Modelling and optimization of Nd:YAG laser micro-turning process during machining of aluminum oxide (Al 2 O 3 ) ceramics using response surface methodology and artificial neural network (original) (raw)
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Optics and Lasers in Engineering, 2009
Nd:YAG laser turning is a new technique for manufacturing micro-grooves on cylindrical surface of ceramic materials needed for the present day precision industries. The importance of laser turning has directed the researchers to search how accurately micro-grooves can be obtained in cylindrical parts. In this paper, laser turning process parameters have been determined for producing square micro-grooves on cylindrical surface. The experiments have been performed based on the statistical five level central composite design techniques. The effects of laser turning process parameters i.e. lamp current, pulse frequency, pulse width, cutting speed (revolution per minute, rpm) and assist gas pressure on the quality of the laser turned micro-grooves have been studied. A predictive model for laser turning process parameters is created using a feed-forward artificial neural network (ANN) technique utilized the experimental observation data based on response surface methodology (RSM). The optimization problem has been constructed based on RSM and solved using multi-objective genetic algorithm (GA). The neural network coupled with genetic algorithm can be effectively utilized to find the optimum parameter value for a specific laser micro-turning condition in ceramic materials. The optimal process parameter settings are found as lamp current of 19 A, pulse frequency of 3.2 kHz, pulse width of 6% duty cycle, cutting speed as 22 rpm and assist air pressure of 0.13 N/mm 2 for achieving the predicted minimum deviation of upper width of À0.0101 mm, lower width 0.0098 mm and depth À0.0069 mm of laser turned micro-grooves.
Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2010
Selection of machining parameter combinations for obtaining optimum circularity at entry and exit and hole taper is a challenging task in laser microdrilling owing to the presence of a large number of process variables. There is no perfect combination of parameters that can simultaneously result in higher circularity at entry and exit and lower hole taper. The current paper attempts to develop a strategy for predicting machining parameter settings for the generation of the maximum circularity at entry and exit and minimum hole taper. An artificial neural network (ANN) is used for process modelling of laser microdrilling of titanium nitride-alumina composite and a feed-forward back-propagation network is developed to model the machining process. The model, after proper training, is capable of predicting the response parameters as a function of five different control parameters. Experimental results demonstrate that the machining model is suitable and the optimization strategy satisfies practical requirements. The developed model is found to be unique, powerful, and flexible.
International Journal of Industrial Engineering Computations, 2015
Laser direct structuring (LDS) is very important step in the MID process and it is a complex process due to different parameters, which influence on this process and its final product. Therefore, it is very important to use a reliable model to predict, analyze and control the performance of the (LDS) process and the quality of the final product. In this work we develop mathematical models by using Artificial Neural Network (ANN) and Response Surface Methodology (RSM) to study this process. The proposed models are used to study the effect of the LDS parameters on the groove dimensions (width and depth), lap dimensions (groove lap width and height) and finally the heat effective zone (interaction width), which are important to determine the line width/space in the MID products and the metallization profile after the metallization step. We also study the relationship between the LDS parameters and the surface roughness which is very important factor for the adhesion strength of MID structures. Moreover these models capable of finding a set of optimum LDS parameters that provide the required micro-channel dimensions with the best or the suitable surface roughness. A set of experimental tests are carried out to validate the developed ANN and the RSM models. It has been found that the predicted values for the proposal ANN and RSM models were closer to the experimental values, and the overall average absolute percentage errors were 4.02 % and 6.52%, respectively. Finally, it has been found that, the developed ANN model could be used to predict the response of the LDS process more accurately than RSM model.
Prediction the Influence of Machining Parameters for CNC Turning of Aluminum Alloy Using RSM and ANN
Engineering and Technology Journal, 2020
The main objective of this paper is to develop a prediction model using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) for the turning process of Aluminum alloy 6061 round rod. The turning experiments carried out based on the Central Composite Design (CCD) of Response Surface Methodology. The influence of three independent variables such as Cutting speed (150, 175 and 200 mm/ min), depth of cut (0.5, 1 and 1.5 mm) and feed rate (0.1, 0.2 and 0.3 mm/rev) on the Surface Roughness (Ra) were analyzed through analysis of variance (ANOVA). The response graphs from the Analysis of Variance (ANOVA) present that feed-rate has the strongest influence on Ra dependent on cutting speed and depth of cut. Surface response methodology developed between the machining parameters and response and confirmation experiments reveals that the good agreement with the regression models. The coefficient of determination value for RSM model is found to be high (R2 = 0.961). It indicates...
