Model Selection and Evaluation for Machine Learning: Deep Learning in Materials Processing-Reference-Cited by-同舟云学术

Model Selection and Evaluation for Machine Learning: Deep Learning in Materials Processing

Published:2020-09 Issue:3 Volume:9 Page:287-300
ISSN:2193-9764
Container-title:Integrating Materials and Manufacturing Innovation
language:en
Short-container-title:Integr Mater Manuf Innov

Author:

Kopper Adam^ORCID,Karkare Rasika,Paffenroth Randy C.,Apelian Diran

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,General Materials Science

Link

https://link.springer.com/content/pdf/10.1007/s40192-020-00185-1.pdf

Reference56 articles.

1. Dietrich D, Heller B, Yang B (2015) Data science and big data analytics: discovering, analyzing, visualizing and presenting data, 1st edn. Wiley, Hoboken

2. Capgemini Consulting Group, Industry_4.0_-The_Capgemini_Consulting_V.pdf. Capgemini, 2014. https://www.capgemini.com/consulting/wp-content/uploads/sites/30/2017/07/capgemini-consulting-industrie-4.0_0_0.pdf

3. Folk J (2019) U.S. Aluminum Casting Industry—2019, Cast. Eng., no. July 2019, pp 16–19, Jun. 2019

4. Spada A (2020) Revitalization of North American Metalcasting, 2012, Accessed: May 24, 2020. https://www.diecasting.org/docs/statistics/North_America.pdf

5. Rai JK, Lajimi AM, Xirouchakis P (2008) An intelligent system for predicting HPDC process variables in interactive environment. J Mater Process Technol 203(1–3):72–79. https://doi.org/10.1016/j.jmatprotec.2007.10.011

Cited by 24 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Recurrent neural networks as virtual cavity pressure and temperature sensors in high-pressure die casting;The International Journal of Advanced Manufacturing Technology;2024-08-29

2. Mold breakout prediction based on computer vision and machine learning;Journal of Iron and Steel Research International;2024-04-30

3. Machine learning-supported manufacturing: a review and directions for future research;Production & Manufacturing Research;2024-03-15

4. Automatic Counting of Planting Microsites via Local Visual Detection and Global Count Estimation;IEEE Transactions on Emerging Topics in Computational Intelligence;2023-12

5. A Smote-Based Churn Prediction System Using Machine Learning Techniques;2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0 (IDICAIEI);2023-11-27