, Lauren S. Bailey, Jamin Pillars, Samuel Garrett Williams, Tomas F. Babuska, John Curry, Frank W. DelRio, Amelia A. Henriksen, Anthony Garland, Justin Hall, Brandon A. Krick, Brad L. Boyce
Abstract 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 critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategies that are difficult to generalize. Herein we describe an implemented workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni–Fe thin films resulting in a data cube with over 160,000 individual data files. Our acceleration strategies do not require manufacturing-scale resources and are thus amenable to typical materials research facilities in academic, government, or commercial laboratories. The workflow demonstrated the acceleration of six characterization modalities: optical microscopy, laser profilometry, X-ray diffraction, X-ray fluorescence, nanoindentation, and tribological (friction and wear) testing, each with speedup factors ranging from 13–46x. In addition, automated data upload to a repository using FAIR data principles was accelerated by 64x.
{"title":"A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films","authors":"Kimberly L. Bassett, Tylan Watkins, Jonathan Coleman, Nathan Bianco, Lauren S. Bailey, Jamin Pillars, Samuel Garrett Williams, Tomas F. Babuska, John Curry, Frank W. DelRio, Amelia A. Henriksen, Anthony Garland, Justin Hall, Brandon A. Krick, Brad L. Boyce","doi":"10.1007/s40192-023-00315-5","DOIUrl":"https://doi.org/10.1007/s40192-023-00315-5","url":null,"abstract":"Abstract 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 critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategies that are difficult to generalize. Herein we describe an implemented workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni–Fe thin films resulting in a data cube with over 160,000 individual data files. Our acceleration strategies do not require manufacturing-scale resources and are thus amenable to typical materials research facilities in academic, government, or commercial laboratories. The workflow demonstrated the acceleration of six characterization modalities: optical microscopy, laser profilometry, X-ray diffraction, X-ray fluorescence, nanoindentation, and tribological (friction and wear) testing, each with speedup factors ranging from 13–46x. In addition, automated data upload to a repository using FAIR data principles was accelerated by 64x.","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134908528","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}
Pub Date : 2023-10-26DOI: 10.1007/s40192-023-00311-9
Heather A. Murdoch, Daniel M. Field, Benjamin A. Szajewski, Levi D. McClenny, Andrew Garza, Berend C. Rinderspacher, Mulugeta A. Haile, Krista R. Limmer
{"title":"Tempered Hardness Optimization of Martensitic Alloy Steels","authors":"Heather A. Murdoch, Daniel M. Field, Benjamin A. Szajewski, Levi D. McClenny, Andrew Garza, Berend C. Rinderspacher, Mulugeta A. Haile, Krista R. Limmer","doi":"10.1007/s40192-023-00311-9","DOIUrl":"https://doi.org/10.1007/s40192-023-00311-9","url":null,"abstract":"","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"31 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134910155","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}
Pub Date : 2023-10-25DOI: 10.1007/s40192-023-00313-7
Rangasayee Kannan, Peeyush Nandwana
{"title":"Predicting Sintering Window of Binder Jet Additively Manufactured Parts Using a Coupled Data Analytics and CALPHAD Approach","authors":"Rangasayee Kannan, Peeyush Nandwana","doi":"10.1007/s40192-023-00313-7","DOIUrl":"https://doi.org/10.1007/s40192-023-00313-7","url":null,"abstract":"","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"1 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135111326","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}
Pub Date : 2023-10-23DOI: 10.1007/s40192-023-00314-6
Sebastian Carrion Ständer, Nora Barschkett, Evgeniya Kabliman
Abstract 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 material and resources. Conducting computational HTS involves the application of high-throughput computing (HTC) and developing suitable tools to handle such calculations. Among multiple ICME methods compatible with HTS and HTC, the calculation of phase diagrams known as the CALPHAD method has gained prominence. When combining thermodynamic modeling with kinetic simulations, predicting the entire history of precipitation behavior is possible. However, most reported CALPHAD-based HTS frameworks are restricted to thermodynamic modeling or not accessible. The present work introduces CAROUSEL—an open-sourCe frAmewoRk fOr high-throUghput microStructurE simuLations. It is designed to explore various alloy compositions, processing parameters, and CALPHAD implementations. CAROUSEL offers a graphical interface for easy interaction, scripting workflow for advanced simulations, the calculation distribution system, and simulation data management. Additionally, CAROUSEL incorporates visual tools for exploring the generated data and integrates through-process modeling, accounting for the interplay between solidification and solid-state precipitation. The application area is various metal manufacturing processes where the precipitation behavior is crucial. The results of simulations can be used in upscale material models, thus covering different microstructural phenomena. The present work demonstrates how CAROUSEL can be used for additive manufacturing (AM), particularly for investigating different chemical compositions and heat treatment parameters (e.g., temperature, duration).
{"title":"CAROUSEL: An Open-Source Framework for High-Throughput Microstructure Simulations","authors":"Sebastian Carrion Ständer, Nora Barschkett, Evgeniya Kabliman","doi":"10.1007/s40192-023-00314-6","DOIUrl":"https://doi.org/10.1007/s40192-023-00314-6","url":null,"abstract":"Abstract 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 material and resources. Conducting computational HTS involves the application of high-throughput computing (HTC) and developing suitable tools to handle such calculations. Among multiple ICME methods compatible with HTS and HTC, the calculation of phase diagrams known as the CALPHAD method has gained prominence. When combining thermodynamic modeling with kinetic simulations, predicting the entire history of precipitation behavior is possible. However, most reported CALPHAD-based HTS frameworks are restricted to thermodynamic modeling or not accessible. The present work introduces CAROUSEL—an open-sourCe frAmewoRk fOr high-throUghput microStructurE simuLations. It is designed to explore various alloy compositions, processing parameters, and CALPHAD implementations. CAROUSEL offers a graphical interface for easy interaction, scripting workflow for advanced simulations, the calculation distribution system, and simulation data management. Additionally, CAROUSEL incorporates visual tools for exploring the generated data and integrates through-process modeling, accounting for the interplay between solidification and solid-state precipitation. The application area is various metal manufacturing processes where the precipitation behavior is crucial. The results of simulations can be used in upscale material models, thus covering different microstructural phenomena. The present work demonstrates how CAROUSEL can be used for additive manufacturing (AM), particularly for investigating different chemical compositions and heat treatment parameters (e.g., temperature, duration).","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135412672","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}
Pub Date : 2023-10-06DOI: 10.1007/s40192-023-00310-w
Maxwell Praniewicz, Jason C. Fox, Jared Tarr
{"title":"Part Deflection Measurements of AM‑Bench IN718 3D Build Artifacts","authors":"Maxwell Praniewicz, Jason C. Fox, Jared Tarr","doi":"10.1007/s40192-023-00310-w","DOIUrl":"https://doi.org/10.1007/s40192-023-00310-w","url":null,"abstract":"","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135346895","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号