AZ31 wrought magnesium alloys are light weight materials which play an important role in order to reduces the environmental burdens in modern society because of its high strength to weight ratio,corrosion resistance,a...AZ31 wrought magnesium alloys are light weight materials which play an important role in order to reduces the environmental burdens in modern society because of its high strength to weight ratio,corrosion resistance,and stiffness and machinability.Applications of this material are mainly in structural component i.e.,in constructions,automobile,aerospace,electronics and marine industries.In the present work,the microstructure characterization of the AZ31 alloys up to four ECAP passes at temperature of 573 K was observed for route Bc.Average grain size of the material was reduced from 31.8μm to 8μm after four ECAP passes.Mechanical properties of the alloy improved with increase in number of ECAP passes.Moreover,X-ray diffraction analysis was carried out for as received and ECAP processed material.展开更多
Effect of multi-directional forging(MDF)on wear properties of Mg-Zn alloys(with 2,4,and 6wt%Zn)is investigated.Dry sliding wear test was performed using pin on disk machine on MDF processed and homogenized samples.Wea...Effect of multi-directional forging(MDF)on wear properties of Mg-Zn alloys(with 2,4,and 6wt%Zn)is investigated.Dry sliding wear test was performed using pin on disk machine on MDF processed and homogenized samples.Wear behavior of samples was analyzed at loads of ION and 20 N,with sliding distances of 2000m and 4000m,at a sliding velocity of 3m/s.Microstructures of worn samples were observed under scanning electron microscopy(SEM),energy dispersive spectroscopy(EDS),and x-ray diffraction(XRD)and the results were analyzed.Mechanical properties were evaluated using microhardness test.After 5 passes of MDF,the average grain size was found to be 30±4p m,22±3 pm,and 18±3 pm,in Mg-2%Zn,Mg-4%Zn,and Mg-6%Zn alloys,respectively,with significant improvement in hardness in all cases.Wear resistance was improved after MDF processing,as well as,with increment in Zn content in Mg alloy.However,it decreased when the load and the sliding distance increased.Worn surface exhibited ploughing,delamination,plastic deformation,and wear debris along sliding direction,and abrasive wear was found to be the main mechanism.展开更多
The state of cutting tool determines the quality of surface produced on the machined parts.A faulty tool produces poor sur face,inaccurate geometry and non-economic production.Thus,it is necessary to monitor tool cond...The state of cutting tool determines the quality of surface produced on the machined parts.A faulty tool produces poor sur face,inaccurate geometry and non-economic production.Thus,it is necessary to monitor tool condition for a.machining process to have superior quality and economic production.In the pre-sent study,fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique.Cutting force and vibration signals were acquired to monitor tool condition during machining.A set of four tooling conditions namely healthy,worn flank,broken insert and extended tool overhang have been considered for the study.The machine learning technique was applied to both vibration and cutting force signals.Discrete wavelet features of the signals have been extracted using discrete wavelet trans formation(DWT).This transformation represents a large dataset into approximation coeffcients which contain the most useful information of the dataset.Significant features,among features extracted,were selected using J48 decision tree technique.Clas-sification of tool conditions was carried out us ing Naive Bayes algorithm.A 10 fold cross validation was incorporated to test the validity of classifier.A comparison of performance of classifier was made between cutting force and vibration signal to choose the best signal acquisition method in classifying tool fault conditions using machine learning technique.展开更多
文摘AZ31 wrought magnesium alloys are light weight materials which play an important role in order to reduces the environmental burdens in modern society because of its high strength to weight ratio,corrosion resistance,and stiffness and machinability.Applications of this material are mainly in structural component i.e.,in constructions,automobile,aerospace,electronics and marine industries.In the present work,the microstructure characterization of the AZ31 alloys up to four ECAP passes at temperature of 573 K was observed for route Bc.Average grain size of the material was reduced from 31.8μm to 8μm after four ECAP passes.Mechanical properties of the alloy improved with increase in number of ECAP passes.Moreover,X-ray diffraction analysis was carried out for as received and ECAP processed material.
文摘Effect of multi-directional forging(MDF)on wear properties of Mg-Zn alloys(with 2,4,and 6wt%Zn)is investigated.Dry sliding wear test was performed using pin on disk machine on MDF processed and homogenized samples.Wear behavior of samples was analyzed at loads of ION and 20 N,with sliding distances of 2000m and 4000m,at a sliding velocity of 3m/s.Microstructures of worn samples were observed under scanning electron microscopy(SEM),energy dispersive spectroscopy(EDS),and x-ray diffraction(XRD)and the results were analyzed.Mechanical properties were evaluated using microhardness test.After 5 passes of MDF,the average grain size was found to be 30±4p m,22±3 pm,and 18±3 pm,in Mg-2%Zn,Mg-4%Zn,and Mg-6%Zn alloys,respectively,with significant improvement in hardness in all cases.Wear resistance was improved after MDF processing,as well as,with increment in Zn content in Mg alloy.However,it decreased when the load and the sliding distance increased.Worn surface exhibited ploughing,delamination,plastic deformation,and wear debris along sliding direction,and abrasive wear was found to be the main mechanism.
文摘The state of cutting tool determines the quality of surface produced on the machined parts.A faulty tool produces poor sur face,inaccurate geometry and non-economic production.Thus,it is necessary to monitor tool condition for a.machining process to have superior quality and economic production.In the pre-sent study,fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique.Cutting force and vibration signals were acquired to monitor tool condition during machining.A set of four tooling conditions namely healthy,worn flank,broken insert and extended tool overhang have been considered for the study.The machine learning technique was applied to both vibration and cutting force signals.Discrete wavelet features of the signals have been extracted using discrete wavelet trans formation(DWT).This transformation represents a large dataset into approximation coeffcients which contain the most useful information of the dataset.Significant features,among features extracted,were selected using J48 decision tree technique.Clas-sification of tool conditions was carried out us ing Naive Bayes algorithm.A 10 fold cross validation was incorporated to test the validity of classifier.A comparison of performance of classifier was made between cutting force and vibration signal to choose the best signal acquisition method in classifying tool fault conditions using machine learning technique.
