scholarly journals Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network

2020 ◽  
Vol 57 (3) ◽  
pp. 202-223
Author(s):  
Ionut Laurentiu Sandu ◽  
Florin Susac ◽  
Felicia Stan ◽  
Catalin Fetecau
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Injection Pressure ◽  
Mold Temperature ◽  
Ann Model ◽  
Melt Temperature ◽  
Flow Length ◽  
Cavity Thickness ◽  
Cae Simulation

In this study, computer-aided engineering (CAE) simulation software and the design of experiments (DOE) method were used to simulate the injection molding process in terms of the melt flow length, using a spiral part. Process parameters such as melt temperature, mold temperature, injection pressure and mold cavity thickness were considered as injection molding variables. A predictive model for the flow length was created using a three-layer artificial neural network (ANN). The ANN model was trained with both simulation and experimental data, and the predictive performances were compared in terms of correlation coefficient, root mean square error and mean relative error. The cavity thickness and melt temperature were found to be the most significant factors for both the simulation and the experiment, while the injection pressure and the mold temperature had little effect on the flow length. The ANN model trained with Moldex3D data shows a significantly higher prediction capacity than the ANN model trained with experimental data. However, the melt flow lengths predicted by the ANN model for both Moldex3D and Moldflow simulation data are statistically significant, indicating that the proposed prediction methodology, which combines the ANN model, DOE method and the CAE simulation technology, can effectively predict the flow length of injection molded parts, with a small number of data.

scholarly journals An Investigation into the Dynamic Recrystallization (DRX) Behavior and Processing Map of 33Cr23Ni8Mn3N Based on an Artificial Neural Network (ANN)

Materials ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1282 ◽  
Author(s):  
Zhongman Cai ◽  
Hongchao Ji ◽  
Weichi Pei ◽  
Xuefeng Tang ◽  
Long Xin ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Dynamic Recrystallization ◽  
Microstructure Evolution ◽  
Processing Map ◽  
Resistant Steel ◽  
Ann Model ◽  
Heat Resistant Steel ◽  
Artificial Neural Network Ann

Based on an 33Cr23Ni8Mn3N thermal simulation experiment, the application of an artificial neural network (ANN) in thermomechanical processing was studied. Based on the experimental data, a microstructure evolution model and constitutive equation of 33Cr23Ni8Mn3N heat-resistant steel were established. Stress, dynamic recrystallization (DRX) fraction, and DRX grain size were predicted. These models were evaluated by a variety of statistical indicators to determine that these models would work well if applied in predicting microstructure evolution and that they have high precision. Then, based on the weight of the ANN model, the sensitivity of the input parameters was analyzed to achieve an optimized ANN model. Based on the most widely used sensitivity analysis (SA) method (the Garson method), the input parameters were analyzed. The results show that the most important factor for the microstructure of 33Cr23Ni8Mn3N is the strain rate ( ε ˙ ). For the control of the microstructure, the control of the ε ˙ is preferred. ANN was applied to the development of processing map. The feasibility of the ANN processing map on austenitic heat-resistant steel was verified by experiments. The results show that the ANN processing map is basically consistent with processing map based on experimental data. The trained ANN model was implanted into finite element simulation software and tested. The test results show that the ANN model can accurately expand the data volume to achieve high precision simulation results.

Artificial neural network based modeling of the coupled effect of sulphate and temperature on the strength of cemented paste backfill

2011 ◽  
Vol 38 (1) ◽  
pp. 100-109 ◽  
Author(s):  
Libardo Orejarena ◽  
Mamadou Fall
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Design Parameter ◽  
Curing Temperature ◽  
Ann Model ◽  
Large Variability ◽  
Sulphate Content ◽  
Coupled Effect ◽  
Artificial Neural

Among the different options for mine waste management, cemented paste backfills (CPB) have become important in mining operations around the world due to their environmental and economic benefits. The key design parameter of a CPB structure is its mechanical stability, which is commonly evaluated by the uniaxial compressive strength (UCS) of the CPB material. Experimental studies have shown that the sulphate present within the CPB and the curing temperatures can significantly affect the strength of CPBs. The increasing use of CPBs in underground mine operations as well as the subjection of CPBs to a large variability of thermal (curing temperature) and chemical (sulphate content) loads, make it necessary to model and quantify the coupled effect of sulphate and curing temperature on the strength (key design parameter) of CPBs. Therefore, the main objective of this study is to develop a methodological approach and a mathematical model based on an artificial neural network (ANN) to analyze and predict the effect of different amounts of sulphate on the strength of mature CPBs cured at various temperatures. Based on the experimental results of UCS tests from previous studies on various CPBs, the authors have developed an ANN model by using an ANN methodology implemented through MATLAB™. The developed model is validated with experimental data that is not used for the model development. The validation shows good agreement between the predicted and experimental data. The results from the ANN model of this study show that the coupled effect of curing temperature and sulphate significantly affects the strength of CPBs. This effect can be positive (strength increase) or negative (strength decrease) depending on the initial amount of sulphate content, the curing temperature, and type of binder. Furthermore, this study demonstrates that ANN can be used as a valuable tool to evaluate the coupled influence of sulphate and temperature on the strength of CPBs, i.e., it is a suitable tool for the optimization of a CPB mixture.

