Browsing by Author "Murua, Maialen"
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Item Adaptation of a Branching Algorithm to Solve the Multi-Objective Hamiltonian Cycle Problem(Springer, Cham, 2020) Murua, Maialen; Galar, Diego; Santana, RobertoThe Hamiltonian cycle problem (HCP) consists of finding a cycle of length N in an N-vertices graph. In this investigation, a graph G is considered with an associated set of matrices, in which each cell in the matrix corresponds to the weight of an arc. Thus, a multi-objective variant of the HCP is addressed and a Pareto set of solutions that minimizes the weights of the arcs for each objective is computed. To solve the HCP problem, the Branch-and-Fix algorithm is employed, a specific branching algorithm that uses the embedding of the problem in a particular stochastic process. To address the multi-objective HCP, the Branch-and-Fix algorithm is extended by computing different Hamiltonian cycles and fathoming the branches of the tree at earlier stages. The introduced anytime algorithm can produce a valid solution at any time of the execution, improving the quality of the Pareto Set as time increases.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 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 Tool-Path Problem in Direct Energy Deposition Metal-Additive Manufacturing: Sequence Strategy Generation(IEEE, 2020-05) Murua, Maialen; Suarez, Alfredo; Galar, Diego; Santana, RobertoThe tool-path problem has been extensively studied in manufacturing technologies, as it has a considerable impact on production time. Additive manufacturing is one of these technologies; it takes time to fabricate parts, so the selection of optimal tool-paths is critical. This research analyzes the tool-path problem in the direct energy deposition technology; it introduces the main processes, and analyzes the characteristics of tool-path problem. It explains the approaches applied in the literature to solve the problem; as these are mainly geometric approximations, they are far from optimal. Based on this analysis, this paper introduces a mathematical framework for direct energy deposition and a novel problem called sequence strategy generation. Finally, it solves the problem using a benchmark for several different parts. The results reveal that the approach can be applied to parts with different characteristics, and the solution to the sequence strategy problem can be used to generate tool-paths.Item Wire Arc Additive Manufacturing of Mn4Ni2CrMo Steel: Comparison of Mechanical and Metallographic Properties of PAW and GMAW(Elsevier B.V., 2019) Artaza, Teresa; Suárez, Alfredo; Murua, Maialen; García, J.C.; Tabernero, Iván; Lamikiz, AitzolWire arc additive manufacturing, WAAM, is a popular wire-feed additive manufacturing technology that creates components through the deposition of material layer-by-layer. WAAM has become a promising alternative to conventional machining due to its high deposition rate, environmental friendliness and cost competitiveness. In this research work, a comparison is made between two different WAAM technologies, GMAW (gas metal arc welding) and PAW (plasma arc welding). Comparative between processes is centered in the main variations while manufacturing Mn4Ni2CrMo steel walls concerning geometry and process parameters maintaining the same deposition ratio as well as the mechanical and metallographic properties obtained in the walls with both processes, in which the applied energy is significantly different. This study shows that acceptable mechanical characteristics are obtained in both processes compared to the corresponding forging standard for the tested material, values are 23% higher for UTS and 56% for elongation in vertical direction in the PAW process compared to GMAW (no differences in UTS and elongation results for horizontal direction and in Charpy for both directions) and without significant directional effects of the additive manufacturing technology used.