The Experts below are selected from a list of 21231 Experts worldwide ranked by ideXlab platform

Safian Sharif - One of the best experts on this subject based on the ideXlab platform.

  • Regression and ANN models for estimating minimum value of Machining performance
    Applied Mathematical Modelling, 2012
    Co-Authors: Azlan Mohd Zain, Habibollah Haron, Noman Qasem, Safian Sharif
    Abstract:

    Surface roughness is one of the most common performance measurements in Machining process and an effective parameter in representing the quality of machined surface. The minimization of the Machining performance measurement such as surface roughness (Ra) must be formulated in the standard mathematical model. To predict the minimum Ra value, the process of modeling is taken in this study. The developed model deals with real experimental data of the Ra in the end Milling Machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum Ra value. The results show that regression and ANN models have reduced the minimum Ra value of real experimental data by about 1.57% and 1.05%, respectively.

  • application of ga to optimize cutting conditions for minimizing surface roughness in end Milling Machining process
    Expert Systems With Applications, 2010
    Co-Authors: Azlan Mohd Zain, Habibollah Haron, Safian Sharif
    Abstract:

    This study is carried out to observe the optimal effect of the radial rake angle of the tool, combined with speed and feed rate cutting conditions in influencing the surface roughness result. In Machining, the surface roughness value is targeted as low as possible and is given by the value of the optimal cutting conditions. By looking at previous studies, as far as they have been reviewed, it seems that the application of GA optimization techniques for optimizing the cutting conditions value of the radial rake angle for minimizing surface roughness in the end Milling of titanium alloy is still not given consideration by researchers. Therefore, having dealt with radial rake angle Machining parameter, this study attempts the application of GA to find the optimal solution of the cutting conditions for giving the minimum value of surface roughness. By referring to the real Machining case study, the regression model is developed. The best regression model is determined to formulate the fitness function of the GA. The analysis of this study has proven that the GA technique is capable of estimating the optimal cutting conditions that yield the minimum surface roughness value. With the highest speed, lowest feed rate and highest radial rake angle of the cutting conditions scale, the GA technique recommends [email protected] as the best minimum predicted surface roughness value. This means the GA technique has decreased the minimum surface roughness value of the experimental sample data, regression modelling and response surface methodology technique by about 27%, 26% and 50%, respectively.

  • prediction of surface roughness in the end Milling Machining using artificial neural network
    Expert Systems With Applications, 2010
    Co-Authors: Azlan Mohd Zain, Habibollah Haron, Safian Sharif
    Abstract:

    This paper presents the ANN model for predicting the surface roughness performance measure in the Machining process by considering the Artificial Neural Network (ANN) as the essential technique for measuring surface roughness. A revision of several previous studies associated with the modelling issue is carried out to assess how capable ANN is as a technique to model the problem. Based on the studies conducted by previous researchers, the abilities and limitations of the ANN technique for predicting surface roughness are highlighted. Utilization of ANN-based modelling is also discussed to show the required basic elements for predicting surface roughness in the Milling process. In order to investigate how capable the ANN technique is at estimating the prediction value for surface roughness, a real Machining experiment is referred to in this study. In the experiment, 24 samples of data concerned with the Milling operation are collected based on eight samples of data of a two-level DOE 2^k full factorial analysis, four samples of centre data, and 12 samples of axial data. All data samples are tested in real Machining by using uncoated, TiAIN coated and SN"T"R coated cutting tools of titanium alloy (Ti-6A1-4V). The Matlab ANN toolbox is used for the modelling purpose with some justifications. Feedforward backpropagation is selected as the algorithm with traingdx, learngdx, MSE, logsig as the training, learning, performance and transfer functions, respectively. With three nodes in the input layer and one node in the output layer, eight networks are developed by using different numbers of nodes in the hidden layer which are 3-1-1, 3-3-1, 3-6-1, 3-7-1, 3-1-1-1, 3-3-3-1, 3-6-6-1 and 3-7-7-1 structures. It was found that the 3-1-1 network structure of the SN"T"R coated cutting tool gave the best ANN model in predicting the surface roughness value. This study concludes that the model for surface roughness in the Milling process could be improved by modifying the number of layers and nodes in the hidden layers of the ANN network structure, particularly for predicting the value of the surface roughness performance measure. As a result of the prediction, the recommended combination of cutting conditions to obtain the best surface roughness value is a high speed with a low feed rate and radial rake angle.

