The hot deformation behaviour of 7075 aluminium alloy reinforced with 10%of SiC particles was studied by employing both"processing maps"and microstructural observations.The composite was characterized by emp...The hot deformation behaviour of 7075 aluminium alloy reinforced with 10%of SiC particles was studied by employing both"processing maps"and microstructural observations.The composite was characterized by employing optical microscope to evaluate the microstructural transformations and instability phenomena.The material investigated was deformed by compression in the temperature and strain rate ranges of 300-500℃and 0.001-1.0 s-1,respectively.The deformation efficiency was calculated by strain rate sensitivity(m)values obtained by hot compression tests.The power dissipation efficiency and instability parameters were evaluated and processing maps were constructed for strain of 0.5.The optimum domains and instability zone were obtained for the composites.The optimum processing conditions are obtained in the strain rate range of 0.1-0.9 s-1and temperature range of 390-440 ℃with the efficiency of 30%.展开更多
The mechanical behavior of 2124 Al alloy produced by powder metallurgy was investigated with compression test at different temperatures and strain rates. The tests were performed in the temperature range of 300℃～500...The mechanical behavior of 2124 Al alloy produced by powder metallurgy was investigated with compression test at different temperatures and strain rates. The tests were performed in the temperature range of 300℃～500℃ and at strain rates from 0.001 s^-1 to 1.0 s^-1. The compression flow curves exhibited an initial sharp increase with strain, followed by monotonous hardening. The maximum stress decreased with decreasing strain rate and increasing temperature. The hot deformation characteristics of the material were studied using processing maps. The domain of safety and unsafe regime were identified and validated through microstructural examination.展开更多
Deep drawing is one of the most important processes for forming sheet metal parts.It is widely used for mass production of cup shapes in automobile,aerospace and packaging industries.Cup drawing,besides its importance...Deep drawing is one of the most important processes for forming sheet metal parts.It is widely used for mass production of cup shapes in automobile,aerospace and packaging industries.Cup drawing,besides its importance as forming process,also serves as a basic test for the sheet metal formability.The effect of equipment and tooling parameters results in complex deformation mechanism.Existence of thickness variation in the formed part may cause stress concentration and may lead to acceleration of damage.Using TAGUCHI's signal-to-noise ratio,it is determined that the die shoulder radius has major influence followed by blank holder force and punch nose radius on the thickness distribution of the deep drawn cup of AA 6061 sheet.The optimum levels of the above three factors,for the most even wall thickness distribution,are found to be punch nose radius of 3 mm,die shoulder radius of 8 mm and blank holder force of 4 kN.展开更多
The present work is focused on optimization of machining characteristics of AI/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such ...The present work is focused on optimization of machining characteristics of AI/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such as volume fraction of SiC, cutting speed and feed rate were considered. Artificial neural networks (ANN) was used to train and simulate the experimental data. Genetic algorithms (GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics. Validation of optimized results was also performed by confirmation experiments.展开更多
The present investigation focuses on the parametric influence of machining parameters on the surface finish obtained in turning of glass fiber reinforced polymer （GFRP） composites. The experiments were conducted bas...The present investigation focuses on the parametric influence of machining parameters on the surface finish obtained in turning of glass fiber reinforced polymer （GFRP） composites. The experiments were conducted based on Taguchi＇s experimental design technique. Response surface methodology and analysis of variance （ANOVA） were used to evaluate the composite machining process to perform the optimization. The results revealed that the feed rate was main influencing parameter on the surface roughness. The surface roughness increased with increasing the feed rate but decreased with increasing the cutting speed. Among the other parameters, depth of cut was more insensitive. The predicted values and measured values were fairly close to each other, which indicates that the developed model can be effectively used to predict the surface roughness on the machining of GFRP composites with 95% confidence intervals. Using such model could remarkablely save the time and cost.展开更多
Electrochemical machining（ECM） is one of the important non-traditional machining processes,which is used for machining of difficult-to-machine materials and intricate profiles.Being a complex process,it is very diff...Electrochemical machining（ECM） is one of the important non-traditional machining processes,which is used for machining of difficult-to-machine materials and intricate profiles.Being a complex process,it is very difficult to determine optimal parameters for improving cutting performance.Metal removal rate and surface roughness are the most important output parameters,which decide the cutting performance.There is no single optimal combination of cutting parameters,as their influences on the metal removal rate and the surface roughness are quite opposite.A multiple regression model was used to represent relationship between input and output variables and a multi-objective optimization method based on a non-dominated sorting genetic algorithm-Ⅱ（NSGA-Ⅱ） was used to optimize ECM process.A non-dominated solution set was obtained.展开更多
Digitization has created an abundance of new information sources by altering how pictures are captured.Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the con...Digitization has created an abundance of new information sources by altering how pictures are captured.Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing.This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets.Image retrieval usually encounters difficulties like a)merging the diverse representations of images and their Indexing,b)the low-level visual characters and semantic characters associated with an image are indirectly proportional,and c)noisy and less accurate extraction of image information(semantic and predicted attributes).This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively.Thus,retrieval becomes straightforward and rapid.This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost.We focus on the schema design on a non-clustered index solution,especially cover queries.This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing.Finally,we include non-key columns in addition to the key columns.Experiments on two image data sets‘with and without’filtered indexing show low query cost.We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing.The results show that retrieval by using deep root indexing is simple and fast.展开更多
In recent years, glass fiber reinforced plastics (GFRP) are being extensively used in variety of engineering applications in many different fields such as aerospace, oil, gas and process industries. However, the user... In recent years, glass fiber reinforced plastics (GFRP) are being extensively used in variety of engineering applications in many different fields such as aerospace, oil, gas and process industries. However, the users of FRP are facing difficulties to machine it, because of fiber delamination, fiber pull out, short tool life, matrix debonding, burning and formation of powder like chips. The present investigation focuses on the optimization of machining parameters for surface roughness of glass fiber reinforced plastics (GFRP) using design of experiments (DoE). The machining parameters considered were speed, feed, depth of cut and workpiece (fiber orientation). An attempt was made to analyse the influence of factors and their interactions during machining. The results of the present study gives the optimal combination of machining parameters and this will help to improve the machining requirements of GFRP composites.展开更多
文摘The hot deformation behaviour of 7075 aluminium alloy reinforced with 10%of SiC particles was studied by employing both"processing maps"and microstructural observations.The composite was characterized by employing optical microscope to evaluate the microstructural transformations and instability phenomena.The material investigated was deformed by compression in the temperature and strain rate ranges of 300-500℃and 0.001-1.0 s-1,respectively.The deformation efficiency was calculated by strain rate sensitivity(m)values obtained by hot compression tests.The power dissipation efficiency and instability parameters were evaluated and processing maps were constructed for strain of 0.5.The optimum domains and instability zone were obtained for the composites.The optimum processing conditions are obtained in the strain rate range of 0.1-0.9 s-1and temperature range of 390-440 ℃with the efficiency of 30%.
