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date accessioned2024-12-24T19:11:17Z
date available2024-12-24T19:11:17Z
date copyright6/28/2024 12:00:00 AM
date issued2024
identifier issn1087-1357
identifier othermanu_146_8_080301.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303453
description abstractYuebin GuoYuebin Guo This Special Issue serves as a bridge between the ASME Journal of Manufacturing Science and Engineering (JMSE) and the global community of artificial intelligence manufacturing researchers. The primary objective of the Special Issue is to collect high-level scientific articles in the emerging area of physics-informed machine learning (PIML) for advanced manufacturing and push the boundaries of knowledge. Contributions are sought in recent advances, challenges, and future directions of PIML model development at the levels of processes, machines, and systems.A team of Guest Editors has been setup to collect as diverse selection articles as possible. The team is led by Professor Yuebin Guo (Rutgers University-New Brunswick, Piscataway, NJ) and consists of Professor Yusuf Altintas (The University of British Columbia, Vancouver, BC, Canada), Professor Qing Chang (University of Virginia, Charlottesville, VA), Professor Robert Gao (Case Western Reserve University, Cleveland, OH), Professor Weihong Grace Guo (Rutgers University-New Brunswick, Piscataway, NJ), Dr. Andy Henderson (Hendtech LLC, Greenville, SC), Dr. Jaydeep Karandikar (Oak Ridge National Laboratory, Oak Ridge, TN), and Professor Tony Schmitz (University of Tennessee, Knoxville, TN).
publisherThe American Society of Mechanical Engineers (ASME)
titleSpecial Issue: Physics-Informed Machine Learning for Advanced Manufacturing
typeJournal Paper
journal volume146
journal issue8
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4065694
journal fristpage80301-1
journal lastpage80301-1
page1
treeJournal of Manufacturing Science and Engineering:;2024:;volume( 146 ):;issue: 008
contenttypeFulltext


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