Prediction of Powder Injection Molding Process Parameters Using Artificial Neural Networks

The parameters of Powder Injection Molding (PIM) process were modeled by artificial neural networks (ANNs). The feed-forward multilayer perceptron was utilized and trained by back-propagation algorithm. Particle size, particle morphology, debinding time, and sintering temperature were taken into account and regarded as inputs of the ANN model. The outputs included relative density, wax loss, shrinkage, and hardness. The results obtained using the ANN model were in good agreement with the experimental data. In fact, they displayed an average R-value of 0.95 versus the experimental values. The optimum architecture of ANN was 7-4-1, in which the network was trained with Levenberg–Marquardt training algorithm. Thus, the ANN model can be used to evaluate, calculate, and forecast PIM process parameters.

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Prediction of Sound Insulation of Sandwich Partition Panels by Means of Artificial Neural Networks

Archives of Acoustics ◽

10.1515/aoa-2017-0068 ◽

2017 ◽

Vol 42 (4) ◽

pp. 643-651

Author(s):

Naveen Garg ◽

Siddharth Dhruw ◽

Laghu Gandhi

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Optimization Technique ◽

Back Propagation ◽

National Research Council ◽

Sound Insulation ◽

Ann Model ◽

Back Propagation Algorithm ◽

Design And Optimization ◽

Artificial Neural

Abstract The paper presents the application of Artificial Neural Networks (ANN) in predicting sound insulation through multi-layered sandwich gypsum partition panels. The objective of the work is to develop an Artificial Neural Network (ANN) model to estimate the Rw and STC value of sandwich gypsum constructions. The experimental results reported by National Research Council, Canada for Gypsum board walls (Halliwell et al., 1998) were utilized to develop the model. A multilayer feed-forward approach comprising of 13 input parameters was developed for predicting the Rw and STC value of sandwich gypsum constructions. The Levenberg-Marquardt optimization technique has been used to update the weights in back-propagation algorithm. The presented approach could be very useful for design and optimization of acoustic performance of new sandwich partition panels providing higher sound insulation. The developed ANN model shows a prediction error of ±3 dB or points with a confidence level higher than 95%.

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Influence of Powder Geometry on Powder Injection Molding Process Parameters

Materials Science Forum ◽

10.4028/www.scientific.net/msf.416-418.347 ◽

2003 ◽

Vol 416-418 ◽

pp. 347-353

Author(s):

Luis Mauricio Resende ◽

Alvaro Toubes Prata ◽

Aloísio Nelmo Klein ◽

L.H.S. Almeida

Keyword(s):

Injection Molding ◽

Process Parameters ◽

Powder Injection Molding ◽

Powder Injection ◽

Injection Molding Process ◽

Molding Process

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A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

Energies ◽

10.3390/en15020511 ◽

2022 ◽

Vol 15 (2) ◽

pp. 511

Author(s):

Adeniyi Kehinde Onaolapo ◽

Rudiren Pillay Carpanen ◽

David George Dorrell ◽

Evans Eshiemogie Ojo

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Back Propagation ◽

Real Life ◽

Weather Conditions ◽

Ann Model ◽

Back Propagation Algorithm ◽

Weather Factors ◽

Weather Events ◽

Artificial Neural

The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) and multiple linear regression (MLR) models. The results obtained from the ANN model are found to be satisfactory when compared to those obtained from MLR and ES. The results demonstrate that artificial neural networks are robust and can be used to predict electricity outages with regards to faults caused by severe weather conditions.

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Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

Processes ◽

10.3390/pr9061070 ◽

2021 ◽

Vol 9 (6) ◽

pp. 1070

Author(s):

Abdul Gani Abdul Jameel

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Functional Groups ◽

Average Error ◽

Ann Model ◽

Oh Groups ◽

Unseen Data ◽

Learning Capabilities ◽

Artificial Neural ◽

Oxygenated Fuels

The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were dis-assembled into eight constituent functional groups, namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy.

