Artificial neural network is a powerful technique of computational intelligence and has been applied in a variety of fields such as engineering and computer science. This paper deals with the neural network modeling a...Artificial neural network is a powerful technique of computational intelligence and has been applied in a variety of fields such as engineering and computer science. This paper deals with the neural network modeling and prediction of surface roughness in machining aluminum alloys using data collected from both force and vibration sensors. Two neural network models, including a Multi-Layer Perceptron (MLP) model and a Radial Basis Function (RBF) model, were developed in the present study. Each model includes eight inputs and five outputs. The eight inputs include the cutting speed, the ratio of the feed rate to the tool-edge radius, cutting forces in three directions, and cutting vibrations in three directions. The five outputs are five surface roughness parameters. Described in detail is how training and test data were generated from real-world machining experiments that covered a wide range of cutting conditions. The results show that the MLP model provides significantly higher accuracy of prediction for surface roughness than does the RBF model.展开更多
Aluminium metal matrix composites are finding increased applications in many areas. Adding of the third element to the metal matrix make the composite hybrid. This paper presents the study on the surface roughness cha...Aluminium metal matrix composites are finding increased applications in many areas. Adding of the third element to the metal matrix make the composite hybrid. This paper presents the study on the surface roughness characteristics of a hybrid aluminium metal matrix (Al6061-SiC-Al2O3) composites. The experimental studies were carried out on a lathe. The composites were prepared using the liquid metallurgy technique, in which 3, 6 and 9 wt % of particulates SiC and Al2O3 were dispersed in the base matrix. The obtained cast composites were carefully machined. The characteristics that influence the surface roughness such as feed rate, depth of cut and cutting speed were studied, which made the analysis come to a conclusion that the surface roughness is increases with the increase of feed rate and it reduces the surface roughness with the increase of cutting speed.展开更多
In recent years,there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields,and the demand for accurate machining of such composite materials has grown acco...In recent years,there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields,and the demand for accurate machining of such composite materials has grown accordingly.In this paper,a feed-forward multi-layered artificial neural network(ANN)roughness prediction model,using the Levenberg-Marquardt backpropagation training algorithm,is proposed to investigate the mathematical relationship between cutting parameters and average surface roughness during milling Al/SiC particulate composite materials.Milling experiments were conducted on a computer numerical control(C N C)milling machine with polycrystalline diamond(PCD)tools to acquire data for training the ANN roughness prediction model.Four cutting parameters were considered in these experiments:cutting speed,depth of cut,feed rate,and volume fraction of SiC.These parameters were also used as inputs for the ANN roughness prediction model.The output of the model was the average surface roughness of the machined workpiece.A successfully trained ANN roughness prediction model could predict the corresponding average surface roughness based on given cutting parameters,with a 2.08%mea n relative error.Moreover,a roughness control model that could accurately determine the corresponding cutting parameters for a specific desired roughness with a 2.91%mean relative error was developed based on the ANN roughness prediction model.Finally,a more reliable and readable analysis of the influence of each parameter on roughness or the interaction between different parameters was conducted with the help of the ANN prediction model.展开更多
An attempt was made to investigate the machinability of Si Cp/Al composites based on the experimental study using mill-grinding processing method. The experiments were carried out on a high-speed CNC machining center ...An attempt was made to investigate the machinability of Si Cp/Al composites based on the experimental study using mill-grinding processing method. The experiments were carried out on a high-speed CNC machining center using integrated abrasive cutting tool. The effects of combined machining parameters, e g, cutting speed（vs）, feed rate（vf）, and depth of cut（ap）, with the same change of material removal rate（MRR） on the mill-grinding force and surface roughness（Ra） were investigated. The formation mechanism of typical machined surface defects was analyzed by SEM. The experimental results reveal that with the same change of material removal rate, lower mill-grinding force values can be gained by increasing depth of cut and feed rate simultaneously at higher cutting speed. With the same change of MRR value, lower surface roughness values can be gained by increasing the feed rate at higher cutting speed, rather than just increasing the depth of cut, or increasing the feed rate and depth of cut simultaneously. The machined surface of Si Cp/Al composites reveals typical defects which can influence surface integrity.展开更多
文摘Artificial neural network is a powerful technique of computational intelligence and has been applied in a variety of fields such as engineering and computer science. This paper deals with the neural network modeling and prediction of surface roughness in machining aluminum alloys using data collected from both force and vibration sensors. Two neural network models, including a Multi-Layer Perceptron (MLP) model and a Radial Basis Function (RBF) model, were developed in the present study. Each model includes eight inputs and five outputs. The eight inputs include the cutting speed, the ratio of the feed rate to the tool-edge radius, cutting forces in three directions, and cutting vibrations in three directions. The five outputs are five surface roughness parameters. Described in detail is how training and test data were generated from real-world machining experiments that covered a wide range of cutting conditions. The results show that the MLP model provides significantly higher accuracy of prediction for surface roughness than does the RBF model.
