Interpretation of the effect of transient process data on part quality of injection molding based on explainable artificial intelligence
Author:
Affiliation:
1. Department of Mechanical Engineering, University of Wisconsin–Madison, Madison, WI, USA
2. Wisconsin Institute for Discovery, University of Wisconsin–Madison, Madison, WI, USA
Funder
Ajou University
University of Wisconsin-Madison
Publisher
Informa UK Limited
Subject
Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management
Link
https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2216310
Reference62 articles.
1. In-Mold Sensors for Injection Molding: On the Way to Industry 4.0
2. Industry 4.0 In-Line AI Quality Control of Plastic Injection Molded Parts
3. Explainable artificial intelligence: an analytical review
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1. Explainable artificial intelligence and multi-stage transfer learning for injection molding quality prediction;Journal of Intelligent Manufacturing;2024-05-30
2. Digital Twin Modeling for Smart Injection Molding;Journal of Manufacturing and Materials Processing;2024-05-17
3. Ensemble learning-enabled early prediction of dimensional accuracy for complex products during investment casting;Journal of Manufacturing Processes;2024-03
4. In-mold condition-centered and explainable artificial intelligence-based (IMC-XAI) process optimization for injection molding;Journal of Manufacturing Systems;2024-02
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