Distance Estimation and Material Classification of a Compliant Tactile Sensor Using Vibration Modes and Support Vector Machine

Many animals possess actively movable tactile sensors in their heads,to explore the near-range space.During locomotion,an antenna is used in near range orientation,for example,in detecting,localizing,probing,and negotiating obstacles.A bionic tactile sensor used in the present work was inspired by the antenna of the stick insects.The sensor is able to detect an obstacle and its location in 3 D(Three dimensional) space.The vibration signals are analyzed in the frequency domain using Fast Fourier Transform(FFT) to estimate the distances.Signal processing algorithms,Artificial Neural Network(ANN) and Support Vector Machine(SVM) are used for the analysis and prediction processes.These three prediction techniques are compared for both distance estimation and material classification processes.When estimating the distances,the accuracy of estimation is deteriorated towards the tip of the probe due to the change in the vibration modes.Since the vibration data within that region have high a variance,the accuracy in distance estimation and material classification are lower towards the tip.The change in vibration mode is mathematically analyzed and a solution is proposed to estimate the distance along the full range of the probe.

Research on Blind Source Separation for Machine Vibrations

Blind source separation is a signal processing method based on independent component analysis, its aim is to separate the source signals from a set of observations (output of sensors) by assuming the source signals independently. This paper reviews the general concept of BSS firstly;especially the theory for convolutive mixtures, the model of convolutive mixture and two deconvolution structures, then adopts a BSS algorithm for convolutive mixtures based on residual cross-talking error threshold control criteria, the simulation testing points out good performance for simulated mixtures.

Effect of vibration frequency on microstructure and performance of high chromium cast iron prepared by lost foam casting

In the present research, high chromium cast irons(HCCIs) were prepared using the lost foam casting(LFC) process. To improve the wear resistance of the high chromium cast irons(HCCIs), mechanical vibration was employed during the solidification of the HCCIs. The effects of vibration frequency on the microstructure and performance of the HCCIs under as-cast, as-quenched and as-tempered conditions were investigated. The results indicated that the microstructures of the LFC-produced HCCIs were refined due to the introduction of mechanical vibration, and the hardness was improved compared to that of the alloy without vibration. However, only a slight improvement in hardness was found in spite of the increase of vibration frequency. In contrast, the impact toughness of the as-tempered HCCIs increased with an increase in the vibration frequency. In addition, the wear resistance of the HCCIs was improved as a result of the introduction of vibration and increased with an increase in the vibration frequency.

Synchrophasing Vibration Control of Machines Supported by Discrete Isolators

This paper describes an analytical investigation into synchrophasing,a vibration control strategy on a machinery installation in which two rotational machines are attached to a beam-like raft by discrete resilient isolators.Forces and moments introduced by sources are considered,which effectively represent a practical engineering system.Adjusting the relative phase angle between the machines has been theoretically demonstrated to greatly reduce the cost function,which is defined as the sum of velocity squares of attaching points on the raft at each frequency of interest.The effect of the position of the machine is also investigated.Results show that altering the position of the secondary source may cause a slight change to the mode shape of the composite system and therefore change the optimum phase between the two machines.Although the analysis is based on a one-dimensional Euler– Bernoulli beam and each machine is considered as a rigid-body,a key principle can be derived from the results.However,the factors that can influence the synchrophasing control performance would become coupled and highly complicated.This condition has to be considered in practice.

Combined effects of ultrasonic vibration and FeCoNiCrCu coating on interfacial microstructure and mechanical properties of Al/Mg bimetal by compound casting

In this work,a new treatment method combining ultrasonic vibration with FeCoNiCrCu high entropy alloy(HEA)coating was used to prepared Al/Mg bimetal through the lost foam compound casting.The effects of composite treatment involving ultrasonic vibration and HEA coating on interfacial microstructure and mechanical properties of Al/Mg bimetal were studied.Results demonstrate that the interface thickness of the Al/Mg bimetal with composite treatment significantly decreases to only 26.99%of the thickness observed in the untreated Al/Mg bimetal.The HEA coating hinders the diffusion between Al and Mg,resulting the significant reduction in Al/Mg intermetallic compounds in the interface.The Al/Mg bimetal interface with composite treatment is composed of Al_(3)Mg_(2)and Mg_(2)Si/AlxFeCoNiCrCu+FeCoNiCrCu/δ-Mg+Al_(12)Mg_(17)eutectic structures.The interface resulting from the composite treatment has a lower hardness than that without treatment.The acoustic cavitation and acoustic streaming effects generated by ultrasonic vibration promote the diffusion of Al elements within the HEA coating,resulting in a significant improvement in the metallurgical bonding quality on the Mg side.The fracture position shifts from the Mg side of the Al/Mg bimetal only with HEA coating to the Al side with composite treatment.The shear strength of the Al/Mg bimetal increases from 32.16 MPa without treatment to 63.44 MPa with ultrasonic vibration and HEA coating,increasing by 97.26%.