2014
Laser micromachining technology finds great potentials for successful application in the area of high precision micro-engineering. Laser micro-turning process is one of the new and emerging technologies in the area of laser material processing (LMP) of engineering materials. Laser micro-turning process is one of the latest promising laser material processing techniques which can be employed for generation of micro-turning surface of particular surface profile and dimensional accuracy on cylindrical workpiece. The present paper addresses the laser micro-turning process of cylindrical shaped 99% pure aluminium oxide (Al2O3) ceramics of size 10 mm in diameter and 40 mm in length. The experiments have been conducted utilizing one factor at a time (OFAT) experimental scheme. The targated depth was set at 100 µm. Laser average power, pulse frequency, workpiece rotating speed and Y feed rate were considered as process variables. After each experiment, surface roughness (Ra and Rt) has been...
Mahesh Gopal, 2021
The aim of this study is to determine the effect of the machining parameters and tool geometry. The turning operation is carried out as per the Design of Experiments (DoE) of Response Surface Methodology (RSM) to predict the temperature rise of aluminium-6061 as a cutting material and Al2O3 coated carbide tool is used as a cutting tool for turning operation. The ANOVA analysis is used to measure the performance quality and mathematical model is developed. The values of probability >(F) is less than 0.05 indicates, the model conditions are significant. The cutting speed is the most influencing parameters compared to other parameters. For the optimum machining parameters leading to temperature rise, the Artificial Neural Network (ANN) model is trained and tested using MAT Lab software. The ANN recommends best minimum predicted value of temperature rise. The confirmatory analysis results, the predicted values were found to be in commendable agreement with the experimental values.
Materials Today: Proceedings, 2018
Laser assisted hybrid machining being researched in past decade on various difficult to machine materials to improve the machinability. Predictive modeling approaches such as response surface method (RSM) and artificial neural network (ANN) are widely applied for model development. However, no reported work using RSM and ANN approaches to predict the relationship between the experimental variables (speed, feed, laser power and beam apporach angle) on surface roughness Ra (μm). Furthermore, coefficient of correlation (R2), root mean square error (RMSE) and model predictive error (MPE) are considered as a performance measures for their effectiveness. The results show that the ANN model estimates the machinability indices with high accuracy with a limited number of experiments compared to the response surface model. From the comparative study, ANN model is found to be capable for better prediction of response than the RSM model. ANN model provides a maximum precision benefit of 10% for surface roughness Ra (μm) compared with RSM model. Also the calculated Pearson correlation coefficient showed a robust relationship between the laser beam angle and Ra, surface roughness followed by the speed.
Neural network modeling and analysis of the material removal process during laser machining
International Journal of Advanced Manufacturing Technology, 2003
To manufacture parts with nano- or micro-scale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. In addition, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy.
Artificial Intelligent Model to Predict Surface Roughness in Laser Machining
2009
Light Amplification Stimulation Emission of Radiation or the common name is Laser. The laser light differs from ordinary light due to it has the photons of same frequency, wavelength and phase. Advantages of using laser beam cutting (LBC) are materials with complex figures can easily be cut by incorporating computer numerical control (CNC) motion equipment, LBC has high cutting speed, Low distortion, very high edge quality and most important thing is LBC has a minimal heat affected zone (HAZ).This paper discussed the development of Radian Basis Function Network (RBFN) to predict surface roughness when laser cutting acrylic sheet. The main objectives of this paper are to find the optimum laser parameters (power, material thickness, tip distance and laser speed) and the effect of these parameters on surface roughness. The network was trained until it predict closer to the experimental values. It observed that some of good surface roughness specimen fail in terms of structure when investigate under microscope.