  • Genetic Algorithm for optimizing cutting conditions of uncoated carbide (WC-Co) in Milling Machining operation
    2009 Innovative Technologies in Intelligent Systems and Industrial Applications, 2009
    Co-Authors: Azlan Mohd Zain, Habibollah Haron, Safian Sharif
    Abstract:

    This paper presents the capability of Genetic Algorithm (GA) technique in obtaining the optimal Machining parameters for uncoated carbide (WC-Co) tool to minimize the surface roughness (R a ) value in Milling process. The optimal Machining parameters are generated using MATLAB Optimization toolbox. Regression technique is applied to create the surface roughness predicted equation to be taken as a fitness function of the GA. Result of this study indicated that the GA technique capable to estimate the optimal cutting conditions that yields to the minimum R a value. With high speed, low feed and high radial rake angle of the cutting conditions rate, GA technique recommended 0.17533µm as the best minimum predicted surface roughness value. Consequently, the GA technique has decreased the minimum surface roughness value of the experimental data by about 25.7 %.

Bizhan Rahmati - One of the best experts on this subject based on the ideXlab platform.

  • morphology of surface generated by end Milling al6061 t6 using molybdenum disulfide mos2 nanolubrication in end Milling Machining
    Journal of Cleaner Production, 2014
    Co-Authors: Bizhan Rahmati, Ahmed A D Sarhan, M Sayuti
    Abstract:

    Aluminum alloys are among the most significant metals in industries. The AL6061-T6 aluminum alloy is a highly prominent alloy due to its dominant mechanical properties, such as weldability, hardness and sustainability at high temperatures. AL6061-T6 is commonly used in heavy industries including aerospace, aircraft, automotive, food packaging, etc. Milling of AL6061-T6 is important, especially to create product shape varieties for different applications. However, the demand for high quality draws attention to product quality, particularly machined surface roughness, as it directly affects the product’s appearance, function and reliability. Applying correct lubrication to the Machining zone can enhance the tribological characteristics of AL6061-T6 alloy. For further improvement, introducing nanolubricant may yield superior product quality due to the rolling action of nanoparticles at the tool-workpiece interface, which significantly reduces cutting force. In this research work, a nanolubricant containing MoS2 nanoparticles is developed for end Milling of AL6061-T6 alloy and the surface morphology of the machined workpiece is investigated as well.

Azlan Mohd Zain - One of the best experts on this subject based on the ideXlab platform.

  • Regression and ANN models for estimating minimum value of Machining performance
    Applied Mathematical Modelling, 2012
    Co-Authors: Azlan Mohd Zain, Habibollah Haron, Noman Qasem, Safian Sharif
    Abstract:

    Surface roughness is one of the most common performance measurements in Machining process and an effective parameter in representing the quality of machined surface. The minimization of the Machining performance measurement such as surface roughness (Ra) must be formulated in the standard mathematical model. To predict the minimum Ra value, the process of modeling is taken in this study. The developed model deals with real experimental data of the Ra in the end Milling Machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum Ra value. The results show that regression and ANN models have reduced the minimum Ra value of real experimental data by about 1.57% and 1.05%, respectively.

  • application of ga to optimize cutting conditions for minimizing surface roughness in end Milling Machining process
    Expert Systems With Applications, 2010
    Co-Authors: Azlan Mohd Zain, Habibollah Haron, Safian Sharif
    Abstract:

    This study is carried out to observe the optimal effect of the radial rake angle of the tool, combined with speed and feed rate cutting conditions in influencing the surface roughness result. In Machining, the surface roughness value is targeted as low as possible and is given by the value of the optimal cutting conditions. By looking at previous studies, as far as they have been reviewed, it seems that the application of GA optimization techniques for optimizing the cutting conditions value of the radial rake angle for minimizing surface roughness in the end Milling of titanium alloy is still not given consideration by researchers. Therefore, having dealt with radial rake angle Machining parameter, this study attempts the application of GA to find the optimal solution of the cutting conditions for giving the minimum value of surface roughness. By referring to the real Machining case study, the regression model is developed. The best regression model is determined to formulate the fitness function of the GA. The analysis of this study has proven that the GA technique is capable of estimating the optimal cutting conditions that yield the minimum surface roughness value. With the highest speed, lowest feed rate and highest radial rake angle of the cutting conditions scale, the GA technique recommends [email protected] as the best minimum predicted surface roughness value. This means the GA technique has decreased the minimum surface roughness value of the experimental sample data, regression modelling and response surface methodology technique by about 27%, 26% and 50%, respectively.

  • prediction of surface roughness in the end Milling Machining using artificial neural network
    Expert Systems With Applications, 2010
    Co-Authors: Azlan Mohd Zain, Habibollah Haron, Safian Sharif
    Abstract:

