Maquinagem não convencional sobre placas compósitas

A utilização de materiais compósitos tem crescido nas últimas décadas em virtude do seu baixo peso, elevada resistência e rigidez. A furação de placas compósitas é um desafio que se coloca às indústrias que trabalham com este tipo de materiais, sendo frequente o recurso a ferramentas convencionais, normalmente utilizadas na construção mecânica, com adaptações. Neste trabalho, apresenta-se uma técnica não convencional baseada na furação por jacto de areia sendo avaliadas as descontinuidades causadas em placas compósitas. Durante este processo de furação, as camadas da placa não resistem o tempo necessário para concluir a perfuração, aquecendo e fragmentando- se, e a areia, com o impacto, perde rapidamente o poder abrasivo. Além disso, o tempo necessário para completar com sucesso a operação seria superior a 5 horas, uma vez que a primeira etapa de jateamento durou aproximadamente uma hora, sendo necessário no fim da mesma repor um novo isolamento devido desgaste sofrido. Nesse momento da operação o progresso obtido era imperceptível. O trabalho experimental realizado permite concluir que a furação por jacto de areia, utilizando um compressor com um caudal de 1700 l/min e jateadora de sucção, não se mostra uma ferramenta promissora para executar operações de furação em placas compósitas

Evaluation of delamination damages on composite plates using techniques of image processing and analysis and a backpropagation artificial neural network

Nowadays, drilling of carbon/epoxy laminates is extremely frequent in manufacturing and assembling processes and is normally carried through using standard drills, like twist or Brad drills. However, it is always necessary to have in mind the need to adapt properly the drilling operations and/or the drilling tools used as the risk of delamination occurrence in the laminates involved, or other kind of damages, is very high. Moreover, delamination can be critical because the mechanical properties of the produced parts can be severely affected. Thus, the production of higher quality holes, with damage minimization, is a key challenge to everyone related with composites industry to develop adequate methodologies to delamination characterization and assessment. In this paper, the delamination caused on laminates by drilling machining operations is analytically evaluated by processing and analyzing enhanced conventional radiography images of the laminates involved. In resume, in order to evaluate the delamination damage in laminates plates caused by drilling operations, the radiography images acquired are processed using a computational methodology that uses techniques of image processing and analysis and a backpropagation artificial neural network. Experimental results show that the proposed methodology can be successfully used to measure and characterize the delaminated area. Hence, using our methodology, the damage evaluation on laminates can become more accurate, efficient and simple

Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

[EN] Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) Grant funded by the Korea Government (MSIT) (2019-0-00136, Development of AI-Convergence Technologies for Smart City Industry Productivity Innovation); The work of Javier Del Ser was supported by the Basque Government through the EMAITEK and ELKARTEK Programs, as well as by the Department of Education of this institution (Consolidated Research Group MATHMODE, IT1294-19); VHCA received support from the Brazilian National Council for Research and Development (CNPq, Grant #304315/2017-6 and #430274/2018-1).Muhammad, K.; Ullah, A.; Lloret, J.; Del Ser, J.; De Albuquerque, VHC. (2021). Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions. IEEE Transactions on Intelligent Transportation Systems. 22(7):4316-4336. https://doi.org/10.1109/TITS.2020.30322274316433622

A comprehensive survey of multi-view video summarization

[EN] There has been an exponential growth in the amount of visual data on a daily basis acquired from single or multi-view surveillance camera networks. This massive amount of data requires efficient mechanisms such as video summarization to ensure that only significant data are reported and the redundancy is reduced. Multi-view video summarization (MVS) is a less redundant and more concise way of providing information from the video content of all the cameras in the form of either keyframes or video segments. This paper presents an overview of the existing strategies proposed for MVS, including their advantages and drawbacks. Our survey covers the genericsteps in MVS, such as the pre-processing of video data, feature extraction, and post-processing followed by summary generation. We also describe the datasets that are available for the evaluation of MVS. Finally, we examine the major current issues related to MVS and put forward the recommendations for future research(1). (C) 2020 Elsevier Ltd. All rights reserved.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2B5B01070067)Hussain, T.; Muhammad, K.; Ding, W.; Lloret, J.; Baik, SW.; De Albuquerque, VHC. (2021). A comprehensive survey of multi-view video summarization. Pattern Recognition. 109:1-15. https://doi.org/10.1016/j.patcog.2020.10756711510

Industrial Cyber-Physical Systems-based Cloud IoT Edge for Federated Heterogeneous Distillation.

Deep convoloutional networks have achieved remarkable performance in a wide range of vision-based tasks in modern internet of things (IoT). Due to privacy issue and transmission cost, mannually annotated data for training the deep learning models are usually stored in different sites with fog and edge devices of various computing capacity. It has been proved that knowledge distillation technique can effectively compress well trained neural networks into light-weight models suitable to particular devices. However, different fog and edge devices may perform different sub-tasks, and simplely performing model compression on powerful cloud servers failed to make use of the private data sotred at different sites. To overcome these obstacles, we propose an novel knowledge distillation method for object recognition in real-world IoT sencarios. Our method enables flexible bidirectional online training of heterogeneous models distributed datasets with a new ``brain storming'' mechanism and optimizable temperature parameters. In our comparison experiments, this heterogeneous brain storming method were compared to multiple state-of-the-art single-model compression methods, as well as the newest heterogeneous and homogeneous multi-teacher knowledge distillation methods. Our methods outperformed the state of the arts in both conventional and heterogeneous tasks. Further analysis of the ablation expxeriment results shows that introducing the trainable temperature parameters into the conventional knowledge distillation loss can effectively ease the learning process of student networks in different methods. To the best of our knowledge, this is the IoT-oriented method that allows asynchronous bidirectional heterogeneous knowledge distillation in deep networks

Damage evaluation of drilled carbon/epoxy laminates based on area assessment methods

The characteristics of carbon fibre reinforced laminates had widened their use, from aerospace to domestic appliances. A common characteristic is the need of drilling for assembly purposes. It is known that a drilling process that reduces the drill thrust force can decrease the risk of delamination. In this work, delamination assessment methods based on radiographic data are compared and correlated with mechanical test results (bearing test)

Avaliação da delaminação após furação em compósitos laminados

As características excepcionais dos materiais compósitos de matriz polimérica têm conduzido à sua crescente utilização em todos os domínios. A necessidade de ligar estas peças entre si obriga à realização frequente de operações de furação que, embora devidamente adaptada, pode provocar diferentes tipos de dano nas peças a ligar. Desses, o dano mais grave é a delaminação pelas suas consequências na perda das propriedades mecânicas. Neste trabalho é apresentado um estudo comparativo da extensão do dano em função da geometria de ferramenta utilizada. Para tal as placas foram radiografadas após furação e a imagem resultante segmentada para medição das áreas associadas. Os resultados mostram a importância de uma adequada geometria de ferramenta na redução do dano causado pela furação em compósitos laminados