Browsing by Keyword "monitoring"
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Item Advances in Robotic Welding for Metallic Materials: Application of Inspection, Modeling, Monitoring and Automation Techniques(2023-04) Curiel, David; Veiga, Fernando; Suarez, Alfredo; Villanueva, Pedro; Tecnalia Research & Innovation; FABRIC_INTELThe robotic welding manufacturing of metal parts is a very important process, especially in heavy industries such as shipbuilding, oil and gas, automotive, and aerospace. There is a great variety of different techniques for manufacturing by robotic welding, and the welding operations are always in a constant process of evolution, as any advance can be significant to avoid defects during the welding process. Although a great deal of research work has been carried out in recent years, thanks to which results and reviews have been presented on this subject, the main aim of this publication is to define and review works that show the advances in the main inspection, modeling, monitoring, and automated operations during the welding process to avoid, or predictively identify, any possible defect in order to obtain an optimum degree of quality in the welding.Item Monitoring, modelling and simulation of bifacial PV modules over normal and high albedos(Institute of Electrical and Electronics Engineers Inc., 2020-09-27) Bouchakour, Salim; Caballero, Daniel Valencia; Luna, Alvaro; Medina, Eduardo Roman; Boudjelthia, El Amin Kouadri; Cortes, Pedro Rodriguez; SISTEMAS FOTOVOLTAICOSThis paper explains the modelling and simulation of we modeled and simulate the power output of bifacial PV modules installed over different albedos using monofacial PV performance models (analytical and empirical). The analytical model and empirical models were evaluated using experimental data obtained from the testbed consisting of three bifacial PV arrays mounted in the same configuration but with different rear irradiance conditions: (1) backside covered by white plastic, (2) over normal albedo, and (3) over high albedo. The models use the measured effective solar irradiances and the PV module temperatures to simulate the power output in a healthy condition; they were adapted to bifacial modules by introducing the irradiance bifacial gain in the models' equations. The results of the comparison showed that both models achieved good performance to simulate the instantaneous output of the PV arrays. Daily energy relative errors during sunny days were much less than overcast days, or about 1% for both models, excluding the shading effect. However, compared to the empirical model, the analytical model achieved better performances considering the bifaciality of the PV modules. In addition, the error for the entire monitored period (74 days during March, April, and May 2020) was less than 1.5% for the analytical model, including sunny and cloudy days; such high accuracy in the energy output forecast shows that the analytical model can accurately predict the generation of bifacial PV modules under different skies conditions.Item SLA-Driven Monitoring of Multi-cloud Application Components Using the MUSA Framework(Institute of Electrical and Electronics Engineers Inc., 2016-11-23) Rios, Erkuden; Mallouli, Wissam; Rak, Massimiliano; Casola, Valentina; Ortiz, Antonio M.; CIBERSEC&DLTThe applications that rely on the combined use of multiple independent clouds pose a challenge to the control of their security. The difficulty of this task resides on the lack of insight on the cloud providers security measures, plus the need to simultaneously monitor the behaviour of multipleindividual components deployed in different clouds. This paper presents the SLA-driven monitoring of multi-cloud application security compliance. In this approach, the application security levels, controls and metrics are specified at design time in the Service Level Agreement (SLA) creation process and continuously monitored at runtime once the application components are deployed over the multi-cloud. The security monitoring is based on the Montimage Monitoring Tool (MMT), that combines Deep Packet Inspection (DPI) and data mining techniques to collect and analyse measurements at both network and application component levels for a holistic assurance.Item Towards Zero-Defect Manufacturing Based on Artificial Intelligence through the Correlation of Forces in 5-Axis Milling Process(2024-04) Cascón-Morán, Itxaso; Gómez, Meritxell; Fernández, David; Gil Del Val, Alain; Alberdi, Nerea; González, Haizea; ROBOTICA_FLEXZero-Defect Manufacturing (ZDM) is a promising strategy for reducing errors in industrial processes, aligned with Industry 4.0 and digitalization, aiming to carry out processes correctly the first time. ZDM relies on digital tools, notably Artificial Intelligence (AI), to predict and prevent issues at both product and process levels. This study’s goal is to significantly reduce errors in machining large parts. It utilizes data from process models and in situ monitoring for AI-driven predictions. AI algorithms anticipate part deformation based on manufacturing data. Mechanistic models simulate milling processes, calculating tool deflection from cutting forces and assessing geometric and dimensional errors. Process monitoring provides real-time data to the models during execution. The research focuses on a high-value component from the oil and gas industry, serving as a test piece to predict geometric errors in machining based on the deviation of cutting forces using AI techniques. Specifically, an AISI 1095 steel forged flange, intentionally misaligned to introduce error, undergoes multiple milling operations, including 3-axis roughing and 5-axis finishing, with 3D scans after each stage to monitor progress and deviations. The work concludes that Support Vector Machine algorithms provide accurate results for the estimation of geometric errors from the machining forces.