Improving Ultrasonic Testing by Using Machine Learning Framework Based on Model Interpretation Strategy

Ultrasonic testing(UT)is increasingly combined with machine learning(ML)techniques for intelligently identifying damage.Extracting signifcant features from UT data is essential for efcient defect characterization.Moreover,the hidden physics behind ML is unexplained,reducing the generalization capability and versatility of ML methods in UT.In this paper,a generally applicable ML framework based on the model interpretation strategy is proposed to improve the detection accuracy and computational efciency of UT.Firstly,multi-domain features are extracted from the UT signals with signal processing techniques to construct an initial feature space.Subsequently,a feature selection method based on model interpretable strategy(FS-MIS)is innovatively developed by integrating Shapley additive explanation(SHAP),flter method,embedded method and wrapper method.The most efective ML model and the optimal feature subset with better correlation to the target defects are determined self-adaptively.The proposed framework is validated by identifying and locating side-drilled holes(SDHs)with 0.5λcentral distance and different depths.An ultrasonic array probe is adopted to acquire FMC datasets from several aluminum alloy specimens containing two SDHs by experiments.The optimal feature subset selected by FS-MIS is set as the input of the chosen ML model to train and predict the times of arrival(ToAs)of the scattered waves emitted by adjacent SDHs.The experimental results demonstrate that the relative errors of the predicted ToAs are all below 3.67%with an average error of 0.25%,signifcantly improving the time resolution of UT signals.On this basis,the predicted ToAs are assigned to the corresponding original signals for decoupling overlapped pulse-echoes and reconstructing high-resolution FMC datasets.The imaging resolution is enhanced to 0.5λby implementing the total focusing method(TFM).The relative errors of hole depths and central distance are no more than 0.51%and 3.57%,respectively.Finally,the superior performance of the proposed FS-MIS is validated by comparing it with initial feature space and conventional dimensionality reduction techniques.

An Effective Machine-Learning Based Feature Extraction/Recognition Model for Fetal Heart Defect Detection from 2D Ultrasonic Imageries

Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Cardiology,medical imaging technology(2D ultrasonic,MRI)has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis.It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane(FHUSP)manually.Compared withmanual identification,automatic identification through artificial intelligence can save a lot of time,ensure the efficiency of diagnosis,and improve the accuracy of diagnosis.In this study,a feature extraction method based on texture features(Local Binary Pattern LBP and Histogram of Oriented Gradient HOG)and combined with Bag of Words(BOW)model is carried out,and then feature fusion is performed.Finally,it adopts Support VectorMachine(SVM)to realize automatic recognition and classification of FHUSP.The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets.Compared with some other methods and the single method model,the classification accuracy of our model has been obviously improved,with the highest accuracy reaching 87.35%.Similarly,we also verify the performance of the model in normal and abnormal planes,and the average accuracy in classifying abnormal and normal planes is 84.92%.The experimental results show that thismethod can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.

Vibration suppression in multi-tool ultrasonic machining to multi-external and parametric excitations

Ultrasonic machining （USM） is of particular interest for the machining of non-conductive, brittle materials such as engineering ceramics. In this paper, a multi-tool technique is used in USM to reduce the vibration in the tool holder and have reasonable amplitude for the tools. This can be done via dynamic absorbers. The coupling of four nonlinear oscillators of the tool holder and tools representing ultrasonic cutting process are investigated. This leads to a four-degree-of-freedom system subjected to multi-external and multi-parametric excitation forces. The aim of this work is to control the tool holder behavior at simultaneous primary, sub-harmonic and internal resonance condition. Multiple scale perturbation method is used to obtain the solution up to the second order approximations. The different resonance cases are reported and studied numerically. The stability of the system is investigated by using both phase-plane and frequency response techniques. The effects of the different parameters of the tools on the system behavior are studied numerically. Comparison with the available published work is reported.

Analysis of the Rotary Ultrasonic Machining Mechanism

Ultrasonic machining (USM) is considered as an effective method for machining hard and brittle materials such as glass, engineering ceramics, semiconductors, diamonds, metal composites and so on. However, the low material removal rate due to using abrasive slurry limits further application of USM. Rotary ultrasonic machining (rotary USM) superimposes rotational movement on the tool head that vibrates at ultrasonic frequency (20 kHz) simultaneously. The tool is made of mild steel coated or bonded with diamond abrasive. Therefore, abrasive slurry is abandoned and coolant is used to carry debris out of working area. Compared with USM, rotary USM can obtain much higher material removal rate, deep holes, and fine precision, which leads to its further application. Combined with CNC technology, rotary USM can be used to conduct contour machining of hard and brittle materials. In this paper, the movement of abrasive particles in tool tip of rotary ultrasonic machining is analyzed. The impacting and grinding of abrasive in tool tip to machined surface are considered as main factors to material removal rate. The process of crack forming and growing in one loading and unloading cycle can be described as following stages: a) When abrasive particle acts the pressure on work-piece, the macro cracks in periphery of contact area are exerted increasing tensile stress. b) As the tensile stress increase to the critical of material tension, the one of cracks in periphery of contact area begins to propagate around contact area and develop beneath the surface to certain depth. c) Indentation area varies with increasing of load, the circle crack around contact area steadily or dynamical propagates towards inside of work-piece. d) As tensile stress in crack increases to critical of crack steady failure, circle crack suddenly becomes conic crack. e) Further increase load, the crack continues to grow while contact area is surrounded by conic cracks. f) During unloading, conic crack begins to close, some of cracks continue their extension towards the surface and forms a circle groove. The mathematical model for material removal rate shows that the factors affecting on material removal rate are static load, grid and concentration of abrasive, mechanical properties of machined materials, rotational speed of tool and feed speed of work-piece.

