Double Ensemble Technique for Improving the Weight Defect Prediction of Injection Molding in Smart Factories-Reference-Cited by-同舟云学术

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Double Ensemble Technique for Improving the Weight Defect Prediction of Injection Molding in Smart Factories

Published:2023 Issue: Volume:11 Page:113605-113622
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Koo Kwangmo¹^ORCID,Choi Keunho²^ORCID,Yoo Donghee³^ORCID

Affiliation:

1. Department of Management of Technology, Gyeongsang National University, Jinju, South Korea

2. Department of Business Administration, Hanbat National University, Daejeon, South Korea

3. Department of Management Information Systems (Business and Economics Research Institute), Gyeongsang National University, Jinju, South Korea

Funder

Ministry of Education of the Republic of Korea and the National Research Foundation of Korea

‘Program to train professionals in spreading convergence technology commercialization’ of the Ministry of Trade, Industry and Energy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10283848.pdf?arnumber=10283848

Reference26 articles.

1. A Machine Learning Method for Predicting Part Weight, Dimensions, and Residual Stress during Injection Molding

2. Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method

3. Quality Prediction for Injection Molding by Using a Multilayer Perceptron Neural Network

4. Machine Learning Methods for Quality Prediction in Thermoplastics Injection Molding

5. A Framework of a Smart Injection Molding System Based on Real-time Data

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