2026-04-17T03:47:49Zhttps://recercat.cat/oai/request
oai:recercat.cat:2117/4455902025-11-08T13:01:11Zcom_2072_1033col_2072_452951
RECERCAT author Jonart, Matéo 2025-11-08T13:01:11Z 2025-11-08T13:01:11Z 2025-07-10 http://hdl.handle.net/2117/445590 The growing demand for lightweight and efficient aerospace structures has motivated the exploration of Artificial Intelligence (AI) in structural analysis workflows. This thesis investigates the potential of image-based Machine Learning (ML) techniques by manually implementing a simplified, fixed-filter convolutional architecture (pseudo-CNN) in MATLAB, intentionally avoiding high-level ML libraries to gain a deeper understanding of fundamental concepts. The main objective is to compare the performance of a fully-connected neural network (FC-NN) with that of a pseudo-CNN that applies handcrafted filters before classification. Both models are tested on image classification tasks using the MNIST digit dataset and a set of colored animal images. The results aim to highlight the benefits of spatial feature extraction—even without trainable convolution filters—over raw-pixel-based input processing. While this project does not implement a fully trainable CNN, nor does it directly tackle structural optimization tasks, a discussion is provided on how such pre-processing strategies may be conceptually adapted to assist in aerospace applications such as defect detection or structural inspection. The project therefore serves as a pedagogical and exploratory foundation for future work at the intersection of AI and aerospace engineering. Open Access Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Structural analysis (Engineering) Machine learning Artificial intelligence--Engineering applications Neural networks (Computer science) Estructures, Teoria de les Aprenentatge automàtic Intel·ligència artificial--Aplicacions a l'enginyeria Xarxes neuronals (Informàtica) Study of convolutional networks for image classification with applications in aerospace engineering Bachelor thesis