Magnesium alloy(AZ91D)composites reinforced with silicon carbide particle with different volume percentage were fabricated by two step stir casting process.The effect of changes in particle size and volume fraction of...Magnesium alloy(AZ91D)composites reinforced with silicon carbide particle with different volume percentage were fabricated by two step stir casting process.The effect of changes in particle size and volume fraction of SiC particles on physical and mechanical properties of composites were evaluated under as cast and heat treated(T6)conditions.The experimental results were compared with the standard theoretical models.The results reveal that the mechanical properties of composites increased with increasing SiC particles and decrease with increasing particle size.Distribution of particles and fractured surface were studied through SEM and the presence of elements is revealed by EDS study.展开更多
Surface metal matrix composite is produced on the as cast Magnesium Rare Earth alloy-RZ 5 by single pass friction stir processing using various micro/nano sized reinforcement particles namely Boron Carbide(B_(4)C),Mul...Surface metal matrix composite is produced on the as cast Magnesium Rare Earth alloy-RZ 5 by single pass friction stir processing using various micro/nano sized reinforcement particles namely Boron Carbide(B_(4)C),Multi Walled Carbon Nano Tubes(MWCNTs),and a mixture of ZrO_(2)+Al_(2)O_(3)particles.Fine grained metal matrix composites having the grain size ranging 0.8μm to 1.87μm are achieved.Grain boundary pinning by the reinforcement particles has resulted in the transformation of coarse grained(∼81μm)base material into fine grained(<1μm)metal matrix composite.Finer grain structure and the presence of reinforcements at the stir zone have resulted in increased and improved mechanical properties of the developed composites.Microhardness ranging between 125 HV and 403 HV is achieved.Uni-axial Tensile Testing of the developed composites exhibited improvement in tensile strength.Metal matrix composites developed using various reinforcements exhibited an increase in strength ranges between 250 MPa and 320 MPa.展开更多
The present study is to optimize the process parameters for friction welding of duplex stainless steel(DSS UNS S32205).Experiments were conducted according to central composite design.Process variables,as inputs of th...The present study is to optimize the process parameters for friction welding of duplex stainless steel(DSS UNS S32205).Experiments were conducted according to central composite design.Process variables,as inputs of the neural network,included friction pressure,upsetting pressure,speed and burn-off length.Tensile strength and microhardness were selected as the outputs of the neural networks.The weld metals had higher hardness and tensile strength than the base material due to grain refinement which caused failures away from the joint interface during tensile testing.Due to shorter heating time,no secondary phase intermetallic precipitation was observed in the weld joint.A multi-layer perceptron neural network was established for modeling purpose.Five various training algorithms,belonging to three classes,namely gradient descent,genetic algorithm and LevenbergeM arquardt,were used to train artificial neural network.The optimization was carried out by using particle swarm optimization method.Confirmation test was carried out by setting the optimized parameters.In conformation test,maximum tensile strength and maximum hardness obtained are 822 MPa and 322 Hv,respectively.The metallurgical investigations revealed that base metal,partially deformed zone and weld zone maintain austenite/ferrite proportion of 50:50.展开更多
The optimum friction welding （FW） parameters of duplex stainless steel （DSS） UNS $32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input par...The optimum friction welding （FW） parameters of duplex stainless steel （DSS） UNS $32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input parameters were friction pressure （F）, upset pressure （U）, speed （S） and burn-off length （B）, and responses were hardness and ultimate tensile strength. To achieve the quality of the welded joint, the ultimate tensile strength and hardness were maximized, and response surface methodology （RSM） was applied to create separate regression equations of tensile strength and hardness. Intelligent optimization technique such as genetic algorithm was used to predict the Pareto optimal solutions. Depending upon the application, preferred suitable welding parameters were selected. It was inferred that the changing hardness and tensile strength of the friction welded joint influenced the upset pressure, friction Pressure and speed of rotation.展开更多
基金This work was supported by Department of Science and Technology,Government of India,under Grant No:RP02197.
文摘Magnesium alloy(AZ91D)composites reinforced with silicon carbide particle with different volume percentage were fabricated by two step stir casting process.The effect of changes in particle size and volume fraction of SiC particles on physical and mechanical properties of composites were evaluated under as cast and heat treated(T6)conditions.The experimental results were compared with the standard theoretical models.The results reveal that the mechanical properties of composites increased with increasing SiC particles and decrease with increasing particle size.Distribution of particles and fractured surface were studied through SEM and the presence of elements is revealed by EDS study.
文摘Surface metal matrix composite is produced on the as cast Magnesium Rare Earth alloy-RZ 5 by single pass friction stir processing using various micro/nano sized reinforcement particles namely Boron Carbide(B_(4)C),Multi Walled Carbon Nano Tubes(MWCNTs),and a mixture of ZrO_(2)+Al_(2)O_(3)particles.Fine grained metal matrix composites having the grain size ranging 0.8μm to 1.87μm are achieved.Grain boundary pinning by the reinforcement particles has resulted in the transformation of coarse grained(∼81μm)base material into fine grained(<1μm)metal matrix composite.Finer grain structure and the presence of reinforcements at the stir zone have resulted in increased and improved mechanical properties of the developed composites.Microhardness ranging between 125 HV and 403 HV is achieved.Uni-axial Tensile Testing of the developed composites exhibited improvement in tensile strength.Metal matrix composites developed using various reinforcements exhibited an increase in strength ranges between 250 MPa and 320 MPa.
文摘The present study is to optimize the process parameters for friction welding of duplex stainless steel(DSS UNS S32205).Experiments were conducted according to central composite design.Process variables,as inputs of the neural network,included friction pressure,upsetting pressure,speed and burn-off length.Tensile strength and microhardness were selected as the outputs of the neural networks.The weld metals had higher hardness and tensile strength than the base material due to grain refinement which caused failures away from the joint interface during tensile testing.Due to shorter heating time,no secondary phase intermetallic precipitation was observed in the weld joint.A multi-layer perceptron neural network was established for modeling purpose.Five various training algorithms,belonging to three classes,namely gradient descent,genetic algorithm and LevenbergeM arquardt,were used to train artificial neural network.The optimization was carried out by using particle swarm optimization method.Confirmation test was carried out by setting the optimized parameters.In conformation test,maximum tensile strength and maximum hardness obtained are 822 MPa and 322 Hv,respectively.The metallurgical investigations revealed that base metal,partially deformed zone and weld zone maintain austenite/ferrite proportion of 50:50.
文摘The optimum friction welding （FW） parameters of duplex stainless steel （DSS） UNS $32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input parameters were friction pressure （F）, upset pressure （U）, speed （S） and burn-off length （B）, and responses were hardness and ultimate tensile strength. To achieve the quality of the welded joint, the ultimate tensile strength and hardness were maximized, and response surface methodology （RSM） was applied to create separate regression equations of tensile strength and hardness. Intelligent optimization technique such as genetic algorithm was used to predict the Pareto optimal solutions. Depending upon the application, preferred suitable welding parameters were selected. It was inferred that the changing hardness and tensile strength of the friction welded joint influenced the upset pressure, friction Pressure and speed of rotation.