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Hadi Salehi ◽  
Mosayyeb Amiri ◽  
Morteza Esfandyari
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Energy Gain ◽  
Operating Conditions ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

Inverse Analysis of Material Parameters of Multiple Foam Layers Based on Artificial Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 3313-3316
Author(s):  
Xiao Xiang Su ◽  
Yao Dong Gu ◽  
J.P. Finlay ◽  
I.D. Jenkinson ◽  
Xue Jun Ren
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Nonlinear Material ◽  
Fe Model ◽  
Ann Model ◽  
Nonlinear Parameters ◽  
Closed Cell ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Closed cell polymeric foams are widely used in sport and medical equipments. In this study, an artificial neural network (ANN) based inverse finite element (FE) program has been developed and used to predict the nonlinear material properties of EVA foams with multiple layers. A 2-D parametric FE model was developed and validated against experimental data. Systematic data from FE simulations was used to train and validate the ANN model. The accuracy and validity of the ANN method were assessed based on both blind tests and experimental data. Results showed that the proposed artificial neural network model is robust and efficient in predicating the nonlinear parameters of foam materials.

Artificial Neural Network Approach to Predict Mechanical Properties of 301 Austenitic Stainless Steel

2010 ◽  
Vol 658 ◽  
pp. 145-148 ◽  
Author(s):  
Zhi Yu Chen ◽  
De Ning Zou ◽  
Jun Hui Yu ◽  
Ying Han
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Rolling Reduction ◽  
Ann Model ◽  
Neural Network Approach ◽  
Artificial Neural

In this study, the effect of original thicknesses of plate, the thicknesses of plate after rolling and rolling reduction on the strength in 301 stainless steel was modeled by means of artificial neural network (ANN). The experimental data were collected to obtain training set and testing set. The normalization method was employed for avoiding over-fitting. The optimal ANN method architecture was determined by according to the trial and error procedure. The results of the ANN model were in good agreement with experimental data. As can be seen from the result, we believe that the neural network model can efficiently predict the relationship between mechanical properties and rolling reduction in 301 austenitic stainless steel.

scholarly journals Enhancing the Productivity of Wire Electrical Discharge Machining Toward Sustainable Production by using Artificial Neural Network Modelling

2019 ◽  
Vol 7 (1) ◽  
pp. 261-274 ◽  
Author(s):  
Muhammad Akmal Mohd Zakaria ◽  
Raja Izamshah Raja Abdullah ◽  
Mohd Shahir Kasim ◽  
Mohamad Halim Ibrahim
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Wire Electrical Discharge Machining ◽  
Ann Model

Sustainability plays an important role in manufacturing industries through economically-sound processes that able to minimize negative environmental impacts while having the social benefits. In this present study, the modeling of wire electrical discharge machining (WEDM) cutting process using an artificial neural network (ANN) for prediction has been carried out with a focus on sustainable production. The objective was to develop an ANN model for prediction of two sustainable measures which were material removal rate (as an economic aspect) and surface roughness (as a social aspect) of titanium alloy with ten input parameters. By concerning environmental pollution due to its intrinsic characteristics such as liquid wastes, the water-based dielectric fluid has been used in this study which represents an environmental aspect in sustainability. For this purpose, a feed-forward backpropagation ANN was developed and trained using the minimal experimental data. The other empirical modelling techniques (statistics based) are less in flexibility and prediction accuracy. The minimal, vague data and nonlinear complex input-output relationship make this ANN model simple and perfects method in the manufacturing environment as well as in this study. The results showed good agreement with the experimental data confirming the effectiveness of the ANN approach in the modeling of material removal rate and surface roughness of this cutting process.

scholarly journals Intelligent Predicting System for Modeling of Flexurally – Strengthened Reinforced Concrete Beams with CFRP Laminates

2014 ◽  
Vol 61 (1) ◽  
pp. 25-42
Author(s):  
Ibrahim M. Metwally
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Flexural Strength ◽  
Concrete Beams ◽  
Rc Beams ◽  
Ann Model ◽  
Flexural Strengthening ◽  
Cfrp Laminates

Abstract In the last years, a great number of experimental tests have been performed to determine the ultimate strength of reinforced concrete (RC) beams retrofitted in flexure by means of externally bonded carbon fiber-reinforced polymers (CFRP). Most of design proposals for flexural strengthening are based on a regression analysis from experimental data corresponding to specific configurations which makes it very difficult to capture the real interrelation among the involved parameters. To avoid this, an intelligent predicting system such as artificial neural network (ANN) has been developed to predict the flexural capacity of concrete beams reinforced with this method. An artificial neural network model was developed using past experimental data on flexural failure of RC beams strengthened by CFRP laminates. Fourteen input parameters cover the CFRP properties, beam geometrical properties and reinforcement properties; the corresponding output is the ultimate load capacity. The proposed ANN model considers the effect of these parameters which are not generally account together in the current existing design codes with the purpose of reaching more reliable designs. This paper presents a short review of the well-known American building code provisions (ACI 440.2R-08) for the flexural strengthening of RC beams using FRP laminates. The accuracy of the code in predicting the flexural capacity of strengthened beams was also examined with comparable way by using same test data. The study shows that the ANN model gives reasonable predictions of the ultimate flexural strength of the strengthened RC beams. Moreover, the study concludes that the ANN model predicts the flexural strength of FRPstrengthened beams better than the design formulas provided by ACI 440

scholarly journals ANN for Tribological Applications

2009 ◽  
Author(s):  
Nasir Tanzim ◽  
Salih Nbhan ◽  
Tze Hui Liew ◽  
Chee Wen Chin ◽  
B. F. Yousif
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Steady State ◽  
Friction Coefficient ◽  
Current Work ◽  
Ann Model ◽  
Reasonable Accuracy ◽  
Layered Model ◽  
Artificial Neural Network Ann

The current work is an attempt to investigate the possibility of using artificial neural network (ANN) modelling as a tool for friction coefficient prediction. The ANN model was trained at various configurations with different functions of training to develop the optimal ANN model. The experimental data was obtained from previous works. The results revealed that single layered model has reasonable accuracy in prediction when trained with TrainLM function. The results were acceptable especially in predicting steady-state friction coefficient, which proved ANN technology’s ability to predict the friction co-efficient.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

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