Tuesday, October 5, 2021

IISE Transactions: Special Issue Call-for-papers - AI and Machine Learning for Manufacturing

 

Special Issue: AI and Machine Learning for Manufacturing

IISE Transactions: Focused Issue on Design and Manufacturing 

As a trend of future manufacturing, consumer demand increasingly shifts to personalization, customization, and consumer-maker co-creation. Intertwined with these changing demands , rapid technology advances in new manufacturing technologies, Internet of Things (IoT), robotics, AI and machine learning methods, have significantly expanded, and are continually expanding manufacturing capability, utility, and accessibility. Manufacturing processes and systems have become more connected, intelligent, agile, and collaborative. The special editor team senses that we are right now at a critical juncture of the manufacturing revolution. IISE Transactions Design and Manufacturing Focused Issue is hereby organizing a special issue to capture this moment and capitalize this opportunity. This special issue has two primary objectives: (a) showcase how the AI and machine learning methods have reshaped the landscape of manufacturing in its research and practice; and (b) bring a community of researchers in multiple disciplines to establish new theories, methodologies, and tools to enable smart and intelligent manufacturing by taking full advantage of the recent AI and machine learning innovations.

The topics include, but will not be limited to the following:


·       AI methodologies for manufacturing

·       Cybersecurity for manufacturing

·       Digital twins for manufacturing

·       Fabrication-aware machine learning

·       Human-robot collaborative manufacturing

·       Intelligent manufacturing machines or processes (e.g., smart 3D printers)

·       Intelligent manufacturing systems

·       Intelligent maintenance for manufacturing

·       Machine learning enabled in-process quality improvements methods

·       Machine learning enabled design optimization 

·       Machine learning-based collaborative manufacturing

·       Manufacturing-as-a-Service (MaaS)

·       New anomaly detection algorithms with limited supervision.

·       New advances in cyber-physical manufacturing systems and Industry 4.0

·       Novel manufacturing such as space manufacturing

·       Physical model-guided machine learning for manufacturing

·       Smart monitoring and control of manufacturing

·       Smart sensing and IoT for manufacturing

Papers must be submitted through http://mc.manuscriptcentral.com/iietransactions and prepared according to the journal’s Instructions for authors. Select “Special Issue” for the question “Please select the Focus Issue to which the paper is most related” at Step 1 in the submission process, and select the specific special issue at Step 6. We highly encourage authors to submit abstracts to the lead editor (qiang.huang@usc.edu) in order for the editorial team to provide feedback on the submission and to facilitate a timely review of the full paper.

Important Dates

·       (Encouraged) Abstract Submission: 3/31/2022

·       Manuscript submission: 6/30/2022

·       Completion of 1st round review: 9/30/2022

·       Completion of 2nd round review: 1/31/2023

·       Final manuscript submission: 3/1/2023

·       Tentative publication date: 7/2023


Guest Editors   

Qiang Huang, Professor
University of Southern California
 
Bianca Maria Colosimo, Professor
Politecnico di Milano

John Hart, Professor
Massachusetts Institute of Technology

Conrad Tucker, Professor
Carnegie Mellon University

Lihui Wang, Professor
KTH Royal Institute of Technology

Focus Issue Editor   
Zhenyu (James) Kong, Professor
Virginia Polytechnic Institute and State University


_________________________________________________
Qiang Huang, Ph.D.
Professor
Epstein Department of Industrial and Systems Engineering
Mork Family Department of Chemical Engineering & Materials Science
University of Southern California 
3715 McClintock Ave, GER 216C
Los Angeles, CA 90089-0193
Phone: 213-740-2433(O)



Tuesday, September 7, 2021

Fabrication-aware Machine Learning of Heterogeneous Shape Quality Data Wins 2021 IEEE-CASE Best Conference Paper Award

Ph.D. candidate Yuanxiang Wang and post-doctoral scholar Cesar Ruiz at the USC Daniel J. Epstein Department of Industrial and Systems Engineering received the Best Conference Paper Award in 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE). The presentation video can be found at https://case2021.sciencesconf.org/resource/page/id/37.

Extended Fabrication-Aware Convolution Learning Framework for Predicting 3D Shape Deformation in Additive Manufacturing

 Yuanxiang Wang, Cesar Ruiz, Qiang Huang*

Engineering processes such as 3D printing generate complex shape data in the form of 3D point clouds. Qualification and verification of 3D shapes involves modeling and learning of heterogeneous shape deviation data that are affected by both product geometries and process physics. This study develops an engineering-informed, small-sample machine learning methodology to learn and predict deviations of smooth and non-smooth 3D shapes in a unified modeling framework. The generation of a non-smooth 3D shape is mathematically decomposed into the smooth base shape formation and shape difference realization. Both process physics and shape geometries are captured in the learning framework. It provides a new data analytical tool for shape engineering in additive manufacturing and beyond.