文摘The mechanical behavior of 2124 Al alloy produced by powder metallurgy was investigated with compression test at different temperatures and strain rates. The tests were performed in the temperature range of 300℃～500℃ and at strain rates from 0.001 s^-1 to 1.0 s^-1. The compression flow curves exhibited an initial sharp increase with strain, followed by monotonous hardening. The maximum stress decreased with decreasing strain rate and increasing temperature. The hot deformation characteristics of the material were studied using processing maps. The domain of safety and unsafe regime were identified and validated through microstructural examination.
文摘Deep drawing is one of the most important processes for forming sheet metal parts.It is widely used for mass production of cup shapes in automobile,aerospace and packaging industries.Cup drawing,besides its importance as forming process,also serves as a basic test for the sheet metal formability.The effect of equipment and tooling parameters results in complex deformation mechanism.Existence of thickness variation in the formed part may cause stress concentration and may lead to acceleration of damage.Using TAGUCHI's signal-to-noise ratio,it is determined that the die shoulder radius has major influence followed by blank holder force and punch nose radius on the thickness distribution of the deep drawn cup of AA 6061 sheet.The optimum levels of the above three factors,for the most even wall thickness distribution,are found to be punch nose radius of 3 mm,die shoulder radius of 8 mm and blank holder force of 4 kN.
文摘The present work is focused on optimization of machining characteristics of AI/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such as volume fraction of SiC, cutting speed and feed rate were considered. Artificial neural networks (ANN) was used to train and simulate the experimental data. Genetic algorithms (GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics. Validation of optimized results was also performed by confirmation experiments.
文摘The present investigation focuses on the parametric influence of machining parameters on the surface finish obtained in turning of glass fiber reinforced polymer （GFRP） composites. The experiments were conducted based on Taguchi＇s experimental design technique. Response surface methodology and analysis of variance （ANOVA） were used to evaluate the composite machining process to perform the optimization. The results revealed that the feed rate was main influencing parameter on the surface roughness. The surface roughness increased with increasing the feed rate but decreased with increasing the cutting speed. Among the other parameters, depth of cut was more insensitive. The predicted values and measured values were fairly close to each other, which indicates that the developed model can be effectively used to predict the surface roughness on the machining of GFRP composites with 95% confidence intervals. Using such model could remarkablely save the time and cost.
文摘Electrochemical machining（ECM） is one of the important non-traditional machining processes,which is used for machining of difficult-to-machine materials and intricate profiles.Being a complex process,it is very difficult to determine optimal parameters for improving cutting performance.Metal removal rate and surface roughness are the most important output parameters,which decide the cutting performance.There is no single optimal combination of cutting parameters,as their influences on the metal removal rate and the surface roughness are quite opposite.A multiple regression model was used to represent relationship between input and output variables and a multi-objective optimization method based on a non-dominated sorting genetic algorithm-Ⅱ（NSGA-Ⅱ） was used to optimize ECM process.A non-dominated solution set was obtained.
文摘Digitization has created an abundance of new information sources by altering how pictures are captured.Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing.This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets.Image retrieval usually encounters difficulties like a)merging the diverse representations of images and their Indexing,b)the low-level visual characters and semantic characters associated with an image are indirectly proportional,and c)noisy and less accurate extraction of image information(semantic and predicted attributes).This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively.Thus,retrieval becomes straightforward and rapid.This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost.We focus on the schema design on a non-clustered index solution,especially cover queries.This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing.Finally,we include non-key columns in addition to the key columns.Experiments on two image data sets‘with and without’filtered indexing show low query cost.We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing.The results show that retrieval by using deep root indexing is simple and fast.
文摘 In recent years, glass fiber reinforced plastics (GFRP) are being extensively used in variety of engineering applications in many different fields such as aerospace, oil, gas and process industries. However, the users of FRP are facing difficulties to machine it, because of fiber delamination, fiber pull out, short tool life, matrix debonding, burning and formation of powder like chips. The present investigation focuses on the optimization of machining parameters for surface roughness of glass fiber reinforced plastics (GFRP) using design of experiments (DoE). The machining parameters considered were speed, feed, depth of cut and workpiece (fiber orientation). An attempt was made to analyse the influence of factors and their interactions during machining. The results of the present study gives the optimal combination of machining parameters and this will help to improve the machining requirements of GFRP composites.
