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machine learning
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Journal Articles
AM&P Technical Articles (2020) 178 (5): 32–33.
Published: 01 July 2020
... additive manufacturing artificial intelligence machine learning ADVANCED MATERIALS & PROCESSES | JULY/AUGUST 2020 3 2 httpsdoi.org/10.31399/asm.amp.2020-05.p032 ADDITIVE MANUFACTURING TRENDS: ARTIFICIAL INTELLIGENCE & MACHINE LEARNING To share the latest trends on how additive manufacturing...
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This article is an informal discussion among three industry leaders about how additive manufacturing is impacted by new data-driven technologies. The panelists are Elizabeth A. Holm, James C. Williams, and Edward D. Herderick; the discussion is moderated by Hanchen Huang. The discussion is a preview for an IMAT 2020 panel session on the same topic organized by the ASM Emerging Technologies Awareness Committee.
Journal Articles
AM&P Technical Articles (2021) 179 (1): 16–21.
Published: 01 January 2021
...Martin Müller; Dominik Britz; Frank Mücklich This article demonstrates the application of machine learning to the classification and segmentation of bainitic microstructures and compares three approaches for assigning the ground truth: correlative microscopy using EBSD as an additional information...
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This article demonstrates the application of machine learning to the classification and segmentation of bainitic microstructures and compares three approaches for assigning the ground truth: correlative microscopy using EBSD as an additional information source, comparison with images obtained from specially produced reference samples, and unsupervised learning.
Journal Articles
AM&P Technical Articles (2021) 179 (2): 13–18.
Published: 01 February 2021
...Elizabeth A. Holm; Ryan Cohn; Nan Gao; Andrew R. Kitahara; Bo Lei; Srujana Rao Yarasi; Thomas P. Matson Vision-based machine learning systems for microstructural characterization and analysis are being successfully used for image classification, semantic and instance segmentation, and object...
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Vision-based machine learning systems for microstructural characterization and analysis are being successfully used for image classification, semantic and instance segmentation, and object detection, leading to accurate, autonomous, objective, and repeatable results.
Journal Articles
AM&P Technical Articles (2023) 181 (3): 13–19.
Published: 01 April 2023
...Annie Wang; Zach Simkin; William E. Frazier This article starts with a synopsis of machine learning (ML) and explores the characteristics of ML algorithms. It then reports on the results of two recently completed research projects investigating the potential use of ML to establish additive...
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This article starts with a synopsis of machine learning (ML) and explores the characteristics of ML algorithms. It then reports on the results of two recently completed research projects investigating the potential use of ML to establish additive manufacturing materials property allowables. Although continued research and development work is required, the results are very promising.
Journal Articles
AM&P Technical Articles (2024) 182 (4): 14–20.
Published: 01 May 2024
...Joshua Stuckner; S. Mohadeseh Taheri-Mousavi; James E. Saal This article provides a brief overview of the many ways that artificial intelligence and machine learning are being used for materials and manufacturing research. Several case studies show how the discovery, development, and deployment...
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This article provides a brief overview of the many ways that artificial intelligence and machine learning are being used for materials and manufacturing research. Several case studies show how the discovery, development, and deployment of novel materials are being dramatically accelerated through automation and data-driven models.
Journal Articles
AM&P Technical Articles (2018) 176 (1): 23–26.
Published: 01 January 2018
... analytics, artificial intelligence (machine learning and deep learning), blockchain, digital thread and digital twins, Internet of Things (IoT) or Industry 4.0, additive manufacturing, electric vehicles, and autonomous vehicles. An in-depth understanding of technology trends not only keep materials...
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An in-depth understanding of technology trends not only keep materials engineers relevant, but also puts them in the driver’s seat when it comes to formulating strategies and executing projects. Some of today’s technology trends that are relevant to materials engineers include data analytics, artificial intelligence (machine learning and deep learning), blockchain, digital thread and digital twins, Internet of Things (IoT) or Industry 4.0, additive manufacturing, electric vehicles, and autonomous vehicles.
Journal Articles
AM&P Technical Articles (2019) 177 (7): 16–21.
Published: 01 October 2019
... for additive manufacturing. This article describes the first phase of the probabilistic machine learning framework that was successfully demonstrated to rapidly define optimum parameter sets for commercial high-temperature nickel superalloys, as well as to guide alloy design and selection for compatibility...