    This paper presents the ANN model for predicting the surface roughness performance measure in the Machining process by considering the Artificial Neural Network (ANN) as the essential technique for measuring surface roughness. A revision of several previous studies associated with the modelling issue is carried out to assess how capable ANN is as a technique to model the problem. Based on the studies conducted by previous researchers, the abilities and limitations of the ANN technique for predicting surface roughness are highlighted. Utilization of ANN-based modelling is also discussed to show the required basic elements for predicting surface roughness in the Milling process. In order to investigate how capable the ANN technique is at estimating the prediction value for surface roughness, a real Machining experiment is referred to in this study. In the experiment, 24 samples of data concerned with the Milling operation are collected based on eight samples of data of a two-level DOE 2^k full factorial analysis, four samples of centre data, and 12 samples of axial data. All data samples are tested in real Machining by using uncoated, TiAIN coated and SN"T"R coated cutting tools of titanium alloy (Ti-6A1-4V). The Matlab ANN toolbox is used for the modelling purpose with some justifications. Feedforward backpropagation is selected as the algorithm with traingdx, learngdx, MSE, logsig as the training, learning, performance and transfer functions, respectively. With three nodes in the input layer and one node in the output layer, eight networks are developed by using different numbers of nodes in the hidden layer which are 3-1-1, 3-3-1, 3-6-1, 3-7-1, 3-1-1-1, 3-3-3-1, 3-6-6-1 and 3-7-7-1 structures. It was found that the 3-1-1 network structure of the SN"T"R coated cutting tool gave the best ANN model in predicting the surface roughness value. This study concludes that the model for surface roughness in the Milling process could be improved by modifying the number of layers and nodes in the hidden layers of the ANN network structure, particularly for predicting the value of the surface roughness performance measure. As a result of the prediction, the recommended combination of cutting conditions to obtain the best surface roughness value is a high speed with a low feed rate and radial rake angle.

  • Genetic Algorithm for optimizing cutting conditions of uncoated carbide (WC-Co) in Milling Machining operation
    2009 Innovative Technologies in Intelligent Systems and Industrial Applications, 2009
    Co-Authors: Azlan Mohd Zain, Habibollah Haron, Safian Sharif
    Abstract:

    This paper presents the capability of Genetic Algorithm (GA) technique in obtaining the optimal Machining parameters for uncoated carbide (WC-Co) tool to minimize the surface roughness (R a ) value in Milling process. The optimal Machining parameters are generated using MATLAB Optimization toolbox. Regression technique is applied to create the surface roughness predicted equation to be taken as a fitness function of the GA. Result of this study indicated that the GA technique capable to estimate the optimal cutting conditions that yields to the minimum R a value. With high speed, low feed and high radial rake angle of the cutting conditions rate, GA technique recommended 0.17533µm as the best minimum predicted surface roughness value. Consequently, the GA technique has decreased the minimum surface roughness value of the experimental data by about 25.7 %.

M Sayuti - One of the best experts on this subject based on the ideXlab platform.

  • morphology of surface generated by end Milling al6061 t6 using molybdenum disulfide mos2 nanolubrication in end Milling Machining
    Journal of Cleaner Production, 2014
    Co-Authors: Bizhan Rahmati, Ahmed A D Sarhan, M Sayuti
    Abstract:

    Aluminum alloys are among the most significant metals in industries. The AL6061-T6 aluminum alloy is a highly prominent alloy due to its dominant mechanical properties, such as weldability, hardness and sustainability at high temperatures. AL6061-T6 is commonly used in heavy industries including aerospace, aircraft, automotive, food packaging, etc. Milling of AL6061-T6 is important, especially to create product shape varieties for different applications. However, the demand for high quality draws attention to product quality, particularly machined surface roughness, as it directly affects the product’s appearance, function and reliability. Applying correct lubrication to the Machining zone can enhance the tribological characteristics of AL6061-T6 alloy. For further improvement, introducing nanolubricant may yield superior product quality due to the rolling action of nanoparticles at the tool-workpiece interface, which significantly reduces cutting force. In this research work, a nanolubricant containing MoS2 nanoparticles is developed for end Milling of AL6061-T6 alloy and the surface morphology of the machined workpiece is investigated as well.

Ahmed A D Sarhan - One of the best experts on this subject based on the ideXlab platform.

  • morphology of surface generated by end Milling al6061 t6 using molybdenum disulfide mos2 nanolubrication in end Milling Machining
    Journal of Cleaner Production, 2014
    Co-Authors: Bizhan Rahmati, Ahmed A D Sarhan, M Sayuti
    Abstract:

    Aluminum alloys are among the most significant metals in industries. The AL6061-T6 aluminum alloy is a highly prominent alloy due to its dominant mechanical properties, such as weldability, hardness and sustainability at high temperatures. AL6061-T6 is commonly used in heavy industries including aerospace, aircraft, automotive, food packaging, etc. Milling of AL6061-T6 is important, especially to create product shape varieties for different applications. However, the demand for high quality draws attention to product quality, particularly machined surface roughness, as it directly affects the product’s appearance, function and reliability. Applying correct lubrication to the Machining zone can enhance the tribological characteristics of AL6061-T6 alloy. For further improvement, introducing nanolubricant may yield superior product quality due to the rolling action of nanoparticles at the tool-workpiece interface, which significantly reduces cutting force. In this research work, a nanolubricant containing MoS2 nanoparticles is developed for end Milling of AL6061-T6 alloy and the surface morphology of the machined workpiece is investigated as well.