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The quality of welds is critical to the safety of structures in construction, so early detection of irregularities is crucial. Advances in machine vision inspection technologies, such as deep learning models, have improved the detection of weld defects. This paper presents a new CNN model based on ResNet50 to classify four types of weld defects in radiographic images: crack, pore, non-penetration, and no defect. Stratified cross-validation, data augmentation, and regularization were used to improve generalization and avoid over-fitting. The model was tested on three datasets, RIAWELC, GDXray, and a private dataset of low image quality, obtaining an accuracy of 98.75 %, 90.255 %, and 75.83 %, respectively. The model proposed in this paper achieves high accuracies on different datasets and constitutes a valuable tool to improve the efficiency and effectiveness of quality control processes in the welding industry. Moreover, experimental tests show that the proposed approach performs well on even low-resolution images.

This paper researches the influence of zinc coating of galvanized DP600 steel (DP600G) on the nugget development and mechanical properties of dissimilar DP600 - AISI304 welded joints obtained by resistance spot welding process (RSW). The RSW evaluations consisted of determining, from the dynamic resistance curves, the different stages involved in nugget formation. The experimental results showed that the zinc coating on the DP600G steel has a significant influence on the time needed for the start of the nugget formation, two times higher than that used for non-galvanized steel. The expulsion time was delayed by 1.12 times. The presence of Zn in the DP600G/AISI304 welded joints produced smaller nugget diameters compared to the DP600/AISI304 joints for the same experimental conditions, however higher peak failure load values were obtained for the first. The causes of this unusual behavior are also analyzed in this paper.

This work presents an assessment of the use of Information and Communication Technologies (ICT) in the teaching-learning process of the Mechatronics in the Bachelor of Mechanical Engineering. Initially, concepts related to ICT are showed from the perspective of different researchers and their principal impacts on educational systems. Based on the analyzes the incidence of ICTs in the current teaching-learning process is analyzed and how these constitute a challenge for superior education to achieve a preparation of students based on technology-based learning. In addition, an analysis of the Mechanical Engineering course is carried out, with the objective of demonstrating the contribution of the use of ICT in the implementation of a distance learning course model within the subject, providing innovative solutions to the traditional processes of teaching and learning.

This research proposes a new index to evaluate the stability of the melting process, in three-phase electric arc furnaces (EAFs), based on the acoustic signals generated during the different stages of the casting. The proposed stability index is obtained by characterizing the time and frequency domain of the acoustic signals. During EAF monitoring, acoustic signals were acquired using a microphone coupled to an NI USB-9234 acquisition system. To validate the results, the voltage and current signals were measured with the aid of a Circutor AR6 power analyzer for three-phase electrical networks. The results showed that the acoustic signal energy in the frequency range of 1 to 12 kHz can be used as an indicator of the process stability in the EAF. Finally, the validity of the proposed stability index is evaluated from the process characterization using the harmonic distortion analysis methods and the dynamic U-I characteristics of the arc voltage and current signals. The results obtained demonstrated the effectiveness of the proposal and constitute a starting point for advances in the automatic control of the process in the EAF, from the acoustic signals.

Se diseñó y produjo una aleación de hierro fundido gris (Fe-2,6%wtC 1,5%wtSi) específica para procesamiento semisólido, la cual fue sometida a ensayos de tixoconformación a 1 160 ºC y 1 180 ºC, con tiempos de permanencia de 0, 30, 60 y 90 s, con el fin de analizar los cambios microestructurales provocados a la aleación por el tixoconformado. Se encontró que para los tiempos de permanencia utilizados el comportamiento de las fuerzas requeridas para el proceso, en especial para 1 180 ºC, no fue significativo. El procesamiento semisólido cambia el tipo de grafito “A” a tipo “B” (o “E”), pero no afecta el espacio interdendrítico entre láminas de grafito. El incremento de la fracción líquida disminuye las fuerzas requeridas por el proceso de tixoconformación.

En este trabajo se estudia la influencia de la temperatura y del tiempo de permanencia en la microestructura, dureza y porosidad del material tixoconformado. Se diseñó usando el software Thermocal una aleación de hierro fundido hipoeutéctico para obtener 40 y 50% de fase sólida en estado semisólido.La aleación fue producida de forma convencional en moldes de arena. Las muestras fueron calentadas al estado semisólido a 1160 y 1180oC, y mantenidas a estas temperaturas por 0, 30, 90 y 120s. Después fueron comprimidas en una prensa excéntrica equipada con placas paralelas. Los ensayos revelaron queel procesamiento semisólido cambia en el material el tipo de grafito de “A” a “B” (o “E”), pero no afecta el espacio interdendrítico entre láminas de grafito. La dureza del material tixoconformado aumenta con respecto a la materia prima, mientras la porosidad tiende a disminuir al aumentar la temperatura y eltiempo de permanencia.Palabras claves: tixoconformación, materiales semisólidos, aleaciones ferrosas, evolución de la morfología, microestructura.______________________________________________________________________________AbstractIn this paper is study the influence of temperature and held times in the microstructure evolution, hardness and porosity of the thixoformed material. An alloy of hypoeutectic cast iron was design using the software Thermocal to obtain 40 50% of solid phase in semisolid state. The alloy was produce by conventionalcasting in sand molds. The samples were heated to the semi-solid state at 1160 and 1180oC and held at these temperatures for 0, 30, 90 and 120s. The tests were carried out in eccentric press equipped whit two parallel platen. Tests reveal that the thixoforming operations change the graphite type from “A” to “B” (or“E”), but not affect the interdendritic arm spacing between graphite lamellae. The hardness of the thixoformed material showed an increase compared to designed alloy. The porosity analysis reveal a tendency to decrease whit the increase of temperature and holding time related with the original raw material.Key words: thixoforming, semisolid materials, ferrous alloys, morphology evolution, microstructure.

This paper analyses the correlation between the acoustic emission signals and the main parameters of friction stir welding process based on artificial neural networks (ANNs). The acoustic emission signals in Z and Y directions have been acquired by the AE instrument NI USB-9234. Statistical and temporal parameters of discomposed acoustic emission signals using Wavelet Transform have been used as input of the ANN. The outputs of the ANN model include the parameters of tool rotation speed and travel speed, and tool profile, as well as the tensile strength. A multilayer feed-forward neural network has been selected and trained, using Levenberg-Marquardt algorithm for different network architectures. Finally, an analysis of the comparison between the measured and the calculated data is presented. The model obtained can be used to model and develop an automatic control of the parameters of the process and mechanical properties of joint, based on the acoustic emission signals.

The tribological behavior of a new composite material containing graphene nanosheets (GNS) is presented. The composite material was obtained by Friction Stir Processing, using as metallic matrix the AA1050-H24 alloy. Different tool rotation and advancing speeds were tested in friction stir processing (FSP). The worn surfaces of obtained materials were analyzed by Scanning Electron Microscopy (SEM). Raman spectroscopy demonstrated that graphene reinforcements are successfully mixed into the aluminum matrix. The results proved the feasibility of using GNSs to obtain nanocomposites by FSP. The coefficient of friction of the aluminum alloy was 0.57, decreasing to 0.38 for the nanocomposite GNSs/AA1050. These values decrease for samples obtained at lower tool rotation speeds. The weight losses of the composites are less than that of unreinforced AA1050-H24 alloy for conditions with lower advancing speeds (40 and 60 mm/min) and 1120 rpm.