Plasma spray process modelling using artificial neural networks: Application to Al2O3-TiO2 (13% by weight) ceramic coating structure

2004 ◽  
Vol 120 ◽  
pp. 363-370
Author(s):  
S. Guessasma ◽  
G. Montavon ◽  
C. Coddet
Keyword(s):  
Thermal Spraying ◽  
Processing Parameters ◽  
Ceramic Coatings ◽  
Physical Models ◽  
Spray Process ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Stability ◽  
Experimental Values ◽  
Physical Phenomena

Thermal spraying is a versatile technique of coating manufacturing implementing large variety of materials and processes. The manufacture control is constrained by the understanding of the physical phenomena occurring during the spraying. It is however penalized by the large number of processing parameters (up to 50), their interdependencies, their correlations with the coating attributes and the stability of the process. Numerous statistical, heuristic or physical models intended to response to these constrains, very often partially because considering some aspects of the process. This work aims at considering a more global approach based on a powerful statistical methodology using artificial intelligence. Following this approach, the physical phenomena are encoded in a structure called Artificial Neural Network (ANN). An application of the ANN methodology is discussed in the case of the APS spray process. Some processing parameters categories are related to some coating properties for alumina-titania (13% by weight) ceramic coatings. ANN optimization is presented and discussed. Predicted results show globally a well agreement with the experimental values. Some conclusions point out the advantages of the ANN on the conventional methods, such as the design of experiments, used usually to recognize the controlling factors in a process.

Plasma Spheroidized Alumina/Zircon Mixtures

2000 ◽  
Author(s):  
Y. Li ◽  
K.A. Khor
Keyword(s):  
Thermal Spraying ◽  
Processing Parameters ◽  
High Energy ◽  
Molten State ◽  
Composite Powders ◽  
Spray Process ◽  
Plasma Spheroidization ◽  
Plasma Spray Process ◽  
Energy Ball ◽  
Spheroidized Powder

Abstract The plasma-spray process is specified by the associated processing parameters, where these influence the properties of the resultant deposits. This article describes the preparation and processing of composite powders for use in thermal spraying by mixing high purity zircon and alumina powders. The spheroidized powder were obtained by high energy ball milling and rapid solidification from the molten state during plasma spraying. The article discusses the processes involved in spray drying and plasma spheroidization, describing thermal analysis and mullitization kinetics in the spheroidized alumina/zircon mixtures.

Prediction and optimization of stability parameters for titanium dioxide nanofluid using response surface methodology and artificial neural networks

2013 ◽  
Vol 20 (4) ◽  
pp. 319-330 ◽  
Author(s):  
Ali Sadollah ◽  
Azadeh Ghadimi ◽  
Ibrahim H. Metselaar ◽  
Ardeshir Bahreininejad
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Lauryl Sulfate ◽  
Ann Model ◽  
Stability Parameters ◽  
Weight Percentage ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Stability

AbstractThe effect of various process parameters on the stability of TiO2 nanofluid, which can mostly be defined as zeta potential and particle size, was studied using response surface methodology (RSM) by the design of experiments and was predicted through a trained artificial neural network (ANN). The process parameters studied were weight percentage of surfactant (sodium lauryl sulfate) (0.01–0.2 wt%) and the value of pH (10–12). Central composite design and the RSM were employed to develop a mathematical model as well as to define the optimum condition. A three-layered feed-forward ANN model was designed and used for the prediction of the stability parameters. From the analysis of variance, the significant factors that affected the experimental design responses were also identified. The predicted stability parameters using the RSM and ANNs were compared using figures and tables. It is shown that the trained ANN outperformed the RSM in terms of accuracy and prediction of obtained results.

Artificial neural network-based modelling of optimized experimental study of xylanase production by Penicillium citrinum xym2

2021 ◽  
pp. 095440892110641
Author(s):  
Gaurav Kumar ◽  
Shyama Prasad Saha ◽  
Shilpi Ghosh ◽  
Pranab Kumar Mondal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Enzyme Production ◽  
Xylanase Activity ◽  
Ann Model ◽  
Process Variables ◽  
Xylanase Production ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Experimental Values

The industrial production of enzymes is generally optimized by one-factor-at-a-time (OFAT) approach. However, enzyme production by the method involves submerged or solid-state fermentation, which is laborious and time-consuming and it does not consider interactions among process variables. Artificial neural network (ANN) offers enormous potential for modelling biochemical processes and it allows rational prediction of process variables of enzyme production. In the present work, ANN has been used to predict the experimental values of xylanase production optimized by OFAT. This makes the reported ANN model to predict further optimal values for different input conditions. Both single hidden layered (6-3-1) and double hidden layered (6-12-12-1) were able to closely predict the actual values with MSE equals to 0.004566 and 0.002156, respectively. The study also uses multiple linear regression (MLR) analysis to calculate and compare the outcome with ANN predicted xylanase activity, and to establish a parametric sensitivity.

