Machining parameters affects the final quality of components made in carbon fiber reinforced plastic (CFRP) composite materials. In this framework, the work here presented aims at studying the right combination of cutting speed (v_c) and feed rate (v_f), for dry drilling of carbon fiber reinforced plastic composite materials, which obtained better results regarding roughness, hole cylindricity, and diameter. A series of experimental tests were carried out under different drilling conditions (v_c/v_f), monitoring the thrust force (Fz), torque (T), and electric power (EP), to define which one can help more for industrial daily life production. Results validation was carried out using the analysis of variance, in order to relate main machining parameters cutting speed and linear feed, with thrust force, drilling torque, main spindle electric power and hole quality parameters (average roughness, cylindricity and diameter). The conclusions show that thrust force is not proportional to the cutting speed and the best combinations of cutting speed and feed were found out around the average values of tested parameters. Spindle electric power is an interesting element to take into account because it is easy to consider in real production.

This work presents an automated welding inspection system based on a neural network trained through a series of 2D images of welding seams obtained in the same study. The object detection method follows a geometric deep learning model based on convolutional neural networks. Following an extensive review of available solutions, algorithms, and networks based on this convolutional strategy, it was determined that the You Only Look Once algorithm in its version 8 (YOLOv8) would be the most suitable for object detection due to its performance and features. Consequently, several models have been trained to enable the system to predict specific characteristics of weld beads. Firstly, the welding strategy used to manufacture the weld bead was predicted, distinguishing between two of them (Flux-Cored Arc Welding (FCAW)/Gas Metal Arc Welding (GMAW)), two of the predominant welding processes used in many industries, including shipbuilding, automotive, and aeronautics. In a subsequent experiment, the distinction between a well-manufactured weld bead and a defective one was predicted. In a final experiment, it was possible to predict whether a weld seam was well-manufactured or not, distinguishing between three possible welding defects. The study demonstrated high performance in three experiments, achieving top results in both binary classification (in the first two experiments) and multiclass classification (in the third experiment). The average prediction success rate exceeded 97% in all three experiments.

Machining processes of Non Metal Matrix Composites (NMMC) differ significantly from those carried out on metallic materials due to the constitutive characteristics of composites. In particular, drilling processes of these materials are shown as critical operations, because of the dissimilarity between the physicochemical properties of the different materials that compose them. The final quality of the drilled holes can be evaluated on the basis of the micro and macrogeometrical deviations. This paper reports on the results of a study of microgeometrical deviations of drilled holes of Carbon Fiber Reinforced Plastic (CFRP) as a function of the cutting parameters.

This work presents an automated weld inspection system based on a one-stage Neural Network trained through a series of 2D weld seam images obtained in the same study. Our method uses You Only Look Once in version 8 (YOLOv8) for object detection. Several models have been trained so that the system can predict: the type of Flux-Cored Arc Welding (FCAW)/Gas Metal Arc Welding (GMAW) weld, two of the common welding processes that have been used in most industries: shipbuilding, automotive, aeronautics among others; detect if a weld is well manufactured or defective; and a third experiment where four classes are attempted to be detected: a correctly manufactured weld, if it presents a lack of penetration defect, an undercut defect or other manufacturing problems. The presented system does not stop at determining the correct welding parameters. The study is based on finding a robust and reliable system to support this difficult and critical task of weld inspection. High performance was achieved in all three experiments carried out in this study, both in those that established a binary classification (the first two) and in the one that established a multiclass classification (the third experiment). In all of them, an average prediction success rate of over 97\% was achieved.