scholarly journals Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing

2021 ◽  
Vol 11 (4) ◽  
pp. 1654
Author(s):  
Daiki Ikeuchi ◽  
Alejandro Vargas-Uscategui ◽  
Xiaofeng Wu ◽  
Peter King
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Cold Spray ◽  
Network Model ◽  
Neural Network Model ◽  
Gaussian Function ◽  
Geometric Control ◽  
Network Modelling ◽  
High Production Rate ◽  
Modelling Techniques

Cold spray is emerging as an additive manufacturing technique, particularly advantageous when high production rate and large build sizes are in demand. To further accelerate technology’s industrial maturity, the problem of geometric control must be improved, and a neural network model has emerged to predict additively manufactured geometry. However, limited data on the effect of deposition conditions on geometry growth is often problematic. Therefore, this study presents data-efficient neural network modelling of a single-track profile in cold spray additive manufacturing. Two modelling techniques harnessing prior knowledge or existing model were proposed, and both were found to be effective in achieving the data-efficient development of a neural network model. We also showed that the proposed data-efficient neural network model provided better predictive performance than the previously proposed Gaussian function model and purely data-driven neural network. The results indicate that a neural network model can outperform a widely used mathematical model with data-efficient modelling techniques and be better suited to improving geometric control in cold spray additive manufacturing.

scholarly journals Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Materials ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 2827 ◽  
Author(s):  
Daiki Ikeuchi ◽  
Alejandro Vargas-Uscategui ◽  
Xiaofeng Wu ◽  
Peter C. King
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Cold Spray ◽  
Network Model ◽  
Neural Network Model ◽  
Geometric Control ◽  
Geometric Feature ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
The Neural Network

Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%. We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges. The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.

scholarly journals Energy-based predictions in Lorenz system by a unified formalism and neural network modelling

2010 ◽  
Vol 17 (6) ◽  
pp. 809-815 ◽  
Author(s):  
A. Pasini ◽  
R. Langone ◽  
F. Maimone ◽  
V. Pelino
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Lorenz System ◽  
Lorenz Model ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
Regime Transitions

Abstract. In the framework of a unified formalism for Kolmogorov-Lorenz systems, predictions of times of regime transitions in the classical Lorenz model can be successfully achieved by considering orbits characterised by energy or Casimir maxima. However, little uncertainties in the starting energy usually lead to high uncertainties in the return energy, so precluding the chance of accurate multi-step forecasts. In this paper, the problem of obtaining good forecasts of maximum return energy is faced by means of a neural network model. The results of its application show promising results.

scholarly journals Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model

2017 ◽  
Vol 17 (3) ◽  
pp. 109-123 ◽  
Author(s):  
Olalekan Oshodi ◽  
Obuks Augustine Ejohwomu ◽  
Ibukun Oluwadara Famakin ◽  
Paulo Cortez
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Price Index ◽  
Poor Performance ◽  
Construction Cost ◽  
Multivariate Models ◽  
The Neural Network ◽  
Modelling Techniques ◽  
Cost Forecasting

The poor performance of projects is a recurring event in the construction sector. Information gleaned from literature shows that uncertainty in project cost is one of the significant causes of this problem. Reliable forecast of construction cost is useful in mitigating the adverse effect of its fluctuation, however the availability of data for the development of multivariate models for construction cost forecasting remains a challenge. The study seeks to investigate the reliability of using univariate models for tender price index forecasting. Box-Jenkins and neural network are the modelling techniques applied in this study. The results show that the neural network model outperforms the Box-Jenkins model, in terms of accuracy. In addition, the neural network model provides a reliable forecast of tender price index over a period of 12 quarters ahead. The limitations of using the univariate models are elaborated. The developed neural network model can be used by stakeholders as a tool for predicting the movements in tender price index. In addition, the univariate models developed in the present study are particularly useful in countries where limited data reduces the possibility of applying multivariate models.

Market orientation and performance: modelling a neural network

2009 ◽  
Vol 43 (3/4) ◽  
pp. 421-437 ◽  
Author(s):  
Manuela Silva ◽  
Luiz Moutinho ◽  
Arnaldo Coelho ◽  
Alzira Marques
Keyword(s):  
Neural Network ◽  
Market Orientation ◽  
Network Model ◽  
Neural Network Model ◽  
Performance Modelling ◽  
Network Modelling ◽  
Content Type ◽  
Market Intelligence ◽  
And Performance ◽  
The Impact

PurposeThis paper aims to investigate the impact of market orientation (MO) on performance using a neural network model in order to find new linkages and new explanations for this relationship.Design/methodology/approachThis investigation is based on a survey data collection from a sample of 192 Portuguese companies. A neural network model has been developed to identify the effects of each dimension of MO on each dimension of performance.FindingsRelationship among MO and performance was corroborated but MO's impact is poor and based on its first dimension, market intelligence generation.Research limitations/implicationsFurther research in this field should be conducted using other tools offered by neural network modelling.Practical implicationsManagers should give more attention to cross‐functional co‐ordination in order to improve market intelligence dissemination and responsiveness and, thus, global performance.Originality/valueThe paper presents the development of a neural network model to analyse this relationship.

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