scholarly journals Estimating Contact Force Chains Using Artificial Neural Network

2021 ◽  
Vol 11 (14) ◽  
pp. 6278
Author(s):  
Mengmeng Wu ◽  
Jianfeng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Size ◽  
Granular Materials ◽  
Coordination Number ◽  
Contact Force ◽  
Force Chains ◽  
Granular System ◽  
Biaxial Test ◽  
Artificial Neural

The inhomogeneous distribution of contact force chains (CFC) in quasi-statically sheared granular materials dominates their bulk mechanical properties. Although previous micromechanical investigations have gained significant insights into the statistical and spatial distribution of CFC, they still lack the capacity to quantitatively estimate CFC evolution in a sheared granular system. In this paper, an artificial neural network (ANN) based on discrete element method (DEM) simulation data is developed and applied to predict the anisotropy of CFC in an assembly of spherical grains undergoing a biaxial test. Five particle-scale features including particle size, coordination number, x- and y-velocity (i.e., x and y-components of the particle velocity), and spin, which all contain predictive information about the CFC, are used to establish the ANN. The results of the model prediction show that the combined features of particle size and coordination number have a dominating influence on the CFC’s estimation. An excellent model performance manifested in a close match between the rose diagrams of the CFC from the ANN predictions and DEM simulations is obtained with a mean accuracy of about 0.85. This study has shown that machine learning is a promising tool for studying the complex mechanical behaviors of granular materials.

Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network

2014 ◽  
Vol 21 (3) ◽  
pp. 411-420 ◽  
Author(s):  
Temel Varol ◽  
Aykut Canakci ◽  
Sukru Ozsahin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Size ◽  
Milling Time ◽  
Planetary Mill ◽  
Reinforcement Ratio ◽  
Percentage Error ◽  
Composite Powders ◽  
Neural Network Approach ◽  
Artificial Neural

AbstractIn this study, an artificial neural network approach was employed to predict the effect of B4C size, B4C content, and milling time on the particle size and particle hardness of Al2024-B4C composite powders. Al2024-B4C powder mixtures with various reinforcement weight percentages (5%, 10%, and 20% B4C), reinforcement size (49 and 5 μm), and milling times (0–10 h) were prepared by mechanical alloying process. The properties of the composite powders were analyzed using a laser particle size analyzer for the particle size and a microhardness tester for the powder microhardness. The three input parameters in the proposed artificial neural network (ANN) were the reinforcement size, reinforcement ratio, and milling time. Particle size and particle hardness of the composite powders were the outputs obtained from the proposed ANN. The mean absolute percentage error for the predicted values did not exceed 4.289% for the best prediction model. This model can be used for predicting properties of Al2024-B4C composite powders produced with different reinforcement size, reinforcement ratio, and milling times.

scholarly journals Modeling of Sulfite Concentration, Particle Size, and Reaction Time in Lignosulfonate Production from Barley Straw Using Response Surface Methodology and Artificial Neural Network

BioResources ◽  
2016 ◽  
Vol 11 (4) ◽  
Author(s):  
Maria Guadalupe Serna-Diaz ◽  
Ainhoa Arana-Cuenca ◽  
Joselito Medina-Marin ◽  
Juan Carlos Seck-Tuoh-Mora ◽  
Yuridia Mercado-Flores ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Size ◽  
Response Surface Methodology ◽  
Reaction Time ◽  
Response Surface ◽  
Barley Straw ◽  
Artificial Neural

scholarly journals An Artificial Neural Network Based Analysis of Factors Controlling Particle Size in a Virgin Coconut Oil-Based Nanoemulsion System Containing Copper Peptide

PLoS ONE ◽  
2016 ◽  
Vol 11 (7) ◽  
pp. e0157737 ◽  
Author(s):  
Shazwani Samson ◽  
Mahiran Basri ◽  
Hamid Reza Fard Masoumi ◽  
Emilia Abdul Malek ◽  
Roghayeh Abedi Karjiban
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Size ◽  
Coconut Oil ◽  
Virgin Coconut Oil ◽  
Artificial Neural

scholarly journals Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253617
Author(s):  
Romaan Nazir ◽  
Devendra Kumar Pandey ◽  
Babita Pandey ◽  
Vijay Kumar ◽  
Padmanabh Dwivedi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Size ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Dry Weight ◽  
Solvent Ratio ◽  
Dioscorea Deltoidea ◽  
Extraction Parameters ◽  
Artificial Neural

Introduction Dioscorea deltoidea var. deltoidea (Dioscoreaceae) is a valuable endangered plant of great medicinal and economic importance due to the presence of the bioactive compound diosgenin. In the present study, response surface methodology (RSM) and artificial neural network (ANN) modelling have been implemented to evaluate the diosgenin content from D. deltoidea. In addition, different extraction parameters have been also optimized and developed. Materials and methods Firstly, Plackett-Burman design (PBD) was applied for screening the significant variables among the selected extraction parameters i.e. solvent composition, solid: solvent ratio, particle size, time, temperature, pH and extraction cycles on diosgenin yield. Among seven tested parameters only four parameters (particle size, solid: solvent ratio, time and temperature) were found to exert significant effect on the diosgenin extraction. Moreover, Box-Behnken design (BBD) was employed to optimize the significant extraction parameters for maximum diosgenin yield. Results The most suitable condition for diosgenin extraction was found to be solid: solvent ratio (1:45), particle size (1.25 mm), time (45 min) and temperature (45°C). The maximum experimental yield of diosgenin (1.204% dry weight) was observed close to the predicted value (1.202% dry weight) on the basis of the chosen optimal extraction factors. The developed mathematical model fitted well with experimental data for diosgenin extraction. Conclusions Experimental validation revealed that a well trained ANN model has superior performance compared to a RSM model.

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