Application of supervised machine learning methods in injection molding process for initial parameters setting: prediction of the cooling time parameter-Reference-Cited by-同舟云学术

Application of supervised machine learning methods in injection molding process for initial parameters setting: prediction of the cooling time parameter

Published:2024-04-13 Issue: Volume: Page:
ISSN:2192-6352
Container-title:Progress in Artificial Intelligence
language:en
Short-container-title:Prog Artif Intell

Author:

Tayalati Faouzi^ORCID,Azmani Abdellah^ORCID,Azmani Monir^ORCID

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13748-024-00318-z.pdf

Reference36 articles.

1. Selvaraj, S.K., Raj, A., Rishikesh Mahadevan, R., Chadha, U., Paramasivam, V.: A review on machine learning models in injection molding machines. Adv. Mater. Sci. Eng. (2022). https://doi.org/10.1155/2022/1949061

2. Khan, M., Afaq, S.K., Khan, N.U., Ahmad, S.: Cycle time reduction in injection molding process by selection of robust cooling channel design. ISRN Mech. Eng. 2014, 1–8 (2014). https://doi.org/10.1155/2014/968484

3. Singh, G., Verma, A.: A brief review on injection moulding manufacturing process. Mater. Today Proc. 4(2), 1423–1433 (2017). https://doi.org/10.1016/j.matpr.2017.01.164

4. Prashanth Reddy, K., Panitapu, B.: High thermal conductivity mould insert materials for cooling time reduction in thermoplastic injection moulds. Mater. Today Proc. 4(2), 519–526 (2017). https://doi.org/10.1016/j.matpr.2017.01.052

5. Khosravani, M.R., Nasiri, S.: Injection molding manufacturing process: review of case-based reasoning applications. J. Intell. Manuf. 31(4), 847–864 (2020). https://doi.org/10.1007/s10845-019-01481-0

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1. Correction: Application of supervised machine learning methods in injection molding process for initial parameters setting: prediction of the cooling time parameter;Progress in Artificial Intelligence;2024-05-30

2. Hybrid Approach Integrating Deep Learning-Autoencoder With Statistical Process Control Chart for Anomaly Detection: Case Study in Injection Molding Process;IEEE Access;2024