Process variables in mixture experimental design applied to wood plastic composites
The inclusion of process variables in mixture experimental design is crucial for optimizing final products with precision. Unlike standard response surface designs, which are limited by the requirement that proportions must sum to 100%, mixture-process experiments enable a thorough evaluation of how...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitat Politècnica de València
2025-01-01
|
| Series: | Journal of Applied Research in Technology & Engineering |
| Subjects: | |
| Online Access: | https://polipapers.upv.es/index.php/JARTE/article/view/22171 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832592391218921472 |
|---|---|
| author | Javier Cruz-Salgado Sergio Alonso-Romero Edgar Augusto Ruelas-Santoyo José Alfredo Jiménez-García Israel Miguel-Andrés Roxana Zaricell Bautista-López |
| author_facet | Javier Cruz-Salgado Sergio Alonso-Romero Edgar Augusto Ruelas-Santoyo José Alfredo Jiménez-García Israel Miguel-Andrés Roxana Zaricell Bautista-López |
| author_sort | Javier Cruz-Salgado |
| collection | DOAJ |
| description | The inclusion of process variables in mixture experimental design is crucial for optimizing final products with precision. Unlike standard response surface designs, which are limited by the requirement that proportions must sum to 100%, mixture-process experiments enable a thorough evaluation of how operational factors, such as particle size and mixing time, interact with mixture components. This approach enhances the understanding of how processing conditions affect product properties and leads to more accurate predictive models, thereby improving production consistency and reliability. Regression analysis reveals that interactions between PET and both particle size and mixing time significantly impact the response variable. The model demonstrates strong predictive accuracy, with R-squared and adjusted R-squared values of 92% and 86%, respectively, and a low root mean square error (S) of 0.2818. The PRESS value of 3.38 confirms the model’s ability to accurately predict new data. The absence of high multicollinearity, as indicated by variance inflation factor (VIF) values below 5, further supports the model's stability and interpretability. Contour plots illustrate the effect of varying mixture proportions on the response, such as tensile strength, showing a positive impact of both particle size and mixing time. The highest tensile strength is achieved at maximum levels of these variables, indicating a synergistic effect. Response variable optimization identifies the optimal mixture composition as 90% PET, 10% wood, and no coupling agent. To maximize tensile strength, the largest particle size and longest mixing time should be used, though extrapolating beyond the studied parameters should be done with caution. |
| format | Article |
| id | doaj-art-eb468d81fe13469286b855ecafc43bd8 |
| institution | Kabale University |
| issn | 2695-8821 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | Journal of Applied Research in Technology & Engineering |
| spelling | doaj-art-eb468d81fe13469286b855ecafc43bd82025-01-21T08:46:46ZengUniversitat Politècnica de ValènciaJournal of Applied Research in Technology & Engineering2695-88212025-01-0161122310.4995/jarte.2025.2217121361Process variables in mixture experimental design applied to wood plastic compositesJavier Cruz-Salgado0https://orcid.org/0000-0002-1684-6647Sergio Alonso-Romero1https://orcid.org/0000-0001-6469-0408Edgar Augusto Ruelas-Santoyo2https://orcid.org/0000-0003-0515-7667José Alfredo Jiménez-García3https://orcid.org/0000-0002-5293-4855Israel Miguel-Andrés4https://orcid.org/0000-0002-9433-7864Roxana Zaricell Bautista-López5https://orcid.org/0000-0002-3180-8825Universidad de las Américas Puebla Centro de Innovación Aplicada en Tecnologías Competitivas Technological Institute of Celaya Technological Institute of Celaya Centro de Innovación Aplicada en Tecnologías Competitivas Centro de Innovación Aplicada en Tecnologías Competitivas The inclusion of process variables in mixture experimental design is crucial for optimizing final products with precision. Unlike standard response surface designs, which are limited by the requirement that proportions must sum to 100%, mixture-process experiments enable a thorough evaluation of how operational factors, such as particle size and mixing time, interact with mixture components. This approach enhances the understanding of how processing conditions affect product properties and leads to more accurate predictive models, thereby improving production consistency and reliability. Regression analysis reveals that interactions between PET and both particle size and mixing time significantly impact the response variable. The model demonstrates strong predictive accuracy, with R-squared and adjusted R-squared values of 92% and 86%, respectively, and a low root mean square error (S) of 0.2818. The PRESS value of 3.38 confirms the model’s ability to accurately predict new data. The absence of high multicollinearity, as indicated by variance inflation factor (VIF) values below 5, further supports the model's stability and interpretability. Contour plots illustrate the effect of varying mixture proportions on the response, such as tensile strength, showing a positive impact of both particle size and mixing time. The highest tensile strength is achieved at maximum levels of these variables, indicating a synergistic effect. Response variable optimization identifies the optimal mixture composition as 90% PET, 10% wood, and no coupling agent. To maximize tensile strength, the largest particle size and longest mixing time should be used, though extrapolating beyond the studied parameters should be done with caution.https://polipapers.upv.es/index.php/JARTE/article/view/22171wood plastic compositepetpolyethylene terephthalateprocess variablesdesign of experiments for mixtures |
| spellingShingle | Javier Cruz-Salgado Sergio Alonso-Romero Edgar Augusto Ruelas-Santoyo José Alfredo Jiménez-García Israel Miguel-Andrés Roxana Zaricell Bautista-López Process variables in mixture experimental design applied to wood plastic composites Journal of Applied Research in Technology & Engineering wood plastic composite pet polyethylene terephthalate process variables design of experiments for mixtures |
| title | Process variables in mixture experimental design applied to wood plastic composites |
| title_full | Process variables in mixture experimental design applied to wood plastic composites |
| title_fullStr | Process variables in mixture experimental design applied to wood plastic composites |
| title_full_unstemmed | Process variables in mixture experimental design applied to wood plastic composites |
| title_short | Process variables in mixture experimental design applied to wood plastic composites |
| title_sort | process variables in mixture experimental design applied to wood plastic composites |
| topic | wood plastic composite pet polyethylene terephthalate process variables design of experiments for mixtures |
| url | https://polipapers.upv.es/index.php/JARTE/article/view/22171 |
| work_keys_str_mv | AT javiercruzsalgado processvariablesinmixtureexperimentaldesignappliedtowoodplasticcomposites AT sergioalonsoromero processvariablesinmixtureexperimentaldesignappliedtowoodplasticcomposites AT edgaraugustoruelassantoyo processvariablesinmixtureexperimentaldesignappliedtowoodplasticcomposites AT josealfredojimenezgarcia processvariablesinmixtureexperimentaldesignappliedtowoodplasticcomposites AT israelmiguelandres processvariablesinmixtureexperimentaldesignappliedtowoodplasticcomposites AT roxanazaricellbautistalopez processvariablesinmixtureexperimentaldesignappliedtowoodplasticcomposites |