Mechanism of ultrasonic-pulse electrochemical compound machining based on particles

The electric double layer with the transmission of particles was presented based on the principle of electrochemistry.In accordance with this theory,the cavitation catalysis removal mechanism of ultrasonic-pulse electrochemical compound machining(UPECM) based on particles was proposed.The removal mechanism was a particular focus and was thus validated by experiments.The principles and experiments of UPECM were introduced,and the removal model of the UPECM based on the principles of UPECM was established.Furthermore,the effects of the material removal rate for the main processing parameters,including the particles size,the ultrasonic vibration amplitude,the pulse voltage and the minimum machining gap between the tool and the workpiece,were also studied through UPECM.The results show that the particles promote ultrasonic-pulse electrochemical compound machining and thus act as the catalyzer of UPECM.The results also indicate that the processing speed,machining accuracy and surface quality can be improved under UPECM compound machining.

Study of Stability and Vibration Reduction in Multi-Tool Ultrasonic Machining under Simultaneous Primary and Internal Resonance

The main object of this paper is the mathematical study of the vibration behavior in ultrasonic machining (USM) described by non-linear differential equations. The ultrasonic machining (USM) consists of the tool holder and the absor-bers representing the tools. This leads to four-degree-of-freedom system subject to multi-external excitation forces. The aim of this project is the reduction of the vibrations in the tool holder and have reasonable amplitudes for the tools represented by the multi-absorbers. Multiple scale perturbation method is applied to obtain the solution up to the second order approximation and to study the stability of the steady state solution near different simultaneous resonance cases. The resulting different resonance cases are reported and studied numerically. The stability of the steady state solution near the selected resonance cases is studied applying both frequency response equations and phase-plane technique. The effects of the different parameters of the system and the absorbers on the system behavior are studied numerically. Optimum working conditions for the tools were obtained. Comparison with the available published work is reported.

Experimental Study on the Machining Technology of ZrO_2 Ceramics Using Diamond Wire Saw with Ultrasonic Vibration

The influence of different technological parameter on material remove rate and surface quality of ZrO2 ceramics is studied using the cutting machining method of electroplate diamond wire saw with ultrasonic vibration.Experimental results show that,compared with the same experiment condition without ultrasonic vibration,this cutting method has the advantages of high material remove rate,good surface quality,little brokenness and so on.

Analysis of Machinable Structures and Their Wettability of Rotary Ultrasonic Texturing Method

Tailored surface textures at the micro- or nanoscale dimensions are widely used to get required functional performances. Rotary ultrasonic texturing （RUT） technique has been proved to be capable of fabricating periodic micro- and nanostructures. In the present study, diamond tools with geometrically defined cutting edges were designed for fabricating different types of tailored surface textures using the RUT method. Surface generation mechanisms and machinable structures of the RUT process are analyzed and simulated with a 3D-CAD program. Textured surfaces generated by using a triangular pyramid cutting tip are constructed. Different textural patterns from several micrometers to several tens of micrometers with few burrs were successfully fabricated, which proved that tools with a proper two-rake-face design are capable of removing cutting chips efficiently along a sinusoidal cutting locus in the RUT process. Technical applications of the textured surfaces are also discussed. Wetting properties of textured aluminum surfaces were evaluated by combining the test of surface roughness features. The results show that the real surface area of the textured aluminum surfaces almost doubled by comparing with that of a flat surface, and anisotropic wetting properties were obtained due to the obvious directional textural features.

Ultrasonic Sensor-Based Embedded System for Vehicular Collusion Detection and Alert

The efficacy of an automated collision detection system is contingent upon the caliber and volume of data at its disposal. In the event that the data is deficient, incongruous, or erroneous, it has the potential to generate erroneous positive or negative outcomes, thereby compromising the system’s credibility. The occurrence of false positives is observed when the system erroneously identifies genuine activity as collusion. The phenomenon of false negatives arises when the system is unable to identify instances of genuine collusion. Collusion detection systems are required to handle substantial volumes of data in real time, capable of analyzing relationships between different objects. The intricate nature of collusion can pose difficulties in devising and executing efficient systems for its detection. The present study proposes an automated anti-collision system that utilizes sensor devices to detect objects and activate an alert mechanism in the event that the vehicle approaches the object in close proximity. The study introduces a novel methodology for mitigating vehicular accidents by implementing a combined system that integrates collision detection and alert mechanisms. The proposed system comprises an ultrasonic sensor, a microprocessor, and an alarm system. The sensor transmits a signal to the microcontroller, which in turn sends a signal to the warning unit. The warning unit is designed to prevent potential accidents by emitting an audible warning signal through a buzzer. Additionally, the distance information is displayed on an LCD screen. The Proteus Design Suite is utilized for simulation purposes, while Arduino.cc is employed for implementation.