scholarly journals Artificial Neural Network Algorithms for 3D Printing

Materials ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 163
Author(s):  
Muhammad Arif Mahmood ◽  
Anita Ioana Visan ◽  
Carmen Ristoscu ◽  
Ion N. Mihailescu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
3D Printing ◽  
Process Parameters ◽  
Optimization Methods ◽  
Physical Models ◽  
Network Algorithms ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.

scholarly journals Compressive Strength and Slump Prediction of Two Blended Agro Waste Materials Concretes

2019 ◽  
Vol 13 (1) ◽  
pp. 118-128 ◽  
Author(s):  
Oluwaseye Onikeku ◽  
Stanley M. Shitote ◽  
John Mwero ◽  
Adeola. A. Adedeji ◽  
Christopher Kanali
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Degrees Of Freedom ◽  
Industrial Wastes ◽  
P Value ◽  
Data Sets ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Bamboo Leaf Ash ◽  
Experimental Values

Background: Agro industrial wastes such as Bamboo Leaf Ash (BLA) and Bagasse Ash (BA) need to be employed so as to replace cement in order to produce cheaper concrete, which, in turn, save the environment. Objective: This research focuses on the compressive strength and slump based on Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models for forecasting of compressive strength and slump value for concrete by blending BLA and BA as partial supplementary cement materials accordingly. Methods: Three-layer perceptron was constructed through R (nnet package). A sum total of eleven artificial neural networks were formulated using 214 data sets attained from 27 laboratory concrete mixtures performed. Results: The neural network model forecasted the compressive strength for training, testing and validation with predicted errors of 0.802 MPa and 1.380 MPa. The model over forecasted the compressive strength averagely by 0.644 MPa and 1.905 MPa. The forecasted compressive strength changed averagely by 2.328% and 3.946%. The average difference between the predicted and experimental values was 0.588 MPa and 1.050 MPa. The coefficients of determination were 0.961 and 0.905. The MLR model predicted the slump with predictive error values of 6.634 mm and 8.374 mm. The predicted slump deviated on average by 3.633% and 8.034%. The residual error was 3.075 on 12 degrees of freedom. The multiple R2 and adjusted R2 were 0.9336 and 0.9115. The P-value was found to be 5.639e-07. Conclusion: The results show that ANN and MLR are potent tools for forecasting the compressive strength and slump of concrete blending bamboo leaf ash and baggage ash. Hence, this contributes towards forecasting of the compressive strength and slump of BLA and BA blended concrete. They extends 28 days compressive strength usually found in the literature to 56 and 90 days compressive strength and there was a remarkable improvement as curing age increases. The slump of combined effect of blending BLA and BA at different percentage replacements was tested. In this study, we used BLA blended with BA to produce concrete which is an innovation.

scholarly journals ARTIFICIAL NEURAL NETWORK IN THE MODELLING OF THE EFFECT OF CHROMIUM DOPANTS ON THE MECHANICAL PROPERTIES OF AL-4%CU ALLOY

2019 ◽  
Vol 25 (1) ◽  
Author(s):  
EMAGBETERE EYERE ◽  
PETER ARUOTURE OGHENEKOWHO ◽  
IFEANYI ASHIEDU FESTUS
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Percentage Elongation ◽  
Cu Alloy ◽  
Simulation Results ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Experimental Values ◽  
Strong Agreement

Artificial Neural Network (ANN) was used to model the effect of Chromium dopants on the mechanical properties duralumin (Al-4 %Cu). The results showed that the hardness, yield strength, and ultimate tensile strength increased, while the energy absorbed and percentage elongation decreased, with increasing %wt of Chromium dopants. Simulation results of ANN show strong agreement with experimental values, having satisfactory R-values of Mean Square Error. ANN can suitably be used to predict the mechanical properties of Al-4%Cu doped with Chromium.

scholarly journals Prediction of Concrete Compressive Strength & Slump using Artificial Neural Networks (ANN)

2021 ◽  
Vol 9 (2) ◽  
pp. 84-89
Author(s):  
Yasir M. H. Badawi ◽  
Yousif Hummaida Ahmed
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Ann Model ◽  
Concrete Compressive Strength ◽  
Concrete Mix Design ◽  
Fine Aggregates ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Experimental Values ◽  
Strength And Durability

