Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production Efficiency
This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/22/10279 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850217264192684032 |
|---|---|
| author | Jose Isidro Hernández-Vega Luis Alejandro Reynoso-Guajardo Mario Carlos Gallardo-Morales María Ernestina Macias-Arias Amadeo Hernández Nain de la Cruz Jesús E. Soto-Soto Carlos Hernández-Santos |
| author_facet | Jose Isidro Hernández-Vega Luis Alejandro Reynoso-Guajardo Mario Carlos Gallardo-Morales María Ernestina Macias-Arias Amadeo Hernández Nain de la Cruz Jesús E. Soto-Soto Carlos Hernández-Santos |
| author_sort | Jose Isidro Hernández-Vega |
| collection | DOAJ |
| description | This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at the beginning of a shift change. These data were subjected to exploratory analysis to identify correlations between important variables, such as injection time, cycle time, and mold pressures. Additionally, classification models, including Random Forest and Logistic Regression, were constructed to predict and classify the process state based on these variables. The model results demonstrated high predictive performance, with 99.5% accuracy for Random Forest and 97% for Logistic Regression. These results provide a strong foundation for the early identification of potential problems and informed decision making to improve the efficiency of the plastic injection molding process. This study contributes to the advancement of the integration of intelligent technologies in industrial process optimization, aligned with the principles of Industry 4.0. |
| format | Article |
| id | doaj-art-dafe3c6aeff746528e76943cb407014c |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-dafe3c6aeff746528e76943cb407014c2025-08-20T02:08:07ZengMDPI AGApplied Sciences2076-34172024-11-0114221027910.3390/app142210279Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production EfficiencyJose Isidro Hernández-Vega0Luis Alejandro Reynoso-Guajardo1Mario Carlos Gallardo-Morales2María Ernestina Macias-Arias3Amadeo Hernández4Nain de la Cruz5Jesús E. Soto-Soto6Carlos Hernández-Santos7División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/IT de Nuevo León, Mexico, Guadalupe 67170, Nuevo Leon, MexicoDivisión de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/IT de Nuevo León, Mexico, Guadalupe 67170, Nuevo Leon, MexicoDivisión de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/IT de Nuevo León, Mexico, Guadalupe 67170, Nuevo Leon, MexicoDivisión de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/IT de Nuevo León, Mexico, Guadalupe 67170, Nuevo Leon, MexicoTecnológico Nacional de México/IT de Pachuca, Mexico, Blvd. Felipe Ángeles Km. 84.5, Venta Prieta, Pachuca de Soto 42083, Hidalgo, MexicoTecnológico Nacional de México/IT de Nuevo León, Mexico, Av. Eloy Cavazos 2001, Guadalupe 66170, Nuevo Leon, MexicoDivisión de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/IT de Nuevo León, Mexico, Guadalupe 67170, Nuevo Leon, MexicoTecnológico Nacional de México/IT de Nuevo León, Mexico, Av. Eloy Cavazos 2001, Guadalupe 66170, Nuevo Leon, MexicoThis paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at the beginning of a shift change. These data were subjected to exploratory analysis to identify correlations between important variables, such as injection time, cycle time, and mold pressures. Additionally, classification models, including Random Forest and Logistic Regression, were constructed to predict and classify the process state based on these variables. The model results demonstrated high predictive performance, with 99.5% accuracy for Random Forest and 97% for Logistic Regression. These results provide a strong foundation for the early identification of potential problems and informed decision making to improve the efficiency of the plastic injection molding process. This study contributes to the advancement of the integration of intelligent technologies in industrial process optimization, aligned with the principles of Industry 4.0.https://www.mdpi.com/2076-3417/14/22/10279plastic injections moldingindustrial processesmachine learningclassification modelrandom forestlogistic regression |
| spellingShingle | Jose Isidro Hernández-Vega Luis Alejandro Reynoso-Guajardo Mario Carlos Gallardo-Morales María Ernestina Macias-Arias Amadeo Hernández Nain de la Cruz Jesús E. Soto-Soto Carlos Hernández-Santos Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production Efficiency Applied Sciences plastic injections molding industrial processes machine learning classification model random forest logistic regression |
| title | Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production Efficiency |
| title_full | Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production Efficiency |
| title_fullStr | Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production Efficiency |
| title_full_unstemmed | Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production Efficiency |
| title_short | Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production Efficiency |
| title_sort | plastic injection molding process analysis data integration and modeling for improved production efficiency |
| topic | plastic injections molding industrial processes machine learning classification model random forest logistic regression |
| url | https://www.mdpi.com/2076-3417/14/22/10279 |
| work_keys_str_mv | AT joseisidrohernandezvega plasticinjectionmoldingprocessanalysisdataintegrationandmodelingforimprovedproductionefficiency AT luisalejandroreynosoguajardo plasticinjectionmoldingprocessanalysisdataintegrationandmodelingforimprovedproductionefficiency AT mariocarlosgallardomorales plasticinjectionmoldingprocessanalysisdataintegrationandmodelingforimprovedproductionefficiency AT mariaernestinamaciasarias plasticinjectionmoldingprocessanalysisdataintegrationandmodelingforimprovedproductionefficiency AT amadeohernandez plasticinjectionmoldingprocessanalysisdataintegrationandmodelingforimprovedproductionefficiency AT naindelacruz plasticinjectionmoldingprocessanalysisdataintegrationandmodelingforimprovedproductionefficiency AT jesusesotosoto plasticinjectionmoldingprocessanalysisdataintegrationandmodelingforimprovedproductionefficiency AT carloshernandezsantos plasticinjectionmoldingprocessanalysisdataintegrationandmodelingforimprovedproductionefficiency |