文摘Aluminium metal matrix composites are finding increased applications in many areas. Adding of the third element to the metal matrix make the composite hybrid. This paper presents the study on the surface roughness characteristics of a hybrid aluminium metal matrix (Al6061-SiC-Al2O3) composites. The experimental studies were carried out on a lathe. The composites were prepared using the liquid metallurgy technique, in which 3, 6 and 9 wt % of particulates SiC and Al2O3 were dispersed in the base matrix. The obtained cast composites were carefully machined. The characteristics that influence the surface roughness such as feed rate, depth of cut and cutting speed were studied, which made the analysis come to a conclusion that the surface roughness is increases with the increase of feed rate and it reduces the surface roughness with the increase of cutting speed.
基金This work was supported by the National High Technology Research and Development Plan of China(Grant No.2015AA043505)the Equipment Advanced Research Funds(Grant No.61402100401)+1 种基金the Equipment Advanced Research Key Laboratory Funds(Grant No.6142804180106)Shenzhen Fundamental Research Funds(Grant No.JCYJ20180508151910775).
文摘In recent years,there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields,and the demand for accurate machining of such composite materials has grown accordingly.In this paper,a feed-forward multi-layered artificial neural network(ANN)roughness prediction model,using the Levenberg-Marquardt backpropagation training algorithm,is proposed to investigate the mathematical relationship between cutting parameters and average surface roughness during milling Al/SiC particulate composite materials.Milling experiments were conducted on a computer numerical control(C N C)milling machine with polycrystalline diamond(PCD)tools to acquire data for training the ANN roughness prediction model.Four cutting parameters were considered in these experiments:cutting speed,depth of cut,feed rate,and volume fraction of SiC.These parameters were also used as inputs for the ANN roughness prediction model.The output of the model was the average surface roughness of the machined workpiece.A successfully trained ANN roughness prediction model could predict the corresponding average surface roughness based on given cutting parameters,with a 2.08%mea n relative error.Moreover,a roughness control model that could accurately determine the corresponding cutting parameters for a specific desired roughness with a 2.91%mean relative error was developed based on the ANN roughness prediction model.Finally,a more reliable and readable analysis of the influence of each parameter on roughness or the interaction between different parameters was conducted with the help of the ANN prediction model.
基金Funded by the National Defense Basic Scientific ResearchAerospace Science and Technology Corporation Commonality Technology Research Project
文摘An attempt was made to investigate the machinability of Si Cp/Al composites based on the experimental study using mill-grinding processing method. The experiments were carried out on a high-speed CNC machining center using integrated abrasive cutting tool. The effects of combined machining parameters, e g, cutting speed（vs）, feed rate（vf）, and depth of cut（ap）, with the same change of material removal rate（MRR） on the mill-grinding force and surface roughness（Ra） were investigated. The formation mechanism of typical machined surface defects was analyzed by SEM. The experimental results reveal that with the same change of material removal rate, lower mill-grinding force values can be gained by increasing depth of cut and feed rate simultaneously at higher cutting speed. With the same change of MRR value, lower surface roughness values can be gained by increasing the feed rate at higher cutting speed, rather than just increasing the depth of cut, or increasing the feed rate and depth of cut simultaneously. The machined surface of Si Cp/Al composites reveals typical defects which can influence surface integrity.