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A novel framework including experimental and model-based techniques saves time and enables the introduction of new alloys for additive manufacturing. This article describes the first phase of the probabilistic machine learning framework that was successfully demonstrated to rapidly define optimum parameter sets for commercial high-temperature nickel superalloys, as well as to guide alloy design and selection for compatibility with laser powder bed fusion additive manufacturing.
Journal Articles
AM&P Technical Articles (2023) 181 (1): 23–31.
Published: 01 January 2023
... and industry to develop and integrate advanced manufacturing techniques; methods for in-process monitoring and machine learning; and challenges in adopting new digital manufacturing technologies. Highlights from a member survey and panel discussion outlining the challenges and benefits of advanced...
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Highlights from a member survey and panel discussion outlining the challenges and benefits of advanced manufacturing for the materials community. The article covers research being conducted in academia and industry to develop and integrate advanced manufacturing techniques; methods for in-process monitoring and machine learning; and challenges in adopting new digital manufacturing technologies.
Journal Articles
AM&P Technical Articles (2023) 181 (2): 17–19.
Published: 01 March 2023
... and machine learning to establish a substantial database for modeling. This article describes new software that helps predict properties of high-entropy alloy compositions under high-temperature conditions. The tool was developed by extensive testing on the quinary Al-Co-Cr-Fe-Ni alloy system with both...
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This article describes new software that helps predict properties of high-entropy alloy compositions under high-temperature conditions. The tool was developed by extensive testing on the quinary Al-Co-Cr-Fe-Ni alloy system with both first-principles density functional theory calculations and machine learning to establish a substantial database for modeling.
Journal Articles
AM&P Technical Articles (2022) 180 (2): 21–23.
Published: 01 March 2022
... called Member Market Insights. The layers of this effort started with a ship (Fig. 2). The Materials 4.0 capabilities that showed the greatest member interest in the survey included pragmatic data management and data management education, machine learning, and uncertainty quantification. This led...
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Designed with key input from members, ASM International’s new Data Ecosystem provides a digital platform to assist engineering design and manufacturing stakeholders working in the Materials 4.0 era.
Journal Articles
AM&P Technical Articles (2018) 176 (8): 29–31.
Published: 01 November 2018
... intelligence machine learning ADVANCED MATERIALS & PROCESSES | NOVEMBER/DECEMBER 2018 httpsdoi.org/10.31399/asm.amp.2018-08.p029 PERSPECTIVE INTELLIGENCE TEAMING: 29 SUPER EXCITING, ULTRACOMPETITIVE Synergistic performance can be achieved by integrating judgment-focused humans and prediction-focused AI...
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Synergistic performance can be achieved by integrating judgment-focused humans and prediction-focused AI agents. This article discusses general developments in this area and potential implications for materials innovation in semiconductor devices.
Journal Articles
AM&P Technical Articles (2020) 178 (2): 25–28.
Published: 01 February 2020
... to 2.0. Copyright © ASM International® 2020 2020 ASM International auto body panels data analytics machine learning process monitoring stamping httpsdoi.org/10.31399/asm.amp.2020-02.p025 25 ADVANCED MATERIALS & PROCESSES | FEBRUARY/MARCH 2020 INDUSTRY 4.0 MEETS THE STAMPING LINE Ford...
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Ford Motor Company’s stamping division looks to leap into Industry 4.0 the same way Henry Ford led the transformation from Industry 1.0 to 2.0.
Journal Articles
AM&P Technical Articles (2024) 182 (4): 33–35.
Published: 01 May 2024
... of the materials landscape were reviewed during the planning session, including the circular economy, electric vehicles, in-space manufacturing, metals-based additive manufacturing, materials informatics, as well as artificial intelligence and machine learning. To highlight a few examples of the importance...
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ASM International’s strong strategic plan now includes a focus on financials, to ensure that the Society is ready for the next generation, in addition to building and marketing digital-first products and forming collaborations across disciplines and continents.
Journal Articles
AM&P Technical Articles (2022) 180 (7): 38–41.
Published: 01 October 2022
.... Adapted from Paranjape et al.[3]. path A-B-C shown in Fig. 2b. A regression model is fitted using the support vector machine (SVM) machine learning (ML) method that takes a set of six material CALIBRATION RESULTS USING GLOBAL LOAD 09 parameters as inputs and furnishes the QoI values. Given an experimental...
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Digital image correlation data and Bayesian inference used together facilitate rigorous quantification of the uncertainty in material input parameters for finite element simulations of superelastic deformation.
Journal Articles
AM&P Technical Articles (2020) 178 (2): 48–62.
