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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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...