Browsing by Keyword "Machine Learning"
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Item Compounding process optimization for recycled materials using machine learning algorithms(2022) Lopez-Garcia, Pedro; Barrenetxea, Xabier; García-Arrieta, Sonia; Sedano, Iñigo; Palenzuela, Luis; Usatorre, Luis; Tecnalia Research & Innovation; POLIMEROS; FACTORY; COMPOSITEThe sustainable manufacturing of goods is one of the factors to minimize natural resource depletion and CO2 emissions. In the last decade a big effort has been done to transition from linear economy to circular economy. This transition requires to implement re-manufacturing processes into the current industrial manufacturing framework, replacing the sourcing of raw materials by re-manufacturing technologies. However, this transition is very challenging since it requires the transformation of the companies and more specially their processes, from traditional to circular. To speed up this transformation, the use of tools provided by the 4th industrial revolution are crucial. In particular, the use of artificial intelligence techniques enables the optimization of the re-manufacturing processes and make those optimizations available to all the stakeholders. This paper presents an optimization system for re-manufacturing of recycled fiber through compounding processes with materials that come from composite waste or end of life of products. The proposed approach has been trained with the data collected from several experiments carried out with a compounding machine under different specifications, fiber reinforcement grades, and output material properties. The system will allow to set up a compounding machine for different types of reinforced plastics needless of setting point experiments. The algorithms have been tested with previously unseen scenarios and they have proved to be efficient for giving the optimal material characteristics.Item Feature extraction-based prediction of tool wear of Inconel 718 in face turning(2018-08) Murua, M.; Suárez, A.; López de Lacalle, L. N.; Santana, R.; Wretland, A.; FACTORY; FABRIC_INTELTool wear is a recurring topic in the cutting field, so obtaining knowledge about the tool wear process and the capability of predicting tool wear is of special importance. Cutting processes can be optimised with predictive models that are able to forecast tool wear with a suitable level of accuracy. This research focuses on the application of some regression approaches, based on machine learning techniques, to a face-turning process for Inconel 718. To begin with, feature extraction of the cutting forces is considered, to generate regression models. Subsequently, the regression models are improved with a reduced set of features obtained by computing the feature importance. The results provide evidence that the gradient-boosting regressor allows an increment in the wear prediction accuracy and the random forest regressor has the capability of detecting relevant features that characterise the turning process. They also reveal higher accuracy in predicting tool wear under high-pressure cooling as opposed to conventional lubrication.Item MACHINE LEARNING-BASED ANALYSIS ENGINE TO IDENTIFY CRITICAL VARIABLES IN MULTI-STAGE PROCESSES: APPLICATION TO THE INSTALLATION OF BLIND FASTENERS(2020-09) MURUA, MAIALEN; VEIGA SUAREZ, FERNANDO; ORTEGA LALMOLDA, JUAN ANTONIO; Penalva Oscoz, Mariluz; DIEZ OLIVAN, ALBERTO; Tecnalia Research & InnovationQuality control in manufacturing is a recurrent topic as the ultimate goals are to produce high quality products with less cost. Mostly, the problems related to manufacturing processes are addressed focusing on the process itself putting aside other operations that belong to the part’s history. This research work presents a Machine Learning-based analysis engine for non-expert users which identifies relationships among variables throughout the manufacturing line. The developed tool was used to analyze the installation of blind fasteners in aeronautical structures, with the aim of identifying critical variables for the quality of the installed fastener, throughout the fastening and drilling stages. The results provide evidence that drilling stage affects to the fastening, especially to the formed head’s diameter. Also, the most critical phase in fastening, which is when the plastic deformation occurs, was identified. The results also revealed that the chosen process parameters, thickness of the plate and the faster type influence on the quality of the installed fastener.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.