The interpretive model of manufacturing: a theoretical framework and research agenda for machine learning in manufacturing-Reference-Cited by-同舟云学术

The interpretive model of manufacturing: a theoretical framework and research agenda for machine learning in manufacturing

Published:2021-06-01 Issue:16 Volume:59 Page:4960-4994
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Sharma Ajit¹^ORCID,Zhang Zhibo²^ORCID,Rai Rahul³^ORCID

Affiliation:

1. Mike Illitch School of Business, Wayne State University, Detroit, MI, USA

2. Manufacturing and Design Lab (MADLab), University at Buffalo, Buffalo, NY, USA

3. Geometric Reasoning and Artificial Intelligence Lab (GRAIL), Clemson University, Greenville, SC, USA

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1930234

Reference171 articles.

1. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

2. A review of machining monitoring systems based on artificial intelligence process models

3. Surface roughness prediction based on cutting parameters and tool vibrations in turning operations

4. Acemoglu, Daron, and David Autor. 2010. Skills, Tasks and Technologies: Implications for Employment and Earnings. in Orley Ashenfelter and David Card, eds., Handbook of Labor Economics, Volume 4, Amsterdam: Elsevier-North Holland, 2010, 1043–1171.

5. Robots and Jobs: Evidence from US Labor Markets

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