Browsing by Author "Boto, Fernando"
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Item Data Driven Performance Prediction in Steel Making(2022-01-18) Boto, Fernando; Murua, Maialen; Gutierrez, Teresa; Casado, Sara; Carrillo, Ana; Arteaga, Asier; Tecnalia Research & Innovation; FACTORY; CIRMETAL; PROMETALThis work presents three data-driven models based on process data, to estimate different indicators related to process performance in a steel production process. The generated models allow the optimization of the process parameters to achieve optimal performance and quality levels. A new approach based on ensembles has been developed with feature selection methods and four state-of-the-art regression approximations (random forest, gradient boosting, xgboost and neural networks). The results show that the proposed approach makes the prediction more stable reducing the variance for all cases, even in one case, slightly reducing the bias. Furthermore, from the four machine learning paradigms presented, random forest is the one with the best results in a quantitative way, obtaining a coefficient of determination of 0.98 as a maximum, depending on the target sub-process.Item Implementation of signal processing methods in a structural health monitoring (SHM) system based on ultrasonic guided waves for defect detection in different materials and structures(NDT.net, 2016) Galarza, Nekane; Rubio, Benjamín; Diez, Alberto; Boto, Fernando; Gil, Daniel; Rubio, Jokin; Moreno, Eduardo; SMART_MON; Tecnalia Research & Innovation; FACTORY; INDUSTRY_THINGSThe local defect inspection in longitudinal structures such as plates or pipelines implies high economical costs and it is time consuming mainly in underground infrastructures, energy or water, and aerospace sectors. Moreover, if these structures are non-accessible, their local inspection is not possible. Ultrasonic (US) inspection technique based on guided waves is one of the potential alternatives to address this issue. The US inspection based on these type of waves could be applied in many scenarios to monitor the damage state of structures; i.e., in water underground pipelines to identify the wall thickness losses or impact damage detection on Carbon Fiber Reinforced Composites (CFRC). A SHM system based on guided waves requires a special signal processing in order to identify possible damage in the structure. The signal emitted and received is a combination of different propagation modes which are difficult to identify and analyse. However, if the signals are compared to each other (signal related to non-damaged components compared to damaged signal) it is possible to measure their difference as a distance that can be used to estimate the damage level. In this work, signals corresponding to non-damaged samples have been captured and then different types of damage have been applied for different cases. After the data acquisition phase, the comparison between signals has been carried out by applying different mathematical methods and distance metrics (SDC, DTW, Euclidean, Manhattan and Chebyshev), with the aim of detecting defects in different structures and materials. For this purpose, two cases have been analysed: 1) In CFRC plates subjected to impact damage and deformations and 2) In a pipe coated by cement-mortar in order to quantify the wall thickness losses. In both cases ultrasonic PZT sensors, an ultrasonic multichannel pulser/receiver and a software developed ad-hoc have been used. Although the SHM system components were similar, it must be noted that the type of ultrasonic guided waves used were different; in the case of CFRC plates, Lamb waves were excited whereas in the case of the pipeline, Love waves have been used. A comparison between the above mentioned methods is provided. The results show the validity of the approach for damage characterization.Item Metamodels’ Development for High Pressure Die Casting of Aluminum Alloy(2021-10-31) Anglada, Eva; Boto, Fernando; García de Cortazar, Maider; Garmendia, Iñaki; CIRMETAL; Tecnalia Research & Innovation; FACTORYSimulation is a very useful tool in the design of the part and process conditions of high pressure die casting (HPDC), due to the intrinsic complexity of this manufacturing process. Usually, physics-based models solved by finite element or finite volume methods are used, but their main drawback is the long calculation time. In order to apply optimization strategies in the design process or to implement online predictive systems, faster models are required. One solution is the use of surrogate models, also called metamodels or grey-box models. The novelty of the work presented here lies in the development of several metamodels for the HPDC process. These metamodels are based on a gradient boosting regressor technique and derived from a physics-based finite element model. The results show that the developed metamodels are able to predict with high accuracy the secondary dendrite arm spacing (SDAS) of the cast parts and, with good accuracy, the misrun risk and the shrinkage level. Results obtained in the predictions of microporosity and macroporosity, eutectic percentage, and grain density were less accurate. The metamodels were very fast (less than 1 s); therefore, they can be used for optimization activities or be integrated into online prediction systems for the HPDC industry. The case study corresponds to several parts of aluminum cast alloys, used in the automotive industry, manufactured by high-pressure die casting in a multicavity mold.Item Predictive Maintenance on the Machining Process and Machine Tool(2020-01-01) Jimenez-Cortadi, Alberto; Irigoien, Itziar; Boto, Fernando; Sierra, Basilio; Rodriguez, German; Tecnalia Research & Innovation; FACTORY; FABRIC_INTELThis paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces.Item A slag prediction model in an electric arc furnace process for special steel production(2021) Murua, Maialen; Boto, Fernando; Anglada, Eva; Cabero, Jose Mari; Fernandez, Leixuri; FACTORY; Tecnalia Research & Innovation; CIRMETALIn the steel industry, there are some parameters that are difficult to measure online due to technical difficulties. In these scenarios, soft-sensors, which are online tools that aim forecasting of certain variables, play an indispensable role for quality control. In this investigation, different soft sensors are developed to address the problem of predicting the slag quantity and composition in an electric arc furnace process. The results provide evidence that the models perform better for simulated data than for real data. They also reveal higher accuracy in predicting the composition of the slag than the measured quantity of the slag.Item A statistical data-based approach to instability detection and wear prediction in radial turning processes(2018) Jimenez Cortadi, Alberto; Irigoien, Itziar; Boto, Fernando; Sierra, Basilio; Suarez, Alfredo; Galar, Diego; Tecnalia Research & Innovation; FACTORY; FABRIC_INTELRadial turning forces for tool-life improvements are studied, with the emphasis on predictive rather than preventive maintenance. A tool for wear prediction in various experimental settings of instability is proposed through the application of two statistical approaches to process data on tool-wear during turning processes: three sigma edit rule analysis and Principal Component Analysis (PCA). A Linear Mixed Model (LMM) is applied for wear prediction. These statistical approaches to instability detection generate results of acceptable accuracy for delivering expert opinion. They may be used for on-line monitoring to improve the processing of different materials. The LMM predicted significant differences for tool wear when turning different alloys and with different lubrication systems. It also predicted the degree to which the turning process could be extended while conserving stability. Finally, it should be mentioned that tool force in contact with the material was not considered to be an important input variable for the model.