Process Parameters Optimization for Green Manufacturing of Laser Cladding Based on Artificial Neural Network

2010 ◽  
Vol 97-101 ◽  
pp. 2310-2313 ◽  
Author(s):  
Zong Wei Niu ◽  
Kai Song ◽  
Zhi Yong Li ◽  
F.F. Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Laser Cladding ◽  
Process Parameters ◽  
Green Manufacturing ◽  
Parameters Optimization ◽  
Main Process ◽  
Scan Speed ◽  
Artificial Neural ◽  
Process Factors

Laser cladding is a new developed green manufacturing process. The main process parameters include the power of laser beam, the diameter of laser facula, the scan speed and the quantity of powder supply. It’s difficulty to analyse the influence of the process factors to the product quality because the effect mechanism is quiet complicated. Model of Artificial neural network for the optimization of process parameter in laser cladding manufacturing was developed in this paper. And proper process parameters were achieved which can provide guide for the practice production.

Artificial Intelligence Optimization and Experimental Study of Auto Panel Stamping Process

2011 ◽  
Vol 55-57 ◽  
pp. 1794-1798
Author(s):  
Lin Zhou ◽  
Xiao Min Cheng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Parameters Optimization ◽  
Nonlinear Material ◽  
Object Function ◽  
Stamping Process ◽  
Artificial Neural ◽  
Auto Panel

Auto panel stamping is a complicated plastic deformation process with geometry nonlinear, material nonlinearity and numerous process parameters. The stamping process of a typical auto panel wheel wrap was studied by artificial intelligent optimization and physical experiment. The prediction model of object function was established using artificial neural network. In object function, blank-holder force, drawbead height and fillet radius were selected as the optimized variables and prevention of rupture was considered as the optimization objective. Process parameters optimization was performed with genetic algorithm. The optimized process parameters were used to guide die design and testing, and the result of wheel wrap stamping showed that the forming quality was obviously improved. So the process optimization based on artificial neural network and genetic algorithm is feasible and efficient for auto panel stamping.

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.

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.

scholarly journals Modelling of Rotary EDM Process Parameters of Inconel 718 Using Artificial Neural Networks

Mechanika ◽  
2020 ◽  
Vol 26 (6) ◽  
pp. 540-544
Author(s):  
Jayaraj JEEVAMALAR ◽  
Sundaresan RAMABALAN ◽  
Chinnamuthu SENTHILKUMAR
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Inconel 718 ◽  
Machining Process ◽  
Artificial Neural ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Matlab Programming ◽  
The Relationship

Modelling is used for correlating the relationship between the input process parameters and the output responses during the machining process. To characterize real-world systems of considerable complexity, an Artificial Neural Network (ANN) model is regularly used to replace the mathematical approximation of the relationship. This paper explains the methodological procedure and the outcome of the ANN modeling process for Electrical Discharge Drilling of Inconel 718 superalloy and hollow tubular copper as tool electrode. The most important process parameters in this work are peak current, pulse on time and pulse off time with machining performances of material removal rate and surface roughness. The experiments were performed by L20 Orthogonal Array. In such conditions, an Artificial Neural Network model is developed using MATLAB programming on the Feed Forward Back Propagation technique was used to predict the responses. The experimental data were separated into three parts to train, test the network and validate the model. The developed model has been confirmed experimentally for training and testing in considering the number of iterations and mean square error convergence criteria. The developed model results are to approximate the responses fairly exactly. The model has the mean correlation coefficient of 0.96558. Results revealed that the proposed model can be used for the prediction of the complex EDM drilling process.

Prediction of Characteristic and Performance of Laser Cladding for Al Alloy Based on Artificial Neural Network

2008 ◽  
Vol 35 (10) ◽  
pp. 1632-1636 ◽  
Author(s):  
黄安国 Huang Anguo ◽  
李刚 Li Gang ◽  
汪永阳 Wang Yongyang ◽  
李磊 Li Lei ◽  
李志远 Li Zhiyuan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Laser Cladding ◽  
Al Alloy ◽  
Artificial Neural ◽  
And Performance

Prediction of Friction Coefficients During Scratch Based on an Integrated Finite Element and Artificial Neural Network Method

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Haibo Xie ◽  
Zhanjiang Wang ◽  
Na Qin ◽  
Wenhao Du ◽  
Linmao Qian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Friction Coefficient ◽  
Process Parameters ◽  
Neural Network Method ◽  
Network Method ◽  
Shear Friction ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Abstract An integrated finite element and artificial neural network method is used to analyze the impact of scratch process parameters on some variables related to elastoplastic deformation of titanium alloy. The elastoplastic constitutive parameters applied for scratch simulations are obtained from the nanoindentation experiments and finite element analysis. The validity of the finite element model of scratch is confirmed by comparing the friction forces from simulations to those from experiments. The input parameters of the artificial neural network are three scratch process parameters: tip normal force, tip radius, and shear friction coefficient. The outputs are four variables related to material deformation measured during scratch: scratch depth, elastic recovery height, plowing height, and plowing friction coefficient. The network is trained with pairs of input and output datasets generated by scratch simulations. The prediction results of the neural network are in agreement with the finite element results. The model provides assistance for the prediction and analysis of complex relationships between scratch process parameters and variables related to material deformation, and between the plowing friction coefficient and the relevant parameters. The results show the independence of scratch depth and the shear friction coefficient, and the positive relationships between the shear friction coefficient and plowing friction coefficient.

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