A Comprehensive 3-Steps Methodology for Vibration-Based Fault Detection,Diagnosis and Localization in Rotating Machines

In any industry,it is the requirement to know whether the machine is healthy or not to operate machine further.If the machine is not healthy then what is the fault in the machine and then finally its location.The paper is proposing a 3-Steps methodology for the machine fault diagnosis to meet the industrial requirements to aid the maintenance activity.The Step-1 identifies whether machine is healthy or faulty,then Step-2 detect the type of defect and finally its location in Step-3.This method is extended further from the earlier study on the 2-Steps method for the rotor defects only to the 3-Steps methodology to both rotor and bearing defects.The method uses the optimised vibration parameters and a simple Artificial Neural Network(ANN)-based Machine Learning(ML)model from the earlier studies.The model is initially developed,tested and validated on an experimental rotating rig operating at a speed above 1st critical speed.The proposed method and model are then further validated at 2 different operating speeds,one below 1st critical speed and other above 2nd critical speed.The machine dynamics are expected to be significantly different at these speeds.This highlights the robustness of the proposed 3-Steps method.

Active vibration control of spindle in ultra-precision turning machine

In order to minimize vibration and improve rotary precision of spindle, we apply active vibration control technique to ultra-precision turning machine based on the analysis of vibration characteristic of aerostatic bearing spindle. Using aerostatic bearing itself as actuator, the vibration of spindle is controlled by adjusting admission pressure respectively and by changing pressure distribution in the bearing. The experiments and simulations prove that this method can minimize the vibration of spindle effectively.

Microstructural characteristics of Al-20Si-2Cu-0.4Mg-1Ni alloy formed by rheo-squeeze casting after ultrasonic vibration treatment

A swash plate for air conditioning compressor of cars was formed by rheo-squeeze casting with semi-solid Al-Si alloy slurry prepared by ultrasonic vibration process, and the microstructure of this alloy was investigated. Besides the microstructures of primary Si particles and α(Al)+β-Si eutectic phases, non-equilibrium α(Al) particles or dendrites are discovered in the microstructure of the Al-20Si-2Cu-0.4Mg-1Ni alloy. Rapid cooling generated by squeeze casting process rather than the pressure is considered as the main reason for the formation of non-equilibrium α(Al) phase. The sound pressurizing effect of ultrasonic vibration also enables the non-equilibrium α(Al) phases to form above eutectic temperature and grow into non-dendritic spheroids in the process of semi-solid slurry preparation. Non-equilibrium α(Al) phases formed in the hypereutectic Al-Si alloy with ultrasonic vibration treatment, consist of round α(Al) grains formed above the eutectic temperature and a small amount of fine α(Al) dendrites formed under the eutectic temperature. The volume fraction of primary Si particles is decreased significantly by the effect of ultrasonic vibration through increasing the solid solubility of Si atoms in α(Al) matrix and decreasing the forming temperature range of primary Si particles. The average particle diameter and the volume fraction of primary Si particles in microstructure of the swash-plate by rheo-squeeze casting are 24.3 μm and 11.1%, respectively.

Vibration properties of Paulownia wood for Ruan sound quality using machine learning methods

As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.

Classifying Vibration Modes Generated by The Michelson Interferometer Using Machine Learning Methods

In this paper, we explore the classification of vibration modes generated by handwriting on an optical desk using deep learning architectures. Three deep learning models—Long Short-Term Memory (LSTM) networks with attention mechanism, Video Vision Transformer (ViViT), and Long-term Recurrent Convolutional Network (LRCN)—were evaluated to determine the most effective method for analyzing time series patterns generated by a Michelson interferometer. The interferometer was used to detect vibration modes created by handwriting, capturing time-series data from the diffraction patterns. Among these models, the LSTM-Attention network achieved the highest validation accuracy, reaching up to 92%, outperforming both ViViT and LRCN. These findings highlight the potential of deep learning in material science for detecting and classifying vibration patterns. The powerful performance of the LSTM-Attention model suggests that it could be applied to similar classification tasks in related fields.