Concrete is the most used building material in the world, due to its high compressive strength and durability. Those properties are measured and assessed in fresh and hardened states of concrete, with standard methods which are time and cost consuming. In the present study, the compressive strength and slump of concrete has been predicted using Artificial Neural Network (ANN), which is constructed using different input parameters involving concrete mix design (i.e. coarse & fine aggregates properties, cement content, water/cement (W/C) ratio, admixtures type and dosage …etc.).The predicted strength was compared with the experimentally obtained actual compressive strength and slump data collected in many years for different materials and mix designs in the Sudan. An ANN model has been developed by using MATLAB neural network toolbox. A good co-relationships with regression values of 0.915 and 0.931 for strength and slump respectively have been obtained between the predicted and experimental values. It is concluded that the ANN method can gain acceptable predictions for compressive strength and slump.  

scholarly journals Suspension Plasma-Sprayed Thermal Barrier Coatings for Light-Duty Diesel Engines

2019 ◽  
Vol 28 (7) ◽  
pp. 1674-1687 ◽  
Author(s):  
Wellington Uczak de Goes ◽  
Joop Somhorst ◽  
Nicolaie Markocsan ◽  
Mohit Gupta ◽  
Kseniya Illkova
Keyword(s):  
Combustion Chamber ◽  
Functional Properties ◽  
Diesel Engines ◽  
Fuel Efficiency ◽  
Thermal Spraying ◽  
Ceramic Coatings ◽  
Thermal Spray Process ◽  
Spray Process ◽  
Piston Head ◽  
Light Duty

Abstract Demands for improved fuel efficiency and reduced CO2 emissions of diesel engines have been the driving force for car industry in the past decades. One way to achieve this would be by using thermal spraying to apply a thermal insulation layer on parts of the engine’s combustion chamber. A candidate thermal spray process to give coatings with appropriate properties is suspension plasma spray (SPS). SPS, which uses a liquid feedstock for the deposition of finely structured columnar ceramic coatings, was investigated in this work for application in light-duty diesel engines. In this work, different spray processes and materials were explored to achieve coatings with optimized microstructure on the head of aluminum pistons used in diesel engine cars. The functional properties of the coatings were evaluated in single-cylinder engine experiments. The influence of thermo-physical properties of the coatings on their functional properties has been discussed. The influence of different spray processes on coating formation on the complex piston head profiles has been also discussed. The results show that SPS can be a promising technique for producing coatings on parts of the combustion chamber, which can possibly lead to higher engine efficiency in light-duty diesel engines.

scholarly journals PREDICTION OF THE SIZE OF NANOPARTICLES AND MICROSPORE SURFACE AREA USING ARTIFICIAL NEURAL NETWORK

2018 ◽  
Vol 1 (1) ◽  
pp. 65
Author(s):  
Dženana Sarajlić ◽  
Layla Abdel-Ilah ◽  
Adnan Fojnica ◽  
Ahmed Osmanović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Surface Area ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Experimental Values

This paper presents development of Artificial Neural Network (ANN) for prediction of the size of nanoparticles (NP) and microspore surface area (MSA). Developed neural network architecture has the following three inputs: the concentration of the biodegradable polymer in the organic phase, surfactant concentration in the aqueous phase and the homogenizing pressure. Two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer is trained, using Levenberg-Marquardt training algorithm. For training of this network, as well as for subsequent validation, 36 samples were used. From 36 samples which were used for subsequent validation in this ANN, 80,5% of them had highest accuracy while 19,5% of output data had insignificant differences comparing to experimental values.

scholarly journals ARTIFICIAL NEURAL NETWORK IN THE MODELLING OF THE EFFECT OF CHROMIUM DOPANTS ON THE MECHANICAL PROPERTIES OF AL-4%CU ALLOY

2019 ◽  
Vol 25 (1) ◽  
pp. 16-24
Author(s):  
EYERE EMAGBETERE ◽  
OGHENEKOWHO PETER ARUOTURE ◽  
FESTUS IFEANYI ASHIEDU
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Percentage Elongation ◽  
Cu Alloy ◽  
Simulation Results ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Experimental Values ◽  
Strong Agreement

Artificial Neural Network (ANN) was used to model the effect of Chromium dopants on the mechanical properties duralumin (Al-4 %Cu). The results showed that the hardness, yield strength, and ultimate tensile strength increased, while the energy absorbed and percentage elongation decreased, with increasing %wt of Chromium dopants. Simulation results of ANN show strong agreement with experimental values, having satisfactory R-values of Mean Square Error. ANN can suitably be used to predict the mechanical properties of Al-4%Cu doped with Chromium.

Sign in / Sign up

Export Citation Format

Share Document