Integrating Materials and Manufacturing Innovation

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High resolution micrograph synthesis using a parametric texture model and a particle filter
Integrating Materials and Manufacturing Innovation - Tập 2 - Trang 36-53 - 2013
Ramakrishna Tipireddy, Roger Ghanem, Somnath Ghosh, Daniel Paquet
We present a methodology for synthesizing high resolution micrographs from low resolution ones using a parametric texture model and a particle filter. Information contained in high resolution micrographs is relevant to the accurate prediction of microstructural behavior and the nucleation of instabilities. As these micrographs may be tedious and uneconomical to obtain over an extended spatial doma...... hiện toàn bộ
Role of cyberinfrastructure in educating the next generation of computational materials scientists
Integrating Materials and Manufacturing Innovation - Tập 3 - Trang 85-89 - 2014
Susan B Sinnott, Simon R Phillpot
An overview of cyberinfrastructures developed to advance the field of materials modeling is presented. The role of cyberinfrastructures in educating the next generation of the workforce is also discussed, with an emphasis on the Cyberinfrastructure for Atomistic Simulation (CAMS). The paper concludes with a summary regarding the future outlook of cyberinfrastructures, especially with regard to edu...... hiện toàn bộ
AixViPMaP®—an Operational Platform for Microstructure Modeling Workflows
Integrating Materials and Manufacturing Innovation - Tập 8 - Trang 122-143 - 2019
L. Koschmieder, S. Hojda, M. Apel, R. Altenfeld, Y. Bami, C. Haase, M. Lin, A. Vuppala, G. Hirt, G.J. Schmitz
The present article describes design, architecture, and implementation of the Aachen (“Aix”) Virtual Platform for Materials Processing—AixViPMaP®. This simulation platform focuses on enabling automatic simulation workflows in the area of microstructure evolution and microstructure property relationships by continuum models. Following a description of a variety AixViPMaP® functionalities like user ...... hiện toàn bộ
A Computational Framework for Material Design
Integrating Materials and Manufacturing Innovation - Tập 6 - Trang 229-248 - 2017
Shengyen Li, Ursula R. Kattner, Carelyn E. Campbell
A computational framework is proposed that enables the integration of experimental and computational data, a variety of user-selected models, and a computer algorithm to direct a design optimization. To demonstrate this framework, a sample design of a ternary Ni-Al-Cr alloy with a high work-to-necking ratio is presented. This design example illustrates how CALPHAD phase-based, composition and temp...... hiện toàn bộ
Improving Steel and Steelmaking—an Ionic Liquid Database for Alloy Process Design
Integrating Materials and Manufacturing Innovation - Tập 7 - Trang 195-201 - 2018
David Dilner, Lina Kjellqvist, Huahai Mao, Malin Selleby
The latest development of a thermodynamic database is demonstrated with application examples related to the steelmaking process and steel property predictions. The database, TCOX, has comprehensive descriptions of the solution phases using ionic models. More specifically, applications involving sulphur and oxygen, separately as well as combined, are presented and compared with relevant multi-compo...... hiện toàn bộ
h5ebsd: an archival data format for electron back-scatter diffraction data sets
Integrating Materials and Manufacturing Innovation - Tập 3 - Trang 44-55 - 2014
Michael A Jackson, Michael A Groeber, Michael D Uchic, David J Rowenhorst, Marc De Graef
We present an archival format for electron back-scatter diffraction (EBSD) data based on the HDF5 scientific file format. We discuss the differences between archival and data work flow file formats, and present details of the archival file layout for the implementation of h5ebsd, a vendor-neutral EBSD-HDF5 format. Information on sample and external reference frames can be included in the archival ...... hiện toàn bộ
Prediction of microstructure evolution during multi-stand shape rolling of nickel-base superalloys
Integrating Materials and Manufacturing Innovation - - 2014
Kannan Subramanian, Harish P. Cherukuri
AbstractIn this paper, a comprehensive numerical approach to predict the microstructure of nickel-base superalloys during multi-stand shape rolling is presented. This approach takes into account the severe deformation that occurs during each pass and also the possible reheating between passes. In predicting the grain size at the end of the rolling process, microstr...... hiện toàn bộ
The BAREFOOT Optimization Framework
Integrating Materials and Manufacturing Innovation - Tập 10 - Trang 644-660 - 2021
Richard Couperthwaite, Danial Khatamsaz, Abhilash Molkeri, Jaylen James, Ankit Srivastava, Douglas Allaire, Raymundo Arróyave
This work presents a description of the Batch Reification/Fusion Optimization Framework (BAREFOOT). BAREFOOT is a Bayesian Optimization (BO) Framework that has been built specifically for the aim of material optimization and design. The Framework combines multi-fidelity model fusion with batch BO to enable accelerated materials design. The Framework is built in Python and is available as open-sour...... hiện toàn bộ
Phân loại hình ảnh vi cấu trúc: Một phương pháp kết hợp bộ phân loại sử dụng thước đo tích phân mờ Dịch bởi AI
Integrating Materials and Manufacturing Innovation - Tập 10 - Trang 286-298 - 2021
Shib Sankar Sarkar, Md. Salman Ansari, Arpan Mahanty, Kalyani Mali, Ram Sarkar
Gần đây, các phương pháp dựa trên học máy đã trở nên phổ biến trong nhiều ứng dụng khoa học vật liệu, bao gồm phân loại hình ảnh vi cấu trúc. Bài báo này khám phá việc sử dụng các phương pháp kết hợp bộ phân loại để phân loại hình ảnh vi cấu trúc với độ chính xác cao hơn. Các phương pháp kết hợp bộ phân loại được công nhận là một phương pháp tiên tiến giúp nâng cao hiệu suất của nhiều nhiệm vụ phâ...... hiện toàn bộ
#phân loại hình ảnh #học máy #vi cấu trúc #bộ phân loại #thước đo tích phân mờ
Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data
Integrating Materials and Manufacturing Innovation - Tập 10 - Trang 231-244 - 2021
Ryan Cohn, Elizabeth Holm
Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled datasets and for achieving maximum machine learning performance. This paper demonstrates how to construct, use, and evaluate a high-performance unsupervised machine learning system for classifying images in a popular microstructural dataset. The Northeastern University Steel Surface Defects Datab...... hiện toàn bộ
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