Tunnel boring machine vibration-based deep learning for the ground identification of working faces

Tunnel boring machine(TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself.In this study,deep recurrent neural networks(RNNs) and convolutional neural networks(CNNs) were used for vibration-based working face ground identification.First,field monitoring was conducted to obtain the TBM vibration data when tunneling in changing geological conditions,including mixed-face,homogeneous,and transmission ground.Next,RNNs and CNNs were utilized to develop vibration-based prediction models,which were then validated using the testing dataset.The accuracy of the long short-term memory(LSTM) and bidirectional LSTM(Bi-LSTM) models was approximately 70% with raw data;however,with instantaneous frequency transmission,the accuracy increased to approximately 80%.Two types of deep CNNs,GoogLeNet and ResNet,were trained and tested with time-frequency scalar diagrams from continuous wavelet transformation.The CNN models,with an accuracy greater than 96%,performed significantly better than the RNN models.The ResNet-18,with an accuracy of 98.28%,performed the best.When the sample length was set as the cutterhead rotation period,the deep CNN and RNN models achieved the highest accuracy while the proposed deep CNN model simultaneously achieved high prediction accuracy and feedback efficiency.The proposed model could promptly identify the ground conditions at the working face without stopping the normal tunneling process,and the TBM working parameters could be adjusted and optimized in a timely manner based on the predicted results.

Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure

Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm implemented.In this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer.The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process.The LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features.The proposed model can be used for monitoring the automobile tyre pressure successfully.

Vibrations induced by tunnel boring machine in urban areas: In situ measurements and methodology of analysis

Excavation with tunnel boring machine(TBM)can generate vibrations,causing damages to neighbouring buildings and disturbing the residents or the equipment.This problem is particularly challenging in urban areas,where TBMs are increasingly large in diameter and shallow in depth.In response to this problem,four experimental campaigns were carried out in different geotechnical contexts in France.The vibration measurements were acquired on the surface and inside the TBMs.These measurements are also complemented by few data in the literature.An original methodology of signal processing is pro-posed to characterize the amplitude of the particle velocities,as well as the frequency content of the signals to highlight the most energetic bands.The levels of vibrations are also compared with the thresholds existing in various European regulations concerning the impact on neighbouring structures and the disturbance to local residents.

High Speed of Tufting Machine Based on Transfer Matrix Method

In order to achieve high speed of tufting machine,the oval gear is used as the transmission mechanism between the main shaft and the slave shaft. And then,mathematical model of tufting machine spindle system is established by transfer matrix method.Finally,the dynamic reaction force of the connection and the unbalanced response of the joint between the general tufting machine and the improved spindle system are studied by using Matlab numerical simulation. The analysis results showthat when the spindle speed reaches 1 000 r/min, the dynamic reaction force of the improved spindle system at the joint is far less than that of the general tufting machine,and the unbalanced response is reduced from 0. 22 mm to 0. 10 mm.

Vibration test of micro machined gyroscope based on high speed photography and SURF

Based on three kinds of dynamic test of MEMS, a dynamic system for the vibration test of micro machined gyroscope based on high speed photography is introduced. Firstly, the architecture of the system hardware is introduced. Secondly, the image tracking performance is compared by the test using the template matching algorithm, the mean shift algorithm and the SURF algorithm. The vibration curve shows that high speed photograph combined with SURF algorithm is faster, more ac- curate, and more suitable for the vibration test of micro machined gyroscope. After the frequency a- nalysis and related interpolation, more characteristics of micro gyroscope can be obtained.

Effects of mechanical vibration on filling and solidification behavior, microstructure and performance of Al/Mg bimetal by lost foam compound casting

Al/Mg bimetal was prepared by lost foam solid-liquid compound casting,and the effects of mechanical vibration on the filling and solidification behavior,microstructure and performance of the bimetal were investigated.Results show that the mechanical vibration has a remarkable influence on the filling and solidification processes.It is found that after mechanical vibration,the filling rate increases and the filling rate at different times is more uniform than that without vibration.In addition,the mechanical vibration also increases the wettability between liquid AZ91D and A356 inlays.The mechanical vibration reduces the horizontal and vertical temperature gradient of the casting and makes the temperature distribution of the whole casting more uniform.Compared to the Al/Mg bimetal without vibration,the shear strength is improved by 39.76%after the mechanical vibration is applied,due to the decrease of the inclusions and Al_(12)Mg_(17) dendrites,and the refinement and uniform distribution of the Mg_(2)Si particles in the interface of the Al/Mg bimetal.

