[en] The Fourth Industrial Revolution, also known as Industry 4.0, has brought about a significant transformation in the industrial world through the implementation of advanced technologies such as automation, artificial intelligence and the interconnection of systems. These innovations are primarily aimed at improving operational efficiency, reducing costs and raising product quality by eliminating repetitive tasks and leveraging real-time data analysis. In this context, the InterQ project, supported by the European Union, has emerged with the mission of revolutionizing measurement, prediction and control methods in industry, ensuring defect-free production through the use of artificial intelligence tools. As part of this ambitious project, the company ITP Aero has requested a feasibility analysis to develop a model for automatic error detection in aircraft casings. Using a camera, multiple snapshots of each corner of the casing were captured using different spectrums. The defects were previously marked and, with this project, the aim is to train a model that can automatically detect new defects. To this end, a development environment was created with a series of programs and scripts specifically designed to process image data. This environment facilitated not only data preparation, but also the training and evaluation of the chosen model: YOLOv5, selected for its simplicity and speed. As the project progressed, experiments were conducted that demonstrated a gradual but significant improvement in the results. This improvement was the result of multiple iterations, where data sets were continuously optimized and model hyperparameters were fine-tuned Despite time and resource constraints, the results obtained supported the feasibility of the project. There was steady improvement throughout the experimentation, suggesting that with more time and resources, even more outstanding results could be obtained. ITP Aero considered the project to be viable, albeit in need of further improvement. .

[eu] Laugarren Industria Iraultzak, 4.0 Industria izenez ere ezagutzen denak, eraldaketa sakona eragin du industriaren munduan, automatizazioa, adimen artifiziala eta sistemen interkonexioa bezalako teknologia aurreratuak ezarriz. Berrikuntza horien helburu nagusia da eraginkortasun operatiboa hobetzea, kostuak murriztea eta produktuen kalitatea handitzea, zeregin errepikakorrak ezabatzean eta datuen analisia denbora errealean aprobetxatzean. Testuinguru horretan, InterQ proiektua sortu da, Europar Batasunaren babesarekin, industrian neurtzeko, iragartzeko eta kontrolatzeko metodoak goitik behera aldatzeko, akatsik gabeko ekoizpena ziurtatuz, adimen artifizialeko tresnak erabiliz. Anbizio handiko proiektu horren barruan, ITP Aero enpresak bideragarritasun-azterketa bat eskatu du, hegazkin-karkasetan akatsak automatikoki detektatzeko eredu bat garatzeko. Kamera baten bidez, karkasaren txoko bakoitzeko hainbat argazki atera ziren, espektro desberdinak erabiliz. Akatsak aldez aurretik markatu ziren eta, proiektu honekin, akats berriak automatikoki detektatu ahal izango dituen modelo bat entrenatu nahi da. Horretarako, garapen-ingurune bat sortu zen, irudien datuak prozesatzeko berariaz diseinatutako programa eta script batzuekin. Ingurune horrek, datuen prestaketa ez ezik, aukeratutako ereduaren entrenamendua eta ebaluazioa ere erraztu zituen; YOLOv5 eredua, bere sinpletasun eta abiaduragatik hautatua. Proiektuak aurrera egin ahala, emaitzetan hobekuntza mailakatua, baina esanguratsua, erakutsi zuten esperimentuak egin ziren. Hobekuntza hori iterazio ugariren emaitza izan zen, non datu-multzoak etengabe optimizatu ziren eta zehaztasunez ereduaren hiperparametroak doitu ziren. Denbora eta baliabideak mugatuak izan arren, lortutako emaitzek proiektuaren bideragarritasuna indartu zuten. Esperimentazioan zehar etengabeko hobekuntza egon zen, eta horrek iradokitzen du denbora eta baliabide gehiagorekin emaitza are nabarmenagoak lor litezkeela. ITP Aero enpresak bideragarritzat jo zuen proiektua, baina hobekuntza gehigarrien beharrarekin izan zituen.