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Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽

10.3390/en14082332 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2332

Author(s):

Cecilia Martinez-Castillo ◽

Gonzalo Astray ◽

Juan Carlos Mejuto

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Random Forests ◽

Prediction Models ◽

Meteorological Station ◽

Ann Model ◽

Global Irradiation ◽

Artificial Neural ◽

Input Variables ◽

Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

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Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

Buildings ◽

10.3390/buildings11020044 ◽

2021 ◽

Vol 11 (2) ◽

pp. 44

Author(s):

Fernando A. N. Silva ◽

João M. P. Q. Delgado ◽

Rosely S. Cavalcanti ◽

António C. Azevedo ◽

Ana S. Guimarães ◽

...

Keyword(s):

Neural Networks ◽

Compressive Strength ◽

Artificial Neural Networks ◽

Cross Sections ◽

Influential Factors ◽

Average Error ◽

Ann Model ◽

Cement Ratio ◽

Water Cement Ratio ◽

Artificial Neural

The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

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Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

Water ◽

10.3390/w13050705 ◽

2021 ◽

Vol 13 (5) ◽

pp. 705

Author(s):

Josué Trejo-Alonso ◽

Carlos Fuentes ◽

Carlos Chávez ◽

Antonio Quevedo ◽

Alfonso Gutierrez-Lopez ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Hydraulic Conductivity ◽

Input Data ◽

Logistic Function ◽

Saturated Hydraulic Conductivity ◽

Irrigation District ◽

Pedotransfer Functions ◽

Back Propagation Algorithm ◽

Artificial Neural

In the present work, we construct several artificial neural networks (varying the input data) to calculate the saturated hydraulic conductivity (KS) using a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. All of them were constructed using two hidden layers, a back-propagation algorithm for the learning process, and a logistic function as a nonlinear transfer function. In order to explore different arrays for neurons into hidden layers, we performed the bootstrap technique for each neural network and selected the one with the least Root Mean Square Error (RMSE) value. We also compared these results with pedotransfer functions and another neural networks from the literature. The results show that our artificial neural networks obtained from 0.0459 to 0.0413 in the RMSE measurement, and 0.9725 to 0.9780 for R2, which are in good agreement with other works. We also found that reducing the amount of the input data offered us better results.

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Transfer learning with artificial neural networks between injection molding processes and different polymer materials

Journal of Manufacturing Processes ◽

10.1016/j.jmapro.2021.11.014 ◽

2022 ◽

Vol 73 ◽

pp. 395-408

Author(s):

Yannik Lockner ◽

Christian Hopmann ◽

Weibo Zhao

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Injection Molding ◽

Transfer Learning ◽

Polymer Materials ◽

Artificial Neural

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Rainfall Prediction by Artificial Neural Networks Trained using Different Climate Variables

10.21203/rs.3.rs-569733/v1 ◽

2021 ◽

Author(s):

Mateus Alexandre da Silva ◽

Marina Neves Merlo ◽

Michael Silveira Thebaldi ◽

Danton Diego Ferreira ◽

Felipe Schwerz ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Input Data ◽

Back Propagation ◽

Metropolitan Region ◽

Climate Variables ◽

Back Propagation Algorithm ◽

Rainfall Prediction ◽

Artificial Neural ◽

Belo Horizonte

Abstract Predicting rainfall can prevent and mitigate damages caused by its deficit or excess, besides providing necessary tools for adequate planning for the use of water. This research aimed to predict the monthly rainfall, one month in advance, in four municipalities in the metropolitan region of Belo Horizonte, using artificial neural networks (ANN) trained with different climate variables, and to indicate the suitability of such variables as inputs to these models. The models were developed through the MATLAB® software version R2011a, using the NNTOOL toolbox. The ANN’s were trained by the multilayer perceptron architecture and the Feedforward and Back propagation algorithm, using two combinations of input data were used, with 2 and 6 variables, and one combination of input data with 3 of the 6 variables most correlated to observed rainfall from 1970 to 1999, to predict the rainfall from 2000 to 2009. The most correlated variables to the rainfall of the following month are the sequential number corresponding to the month, total rainfall and average compensated temperature, and the best performance was obtained with these variables. Furthermore, it was concluded that the performance of the models was satisfactory; however, they presented limitations for predicting months with high rainfall.

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