THE DESIGN AND ANALYSIS OF VIBRATION STRUCTURE OF VERTICAL DYNAMIC BALANCING MACHINE

A new type of vibration structure (i.e. supporting system, called swing frame cus- tomarily) of vertical dynamic balancing machine has been designed, which is based on an analysis for the swing frame of a traditional double-plane vertical dynamic balancing machine. The static unbalance and couple unbalance can be e?ectively separated by using the new dynamic balancing machine with the new swing frame. By building the dynamics model, the advantages of the new structure are discussed in detail. The modal and harmonic response are analyzed by using the ANSYS7.0. By comparing the ?nite element modal analysis with the experimental modal analy- sis, the natural frequencies and vibration modes are found. There are many spring boards in the new swing frame. Their sti?nesses are di?erent and assorted with each other. Furthermore, there are three sensors on the measuring points. Therefore, the new dynamic balancing machine can measure static unbalance and coupling unbalance directly, and the interaction between them is faint. The result shows that the new vertical dynamic balancing machine is suitable for inertial measurement of ?ying objects, and can overcome the shortcomings of traditional double-plane vertical dynamic balancing machines, which the e?ect of plane-separation is inferior. The vertical dynamic balancing machine with the new vibration structure can ?nd wide application in the future. The modelling and analysis of the new vibration structure will provide theoretical basis and practical experience for designing new-type vertical dynamic balancing machines.

Microstructural analysis of EN-GJS-450-10 ductile cast iron via vibrational casting

EN-GJS-450-10 ductile cast iron was produced with and without vibration to evaluate microstructural features. To investigate the effect of vibration, a reference, and two different castings having amplitudes of 0.9 mm and 1.8 mm were cast with a fixed vibration frequency of 50 Hz. The nodule count (density), form (type), size distribution, nodularity, and the fraction of graphite, percentages of both ferrite and pearlite phases, length of ferrite shell, and pore, were evaluated via optical microscopy using an image analysis software. It is observed that the microstructure of the cast iron is more uniform by vibrational casting than that by non-vibrational casting. Additionally, mechanical vibration enhances nodule count and nodularity, also, more ferritic matrix could be obtained after the application of vibration. Nodule count and nodularity of vibrational casting with 1.8 mm amplitude increased from 226 nodule per mm2 and 80% to 311 nodule per mm2 and 86.5% of non-vibrational casting. Percentages of ferrite and graphite area dramatically improved from 24% and 16.5% for non-vibrational casting to 57% and 22.3% for vibrational casting with 1.8 mm amplitude, whereas the percentages of pearlite and pores decreased significantly from 56.1% and 5% to 20% and 1%, respectively.

Feature-Based Vibration Monitoring of a Hydraulic Brake System Using Machine Learning

Hydraulic brakes in automobiles are an important control component used not only for the safety of the passenger but also for others moving on the road.Therefore,monitoring the condition of the brake components is inevitable.The brake elements can be monitored by studying the vibration characteristics obtained from the brake system using a proper signal processing technique through machine learning approaches.The vibration signals were captured using an accelerometer sensor under a various fault condition.The acquired vibration signals were processed for extracting meaningful information as features.The condition of the brake system can be predicted using a feature based machine learning approach through the extracted features.This study focuses on a mechatronics system for data acquisitions and a signal processing technique for extracting features such as statistical,histogram and wavelets.Comparative results have been carried out using an experimental study for finding the effectiveness of the suggested signal processing techniques for monitoring the condition of the brake system.

Effect of vibration frequency on primary phase and properties of grey cast iron fabricated by lost foam casting

The properties of gray cast iron(GCI)are affected by density of matrix,size of flake graphite and primary austenite.In this paper,the Y-type specimen of GCI was prepared by lost foam casting(LFC)with and without vibration,and the influence of vibration frequency on the density of matrix,size of primary phase,and properties of the GCI was studied.The results show that the length of the flake graphite and the size of the primary austenite in GCI firstly decrease and then increase with the increase of the vibration frequency.With a vibration frequency of 35 Hz,the length of the flake graphite is the shortest,the primary austenite is the finest and the density of the matrix is the highest.In addition,the tensile strength,elongation and hardness of the GCI firstly increase and then decrease with the increase of the vibration frequency,due to the refinement of the primary phase and the increase of the matrix density.In order to analyze the refinement mechanism of the primary phase of the GCI fabricated by the LFC with vibration,the solidification temperature fields of the GCI fabricated by the LFC with the vibration frequency of 0 and 35 Hz were measured.The results show that the vibration reduces the eutectic point of the GCI and increases the supercooling degree during the eutectic transformation.As a result,the length of the flake graphite and the size of the primary austenite in GCI fabricated by LFC with the vibration frequency of 35 Hz decrease.