Published: 01 February 2020
... contact Ryan Milosh, chief sales and marketing officer at ryan.milosh@asminternational.org. William T. Mahoney, CEO, ASM International bill.mahoney@asminternational.org 52 H I G H L I G H T S IMAT 2020 UPDATE ADVANCED MATERIALS & PROCESSES | FEBRUARY/MARCH 2020 IMAT 2020 Update PSDK and Machine Learning...
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News about ASM members, chapters, events, awards, conferences, affiliates, and other society activities. Topics include: ASM affiliate societies name committee chairs for the 2019-2020 term; ASM President Zi-Kui Liu discusses the ASM response to Materials 4.0 and the Materials Genome Initiative; progress report on the ASM strategic plan; professional profile for Danielle Cote.
Journal Articles
AM&P Technical Articles (2018) 176 (5): 14–17.
Published: 01 July 2018
... all aspects from materials processing to microstructure evolution to predicting materials properties to then linking these properties to design and performance will continue to increase in importance. John Ågren: Machine learning, CALPHAD (including DFT), and process modeling will be combined to make...
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To learn about the latest trends in advanced materials, we turned to four of ASM’s thought leaders: John Ågren, KTH Royal Institute of Technology; George T. (Rusty) Gray III, Los Alamos National Laboratory; Jennie S. Hwang, H-Technologies Group; and David K. Matlock, Colorado School of Mines. This article presents their perspectives on the future of engineered materials, key areas of opportunity, and what ASM can do to support the next generation of materials scientists and engineers.
Journal Articles
AM&P Technical Articles (2020) 178 (6): 51–62.
Published: 01 September 2020
... is needed to run material simulations, aid in machine learning models, and help with Integrated Computational Materials Engineering (ICME) efforts. To respond, we have kicked off the ASM Data Ecosystem Initiative. For more information on this exciting project, see the article on page 58 of this issue. I...
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News about ASM members, chapters, events, awards, conferences, affiliates, and other society activities. Topics include: ASM announces 2020 class of Fellows; Ho Lun Chan, Payam Emadi, and Casey Gilliams are selected as ASM student board members for 2020-2021; John Ågren named 2021 recipient of the J. Willard Gibbs Phase Equilibria Award; Dave Bourell records an introduction to additive manufacturing video presentation; Ray Fryan provides an overview of the ASM Data Ecosystem initiative; professional profile for Elizabeth Perepezko; Andrew Frerichs describes the use of ASM Connect by the Emerging Professionals Commitee; In Memoriam for Edward J. Kubel, Jr., long-time editor of AM&P magazine.
Journal Articles
AM&P Technical Articles (2021) 179 (5): 12–19.
Published: 01 July 2021
... computational materials engineering (ICME) tools are being developed. Modeling and simulations tools, as well as artificial intelligence (e.g., machine learning, neural networks, etc.) are being employed, and new testing methodologies are being adopted. Application of these physics-based and data analytical...
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This article reports on outcomes from an additive manufacturing workshop that developed guidelines for AM data management to realize the promise of Materials 4.0 and achieve process qualification for AM parts.
Journal Articles
AM&P Technical Articles (2020) 178 (1): 22–23.
Published: 01 January 2020
... been redeveloped with machine learning and uncertainty quantification using PyCalphad. This becomes possible due to the large amount of property data predicted by DFT-based first-principles calculations through their high throughput DFT tool kits (DFTTK). Furthermore, DFT-based calculations enabled...
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Meet Zi-Kui Liu, FASM, the new president of ASM, and learn about his academic and scientific background, professional service, and contributions as a researcher and educator.
Journal Articles
AM&P Technical Articles (2021) 179 (8): 51–62.
Published: 01 November 2021
... such as machine learning. The next critical piece in this initiative is educating users on what can be done and how to do it. Like ASM s best-selling Metallurgy for the Non-Metallurgist course, Data Management education will be pragmatic. Students will be taught both the Why and How of going faster...
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News about ASM members, chapters, events, awards, conferences, affiliates, and other society activities. Topics include: ASM affiliate societies announce new officers and board members for 2021; ASM President Judith Todd describes key strategic priorities for the society for the next decade; ASM to launch Data Ecosystem in January 2022; Elvin Beach named editor-in-chief of the Journal of Failure Analysis and Prevention; new ASM technical communities and committees; 2021 highlights from the ASM Materials Education Foundation; professional profile for Deidra Minerd.
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