This paper presents the fault diagnosis of face milling tool based on machine learning approach.While machining,spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are a...This paper presents the fault diagnosis of face milling tool based on machine learning approach.While machining,spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired.A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform(DWT)technique.The decision tree technique is used to select significant features out of all extracted wavelet features.C-support vector classification(C-SVC)andν-support vector classification(ν-SVC)models with different kernel functions of support vector machine(SVM)are used to study and classify the tool condition based on selected features.From the results obtained,C-SVC is the best model thanν-SVC and it can be able to give 94.5%classification accuracy for face milling of special steel alloy 42CrMo4.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
The present study explored the influence of axial gradation of viscoelastic materials on the dynamic response of the sandwich beam for structural applications.The finite element(FE)formulations are used to model and i...The present study explored the influence of axial gradation of viscoelastic materials on the dynamic response of the sandwich beam for structural applications.The finite element(FE)formulations are used to model and investigate dynamic response of the sandwich beam.The classical beam theory is used to develop the FE formulations and Lagrange's approach is considered to obtain the equations of motion(EOM).FE code is developed and validated with the existing literature and also conducted the convergence study for the developed FE method.Further,the influence of different viscoelastic materials and boundary conditions on the dynamic response of the sandwich beam is investigated.Four different axial gradation configurations of viscoelastic materials are considered for the present work to explore the influence on natural frequency,loss factor and frequency response of the sandwich beam.The modeled axial gradation of viscoelastic material has displayed a considerable impact on the peak vibrational amplitude response of the sandwich beam for all the boundary conditions and these configurations improved the damping capabilities at different configurations for the structural applications.展开更多
Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is v...Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.展开更多
Magnetorheological fluid(MRF)sandwich beams belong to a class of adaptive beams that consists of MRF sandwiched between two or more face layers and have a great prospective for use in semi-active control of beam vibra...Magnetorheological fluid(MRF)sandwich beams belong to a class of adaptive beams that consists of MRF sandwiched between two or more face layers and have a great prospective for use in semi-active control of beam vibrations due to their superior vibration suppression capabilities.The composition of MRF has a strong influence on the MRF properties and hence affects the vibration characteristics of the beam.In this work,six MRF samples(MRFs)composed of combination of two particle sizes and three weight fractions of carbonyl iron pow-der(CIP)were prepared and their viscoelastic properties were measured.The MRFs were used to fabricate different MRF core sandwich beams.Additionally,a sandwich beam with commer-cially available MRF 132DG fluid as core was fabricated.The modal parameters of the cantilever MRF sandwich beams were determined at different magnetic fields.Further,sinusoidal sweep excitation tests were performed on these beams at different magnetic fields to investigate their vibration suppres-sion behavior.MRF having larger particle size and higher weight fraction of CIP resulted in higher damping ratio and vibration suppression.Finally,optimal particle size and weight fraction of CIP were determined based on the maximization of damping ratio and minimization of weight of MRF.展开更多
文摘This paper presents the fault diagnosis of face milling tool based on machine learning approach.While machining,spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired.A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform(DWT)technique.The decision tree technique is used to select significant features out of all extracted wavelet features.C-support vector classification(C-SVC)andν-support vector classification(ν-SVC)models with different kernel functions of support vector machine(SVM)are used to study and classify the tool condition based on selected features.From the results obtained,C-SVC is the best model thanν-SVC and it can be able to give 94.5%classification accuracy for face milling of special steel alloy 42CrMo4.
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.
基金support from the Department of Science and Technology (DST)file no.ECR/2016/001448 titled“Experimental Investigation of Passive,Semi-active and Active vibration control of Composite Sandwich Structure”funded by Science and Engineering Research Board,Government of India。
文摘The present study explored the influence of axial gradation of viscoelastic materials on the dynamic response of the sandwich beam for structural applications.The finite element(FE)formulations are used to model and investigate dynamic response of the sandwich beam.The classical beam theory is used to develop the FE formulations and Lagrange's approach is considered to obtain the equations of motion(EOM).FE code is developed and validated with the existing literature and also conducted the convergence study for the developed FE method.Further,the influence of different viscoelastic materials and boundary conditions on the dynamic response of the sandwich beam is investigated.Four different axial gradation configurations of viscoelastic materials are considered for the present work to explore the influence on natural frequency,loss factor and frequency response of the sandwich beam.The modeled axial gradation of viscoelastic material has displayed a considerable impact on the peak vibrational amplitude response of the sandwich beam for all the boundary conditions and these configurations improved the damping capabilities at different configurations for the structural applications.
文摘Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.
基金This work was supported by the Ministry of Human Resource Development[IMPRINT/2016/7330]Ministry of Road Transport and Highways[IMPRINT/2016/7330].
文摘Magnetorheological fluid(MRF)sandwich beams belong to a class of adaptive beams that consists of MRF sandwiched between two or more face layers and have a great prospective for use in semi-active control of beam vibrations due to their superior vibration suppression capabilities.The composition of MRF has a strong influence on the MRF properties and hence affects the vibration characteristics of the beam.In this work,six MRF samples(MRFs)composed of combination of two particle sizes and three weight fractions of carbonyl iron pow-der(CIP)were prepared and their viscoelastic properties were measured.The MRFs were used to fabricate different MRF core sandwich beams.Additionally,a sandwich beam with commer-cially available MRF 132DG fluid as core was fabricated.The modal parameters of the cantilever MRF sandwich beams were determined at different magnetic fields.Further,sinusoidal sweep excitation tests were performed on these beams at different magnetic fields to investigate their vibration suppres-sion behavior.MRF having larger particle size and higher weight fraction of CIP resulted in higher damping ratio and vibration suppression.Finally,optimal particle size and weight fraction of CIP were determined based on the maximization of damping ratio and minimization of weight of MRF.
