Predictive ANN Modeling and Optimization of Injection Molding Parameters to Minimize Warpage in Polypropylene Rectangular Parts
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| Publicado en: | Journal of Manufacturing and Materials Processing vol. 9, no. 7 (2025), p. 236-252 |
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| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3233227516 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-4494 | ||
| 024 | 7 | |a 10.3390/jmmp9070236 |2 doi | |
| 035 | |a 3233227516 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Gámez, Juan Luis |u Departamento de Ingeniería Gráfica, Universidad de Alicante, 03690 Sant Vicent del Raspeig, Spain; jl.gamez@ua.es | |
| 245 | 1 | |a Predictive ANN Modeling and Optimization of Injection Molding Parameters to Minimize Warpage in Polypropylene Rectangular Parts | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Injection molding is a fundamental process for transforming plastics into various industrial components. Among the critical aspects studied in this process, volumetric contraction and warpage of plastic parts are of particular importance. Achieving precise control over warpage is crucial for ensuring the production of high-quality components. This research explores optimizing injection process parameters to minimize volumetric contraction and warpage in rectangular polypropylene (PP) parts. The study employs experimental analysis, MoldFlow simulation, and Artificial Neural Network (ANN) modeling. MoldFlow simulation software provides valuable data on warpage, serving as input for the ANN model. Based on the Backpropagation Neural Network algorithm, the optimized ANN model accurately predicts warpage by considering factors such as part thickness, flow path distance, and flow path tangent. The study highlights the importance of accurately setting injection parameters to achieve optimal warpage results. The BPNN-based approach offers a faster and more efficient alternative to computer-aided engineering (CAE) processes for studying warpage. | |
| 653 | |a Simulation | ||
| 653 | |a Software | ||
| 653 | |a Warpage | ||
| 653 | |a Injection molding | ||
| 653 | |a Molding parameters | ||
| 653 | |a Modelling | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Neural networks | ||
| 653 | |a Back propagation networks | ||
| 653 | |a Variables | ||
| 653 | |a Computer aided engineering--CAE | ||
| 653 | |a Polypropylene | ||
| 653 | |a Geometry | ||
| 653 | |a Computer simulation | ||
| 653 | |a Process parameters | ||
| 700 | 1 | |a Jordá-Vilaplana Amparo |u Departamento de Ingeniería Gráfica, Universitat Politècnica de València, 03801 Alcoy, Spain; amjorvi@upvnet.upv.es | |
| 700 | 1 | |a Peydro Miguel Angel |u Instituto Universitario de Investigación de Tecnología de Materiales, Universitat Politècnica de València, 03801 Alcoy, Spain; mpeydro@upv.es | |
| 700 | 1 | |a Selles, Miguel Angel |u Instituto Universitario de Investigación de Tecnología de Materiales, Universitat Politècnica de València, 03801 Alcoy, Spain; mpeydro@upv.es | |
| 700 | 1 | |a Sanchez-Caballero, Samuel |u Instituto de Diseño para la Fabricación y Producción Automatizada, Universitat Politècnica de València, 03801 Alcoy, Spain; sasanca@dimm.upv.es | |
| 773 | 0 | |t Journal of Manufacturing and Materials Processing |g vol. 9, no. 7 (2025), p. 236-252 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233227516/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233227516/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233227516/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |