[en] The use of lightweight materials has demonstrated to be very energy efficient in the transport sector, reducing the consumption of fossil fuels and increasing the range of electric cars. Carbon fibre reinforced polymers (CFRP) have proven to be effective for structural lightweighting as they combine low density with good mechanical properties, especially in terms of specific strength and stiffness. Resin Transfer Moulding (RTM) is an effective process in the manufacture of high performance, geometrically complex structural parts with low process costs. However, the robustness of the RTM process remains a challenge, as both material and process uncertainties negatively affect the impregnation quality, generating void defects and dry zones. To address these defects, a paradigm shift has to take place, as the production system itself has to become an "expert" in material science and process technology. The manufacturing system must consider the defects generated in impregnation stage to recognise the risks in the final structural performance. In other words, a Digital Twin (DT) of the physical process must be constructed and accurately recreate the RTM process. For this purpose, the continuous Process-Structure-Property-Performance (PSPP) modelling approach has proven to be the key to the generation of DTs. Therefore, the objective of this thesis is to generate the scientific-technological knowledge for Digital Twins of the RTM process following the PSPP approach. Firstly, an experimental characterisation of the material based on the PSPP approach has been carried out to quantify the effect of process parameters on void generation (Process-Structure), and subsequently, void effect on impact properties (Structure-Property). For this purpose, NCF-Epoxy biaxial laminates (0/90) with different void contents (0.58%, 1.35%, 2.44% and 4.34%) have been tested by the drop-weight impact test method. Samples with high void content (4.34%) recorded a 25.9% reduction in peak force, and a 9.57% reduction in dissipated energy compared to samples with low void content (0.58%). In addition, the penetration and perforation limits were lower in the high void content specimens, 20% and 30% respectively. Secondly, a Machine Learning (ML) based impregnation quality diagnosis model has been developed. This model has been fed with information from process, synthetic data generated by FEM simulations, and has been able to predict the generated impregnation structure (Process-Structure). The diagnosis has been performed by means of a Supervised Learning binary classification model trained with synthetic dataset. Among the different predictive models studied, Extreme Gradient Boosting and Light Gradient Boosting Machine were the most accurate models for predicting RTM filling quality, with an accuracy of 84.9% and 83.4%, respectively. In addition, a scaling of the ML model has allowed not only to predict the part quality, but also to locate the zones where the defect was generated. Comparing the computational time of the ML model with the FEM model, a reduction in computational time from 360 s to 1 s was observed. Thus, supervised learning predictive models can be used to diagnose the quality of the manufactured part using RTM. They are fast enough to be integrated into the process and feed the digital twin of the process. Finally, a surrogate model based on ML-FEM has been developed, which, knowing the location and position of the impregnation defect, is able to predict the reduction of mechanical properties (Structure-Properties), and then the impact performance of the parts (Properties-Performance). The surrogate model has allowed predictions for (i) quality classification based on the maximum displacement, (ii) numerical prediction (regression) of the maximum displacement, and (iii) multi-output regression to obtain the impact curves (F(t), δ(t), and E(t)). The results obtained have shown an accuracy of 95.85% in the classification and a R2 of 0.86 in the regression. Once the surrogated model is trained, it is able to make predictions in less than 5 seconds, unlike the FEM model that needs about 60 minutes. In addition, the multi-output regression has been shown to be able to replicate accurately force, displacement and energy curves. Combining both the impregnation quality diagnostic model (Process-Structure) and the structural performance predictive model (Structure-Properties-Performance), it has been possible to generate a DT based on the PSPP approach. Both models are fast enough to be integrated into the RTM process and allow online structural validation considering the process defects.

