000129376 001__ 129376
000129376 005__ 20241125101146.0
000129376 0247_ $$2doi$$a10.3390/polym15193915
000129376 0248_ $$2sideral$$a135662
000129376 037__ $$aART-2023-135662
000129376 041__ $$aeng
000129376 100__ $$0(orcid)0000-0002-0544-0182$$aFernández, Angel$$uUniversidad de Zaragoza
000129376 245__ $$aPredictive Methodology for Quality Assessment in Injection Molding Comparing Linear Regression and Neural Networks
000129376 260__ $$c2023
000129376 5060_ $$aAccess copy available to the general public$$fUnrestricted
000129376 5203_ $$aThe use of recycled polypropylene in industry to reduce environmental impact is increasing. Design for manufacturing and process simulation is a key stage in the development of plastic parts. Traditionally, a trial-and-error methodology is followed to eliminate uncertainties regarding geometry and process. A new proposal is presented, combining simulation with the design of experiments and creating prediction models for seven different process and part quality output features. These models are used to optimize the design without developing additional time-consuming simulations. The study aims to compare the precision and correlation of these models. The methods used are linear regression and artificial neural network (ANN) fitting. A wide range of eight injection parameters and geometry variations are used as inputs. The predictability of nonlinear behavior and compensatory effects due to the complex relationships between this wide set of parameter combinations is analyzed further in the state of the art. Results show that only Back Propagation Neural Networks (BPNN) are suitable for correlating all quality features in a single formula. The use of prediction models accelerates the optimization of part design, applying multiple criteria to support decision-making. The methodology is applied to the design of a plastic support for induction hobs. Furthermore, this methodology has demonstrated that a weight reduction of 27% is feasible. However, it is necessary to combine process parameters that differ from the standard ones with a non-uniform thickness distribution so that the remaining injection parameters, material properties, and dimensions fall within tolerances.
000129376 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T08-23R
000129376 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000129376 590__ $$a4.7$$b2023
000129376 592__ $$a0.8$$b2023
000129376 591__ $$aPOLYMER SCIENCE$$b19 / 95 = 0.2$$c2023$$dQ1$$eT1
000129376 593__ $$aPolymers and Plastics$$c2023$$dQ1
000129376 593__ $$aChemistry (miscellaneous)$$c2023$$dQ1
000129376 594__ $$a8.0$$b2023
000129376 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000129376 700__ $$0(orcid)0000-0003-4230-7900$$aClavería, Isabel$$uUniversidad de Zaragoza
000129376 700__ $$0(orcid)0000-0002-9277-1309$$aPina, Carmelo$$uUniversidad de Zaragoza
000129376 700__ $$0(orcid)0000-0001-9137-3387$$aElduque, Daniel$$uUniversidad de Zaragoza
000129376 7102_ $$15004$$2545$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Ingeniería Mecánica
000129376 7102_ $$15002$$2305$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Expresión Gráfica en Ing.
000129376 773__ $$g15, 19 (2023), 3915 [20 pp.]$$pPolymers (Basel)$$tPolymers$$x2073-4360
000129376 8564_ $$s3665518$$uhttps://zaguan.unizar.es/record/129376/files/texto_completo.pdf$$yVersión publicada
000129376 8564_ $$s2793826$$uhttps://zaguan.unizar.es/record/129376/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000129376 909CO $$ooai:zaguan.unizar.es:129376$$particulos$$pdriver
000129376 951__ $$a2024-11-22-12:04:29
000129376 980__ $$aARTICLE