Intelligent Optimization of Material Mixing Ratio and Process Parameters for Aerated Concrete

2011 ◽  
Vol 243-249 ◽  
pp. 7026-7035
Author(s):  
Fang Yang ◽  
Lin Zhu Sun ◽  
Zi Ling Xie ◽  
Ya Gang Zhou
Keyword(s):  
Neural Network ◽  
Process Parameters ◽  
Reference Value ◽  
Model System ◽  
Process Equipment ◽  
Mixing Ratio ◽  
Sample Test ◽  
Aerated Concrete ◽  
Optimal Material ◽  
Material Mixing

To determine the optimal material mixing ratio of and process parameters for aerated concrete products, we adopted various combinations of material mixing ratio and process parameters to test aerated concrete according to the characteristics of local material and process equipment in this study, and obtained the strength and density of the aerated concrete. Using artificial neural network, we built a three-layer neural network model, which was trained based on the data of test samples to obtain a neural network based model system. Sample test showed that the predicted values of the model system fit the test values well; we utilized this system to analyze the material mixing ratio of and process parameters for aerated concrete products, and obtained their optimization results. The strength and density of the aerated concrete manufactured with the optimized parameters reached the desired targets. This method has some reference value for instructing the production of aerated concrete.

Research on the Applicability and Property of Iron Tailing to Produce Autoclaved Concrete

2013 ◽  
Vol 357-360 ◽  
pp. 982-985
Author(s):  
Ren Juan Sun ◽  
Da Wei Huang ◽  
Mao Zheng Li ◽  
Shan Shan Wei ◽  
Zhi Qin Zhao ◽  
...  
Keyword(s):  
Final Height ◽  
Radioactive Substance ◽  
Mixing Ratio ◽  
Height Ratio ◽  
Sample Test ◽  
Research Findings ◽  
Aerated Concrete ◽  
Mineral Compositions ◽  
The Given ◽  
Material Mixing

Tailings use is an important environment program and also society subject. Iron tailing is an environmental pollution resources, but it can be used to produce autoclaved aerated concrete (AAC). The chemical composition of iron tailing and the mineral compositions were analyzed by XRD test. The content of radioactive substance in iron tailing was also tested. The testing results show that the iron tailing meet the requirment for AAC production. Iron tailing was sieved to analyze the gradation and was milled by the ball grinding mill machine to proper fineness. Based on the given material mixing ratio, sample test showed that the density of AAC varies from 438 to 629 kg m-3 with different finenesses of iron tailing, and the height ratio of final height to casting height of AAC changes with the finenesses of iron tailing. The research findings have important lessons for the production of aerated concrete with tailing.

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

2021 ◽  
Author(s):  
Sherwan Mohammed Najm ◽  
Imre Paniti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Single Point ◽  
Tool Material ◽  
Deformation Mode ◽  
Geometric Accuracy ◽  
Forming Process ◽  
Tool Shape ◽  
Artificial Neural

AbstractIncremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

Modeling and Analysis of Compressive Properties of Porous NiTi Shape Memory Alloy Using Artificial Neural Network

2008 ◽  
Vol 41-42 ◽  
pp. 135-140 ◽  
Author(s):  
Qiang Li ◽  
Xu Dong Sun ◽  
Jing Yuan Yu ◽  
Zhi Gang Liu ◽  
Kai Duan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Thermal Explosion ◽  
Process Parameters ◽  
Niti Shape Memory Alloy ◽  
Compressive Properties ◽  
Porous Niti ◽  
Bp Model

Artificial neural network (ANN) is an intriguing data processing technique. Over the last decade, it was applied widely in the chemistry field, but there were few applications in the porous NiTi shape memory alloy (SMA). In this paper, 32 sets of samples from thermal explosion experiments were used to build a three-layer BP (back propagation) neural network model. According to the registered BP model, the effect of process parameters including heating rate ( ), green density ( ) and particle size of Ti ( d ) on compressive properties of reacted products including ultimate compressive strength ( v D σ ) and ultimate compressive strain (ε ) was analyzed. The predicted results agree with the actual data within reasonable experimental error, which shows that the BP model is a practically very useful tool in the properties analysis and process parameters design of the porous NiTi SMA prepared by thermal explosion method.

The Artificial Neural Network and Regression Analysis Approach to Prediction of the Fiber Diameter of Spunbonding Fabrics

2010 ◽  
Vol 33 ◽  
pp. 74-78
Author(s):  
B. Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Fiber Diameter ◽  
Statistical Regression ◽  
Artificial Neural

In this work, the artificial neural network model and statistical regression model are established and utilized for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, which is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical regression model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

Design of Neural Network Based on Rough Set

2012 ◽  
Vol 263-266 ◽  
pp. 3378-3381
Author(s):  
Xue Min Zhang ◽  
Zhen Dong Mu
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Sample Test ◽  
Low Efficiency ◽  
The Right ◽  
Test Module

After years of development, the neural network classification, clustering and forecasting applications have a lot of development, but the neural network has the inevitable defects, if you enter the attribute set, the classification boundaries are not clear, convergence low efficiency and accuracy, there may even be the state does not converge, using rough set theory, the right value to modify the function to be modified, and joined the contradictions sample test module, after the use of EEG to verify reached the deletion of number of features and the purpose to improve the classification accuracy.

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