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Subject: search.filters.subject.Artificial neural networks

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    Part distortion optimization of aluminum-based aircraft structures using finite element modeling and artificial neural networks
    (CIRP Journal of Manufacturing Science and Technology, 2020)
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    Elias Ledesma-orozco
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    Sergio Ledesma
    Currently, in the aircraft design, thinner structures are required to reduce weight, which in turn presents challenges for the manufacturing of parts and components. One of the identified problems in manufacturing is the machining distortion phenomenon, which causes the generation of scrap during the production of mechanical and structural components. This study presents the use of a finite element procedure, artificial neural network models, and the simulated annealing algorithm to optimize machining distortion phenomena in aluminum-based structures. A finite element procedure that simulates machining distortion by considering residual stresses and machining locations is used to generate training and validation data sets for the construction of an artificial neural network model. Once the performance of the artificial neural network is validated, simulated annealing is used in combination with the neural network model to find the optimum parameters of the machining locations and the residual stresses conditions that reduce distortion phenomena caused by machining. A case study of a specimen that has complex geometrical features, such as those that present in the design of aircraft structures, was used for the validation of the models. The results show that the proposed approach predicts the machining distortion of the specimen obtaining errors below 3% regarding experimental observations. Numerical results not only predict maximum distortions, but the evidence shows that the finite element can estimate the distribution of the distortion presented experimentally in the case study. Additionally, the optimization results helped to reduce the distortions 80% or more for high levels of deformation. Therefore, the proposed method in this study helps in the prediction and optimization of machining distortion of aluminum-based structures.
    Scopus© Citations 15  25
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    Credit Risk Models in the Mexican Context Using Machine Learning
    (2022)
    López, Ana Lilia
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    López, Estefanía
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    The Default Rate is related to the period of the economic cycle in which they are observed, during expansion periods of the economy the default rate tends to be lower. But in contraction periods, the default rate tends to increase and this could be a risk for the stability of a country’s economy. Therefore, it is important to monitor the perspective of the economy in case it is expected to decrease or have abrupt movements. This work aims to identify the economic variables that determine the default rate of the Mexican Financial System and to find a machine learning model that forecasts the default rate. For this, we aggregate a dataset based on three official Mexican sources that compile data from 2013 to 2022, including the COVID-19 pandemic time frame. Then, we propose the analysis using two machine learning models. After the analysis, the results confirm that the artificial neural networks model shows better predictive power for the default rate values. We also implement an easy to use web application to estimate the default rate based on three simple variables. We anticipate this work might help on estimating the default rate and might impact on the strategic policies in the Mexican economy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
      19  2
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    Click-event sound detection in automotive industry using machine/deep learning
    (2021)
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    Gutiérrez, Sebastián
    In the automotive industry, despite the robotic systems on the production lines, factories continue employing workers in several custom tasks getting for semi-automatic assembly operations. Specifically, the assembly of electrical harnesses of engines comprises a set of connections between electrical components. Despite the task is easy to perform, employees tend not to notice that a few components are not being connected properly due to physical fatigue provoked by repetitive tasks. This yields a low quality of the assembly production line and possible hazards. In this work, we propose a sound detection system based on machine/deep learning (ML/DL) approaches to identify click sounds produced when electrical harnesses are connected. The purpose of this system is to count the number of connections properly made and to feedback to the employees. We collect and release a public dataset of 25,000 click sounds of 25 ms length at 22 kHz during three months of assembly operations in an automotive production line located in Mexico. Then, we design an ML/DL-based methodology for click sound detection of assembled harnesses under real conditions of a noisy environment (noise level ranging from −16.67 dB to −12.87 dB) including other machinery sounds. Our best ML/DL model (i.e., a combination between five acoustic features and an optimized convolutional neural network) is able to detect click sounds in a real assembly production line with an accuracy of 94.55±0.83 %. To the best of our knowledge, this is the first time a click sounds detection system in assembling electrical harnesses of engines for giving feedback to the workers is proposed and implemented in a real-world automotive production line. We consider this work valuable for the automotive industry on how to apply ML/DL approaches for improving the quality of semi-automatic assembly operations. © 2021 Elsevier B.V.
    Scopus© Citations 22  24  2
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    Demand prediction using a soft-computing approach : a case study of automotive industry
    (2020)
    Salais-Fierro, Tomás Eloy
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    Saucedo-Martínez, Jania
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    Vela-Haro, Jose Manuel
    According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data. © 2019 by the authors.
    Scopus© Citations 13  14  2