[eu] Material arinen erabilera energetikoki oso eraginkorra dela frogatu da garraioaren sektorean, erregai fosilen kontsumoa murrizteko eta auto elektrikoen autonomia luzatzeko aukera ematen baitu. Karbono-zuntzez indartutako polimeroak (CFRP) eraginkorrak dira egitura arintzeko, dentsitate baxua eta propietate mekaniko onak konbinatzen baitituzte, batez ere erresistentzia eta zurruntasun espezifikoa. Resin Transfer Moulding (RTM) prozesua eraginkorra da prestazio handiko, geometrikoki konplexuak eta kostu baxuko piezak fabrikatzeko. Hala ere, RTM prozesuaren fidagarritasunak erronka bat izaten jarraitzen du, materialaren eta prozesuaren ziurgabetasunek negatiboki eragiten baitute inpregnazio kalitatean, batez ere porositate akatsak eta eremu lehorrak sortzen. Akats horiei aurre egiteko, paradigma aldaketa bat behar da, fabrikazio-sistema bera "aditu" bilakatu behar baita, materialen zientzian eta prozesu teknologian. Fabrikazio-sistemak kontuan hartu behar ditu inpregnazioan sortutako akatsak, azken egituraren errendimendu mekanikoa aurreikusteko. Hau da, prozesu fisikoaren Biki Digitala (DT) eraiki behar da, eta RTM prozesua zehaztasunez digitalki sortu. Horretarako, prozesuaren modelizazio jarraia, Prozesua-Egitura-Propietateak-Portaera (PSPP) metodologian oinarritua, funtsezkoa da. Tesi honen helburua, RTM prozesuaren biki digitalen inguruan ezagutza sortzen da, PSPP ikuspegian oinarrituz. Lehenik eta behin, PSPP ikuspegian oinarritutako azterketa esperimental bat egin da. Azterketa horretan, prozesu parametroek porositatea sortzeko duten eragina kuantifikatu da (Prozesua-Egitura), eta, ondoren, porositateak talka-propietateetan duen eragina (Egitura-Propietateak). Horretarako, NCF-Epoxy biaxialen (0/90) saiakuntzak egin dira porositate-eduki desberdinekin (% 0,58; % 1,35; % 2,44 eta % 4,34), dardo bidezko talka metodoaren bidez. Porositate handiko laginek (% 4,34) % 25,9 murriztu dute indar maximoa, eta % 9,57, berriz, disipazio energia, porosidade baxuko laginen aldean (%0,58). Gainera, penetrazio eta zulatze mugak txikiagoak izan dira porositate handiko laginetan, % 20 eta % 30, hurrenez hurren. Bigarrenik, Machine Learning-en (ML) oinarritutako inpregnazio-kalitatearen diagnostiko modelo bat garatu da. Modelo hau prozesuko datuekin elikatu da, FEM simulazioen bidez sortutako datu sintetikoak, eta prozesu ostean sortutako egitura aurreikusten du (Prozesua-Egitura). Gainbegiratutako Ikaskuntza (Supervised Learning) modelo baten bidez egin da diagnostikoa, datu sintetiko multzo batekin entrenatuta. Aztertutako modelo prediktiboen artean, Extreme Gradient Boosting eta Light Gradient Boosting Machine izan dira RTMaren betetze-kalitatea aurresateko modelorik zehatzenak, % 84,9 eta % 83,4eko zehaztasunarekin. Gainera, ML modeloaren eskalatze bati esker, piezaren kalitatea aurreikusteaz gain, akatsa sortu den eremuak aurkitu ahal izan dira. ML modeloaren kalkulu-denbora FEM modeloarekin alderatuz gero, denbora konputazionalean 360 s-tik 1 s-ra murriztu dela ikusi da. Beraz, gainbegiratutako ikaskuntzaren modelo prediktiboak RTM bidez fabrikatutako piezaren kalitatea diagnostikatzeko erabil daitezke. Bestalde, prozesuan integratzeko eta prozesuaren biki digitala elikatzeko bezain azkarrak dira. Azkenik, ML eta FEM-en oinarritutako modelu subrogatu bat garatu da. Modelo honek, inpregnazio defektuaren kokapena eta porosidade maila ezagututa, propietate mekanikoen murrizketa aurreikus dezake (Egitura-Propietateak), eta, ondoren, piezen talka portaera aurreikus dezake (Propietateak-Portaera). Modelo subrogatua hiru zatitan banatua izan da: (i) gehienezko desplazamenduan oinarritutako kalitate sailkapena, (ii) desplazamendu maximoaren balioaren aurreikuspena (erregresioa) eta (iii) talka-kurbak lortzeko multi-irteera erregresiboa (F(t), δ(t), eta E(t)). Lortutako emaitzek erakutsi dutenez, % 95,85-eko zehaztasuna lortu da sailkapenean, eta % 0,86-koa erregresioko R2 balioan. Bestalde, modelo subrogatua entrenatu ondoren, 5 segundo baino gutxiagoan egin ditzake iragarpenak; FEM modeloak, berriz, 60 minutu behar ditu. Gainera, multiirteera erregresioak frogatu du gai dela indarraren, desplazamenduaren eta energiaren kurba osoak erreplikatzeko. Biak konbinatuz, inpregnazioaren kalitatearen diagnostiko-modeloa (Prozesua-Egitura) eta egiturazko portaeraren modelo prediktiboa (Egitura-Propietateak-Portaera), PSPP ikuspegian oinarritutako DT bat sor daiteke. Bi modeloak RTM prozesuan integratzeko bezain azkarrak dira, eta prozesu-defektuak kontuan hartuta piezen portaera mekanikoa on-line balioztatzeko aukera ematen dute.