Design of Five-Axis Ultrasonic Assistant Compound Machine Tool

A compound machine tool was designed, which combined rotary ultrasonic assisted grinding, electrical discharge machining(EDM) and multi-axis milling. Experimental results indicated that its positioning accuracy was less than 5.6 μm and its repetitive positioning accuracy was less than 1.8 μm; the vibration amplitude of ultrasonic grinding system was uniform and stable, and the EDM system worked well and stably.A smooth surface of K9 optical glass component was achieved by the grinding method.

Study on flaw identification of ultrasonic signal for large shafts based on optimal support vector machine

Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft.A novel automatic defect identification system is presented.Wavelet packet analysis(WPA)was applied to feature extraction of ultrasonic signal,and optimal Support vector machine(SVM)was used to perform the identification task.Meanwhile,comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models.To validate the method,some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.

Ultrasonic prediction of crack density using machine learning:A numerical investigation

Cracks are accounted as the most destructive discontinuity in rock, soil, and concrete. Enhancing our knowledge from their properties such as crack distribution, density, and/or aspect ratio is crucial in geo-systems. The most well-known mechanical parameter for such an evaluation is wave velocity through which one can qualitatively or quantitatively characterize the porous media. In small scales, such information is obtained using the ultrasonic pulse velocity(UPV) technique as a non-destructive test. In large-scale geo-systems, however, it is inverted from seismic data. In this paper, we take advantage of the recent advancements in machine learning(ML) for analyzing wave signals and predict rock properties such as crack density(CD) – the number of cracks per unit volume. To this end, we designed numerical models with different CDs and, using the rotated staggered finite-difference grid(RSG) technique, simulated wave propagation. Two ML networks, namely Convolutional Neural Networks(CNN) and Long Short-Term Memory(LSTM), are then used to predict CD values. Results show that, by selecting an optimum value for wavelength to crack length ratio, the accuracy of predictions of test data can reach R2> 96% with mean square error(MSE) < 25e-4(normalized values). Overall, we found that:(i) performance of both CNN and LSTM is highly promising,(ii) accuracy of the transmitted signals is slightly higher than the reflected signals,(iii) accuracy of 2D signals is marginally higher than 1D signals,(iv)accuracy of horizontal and vertical component signals are comparable,(v) accuracy of coda signals is less when the whole signals are used. Our results, thus, reveal that the ML methods can provide rapid solutions and estimations for crack density, without the necessity of further modeling.

Process Optimization of Ultrasonic Extraction of Puerarin Based on Support Vector Machine

In ultrasonic extraction technology, optimization of technical parameters often considers extraction medium only, without including ultrasonic parameters. This paper focuses on controlling the ultrasonic extraction process of puerarin, investigating the influence of ultrasonic parameters on extraction rate, and empirically analyzing the main components of Pueraria, i.e., isoflavone compounds. A method is presented combining orthogonal experi- mental design with a support vector machine and a predictive model is established for optimization of technical parameters. From the analysis with the predictive model, appropriate process parameters are achieved for higher extraction rate. With these parameters in the ultrasonic extraction of puerarin, the experimental result is satisfactory. This method is of significance to the study of extracfing root-stock plant medicines.

TP-Series Ultrasonic Cleaning Machines

In the patents pavilion of the Fifth Asia-Pacific Fair, the attention of numerous visitors was riveted by an onthe-spot demonstration. Standing before them was a machine with a stainless steel casing partly in a container of clear water. The operator pressed a button and ripples appeared on the surface of the water. The operator took watch chain s and jewellery from the visitors and put them in

Research on Cutting Force of Ultrasonic Diamond Wire Saw

Based on impulse and vibration machining theories,a mathematical model of cutting force for the electroplated diamond ultrasonic wire saw was established using superposition principle.The differences between the cutting forces with and without ultrasonic effect were analyzed theoretically and experimentally.The results indicate that the cutting force of diamond wire increases along with the spindle speed decrease and the lateral pressure increase.The force in ultrasonic vibration cutting is about 20% to 30% less than that in conventional cutting.Also,the cutting trajectory of single diamond grit in sawing process is simulated,and the reason that the ultrasonic vibration can reduce the cutting force is explained further.

Monitoring Thermal Coagulation with Ultrasonic Textures

The feasibility of using B-mode ultrasound image textures and pattern recognition technique to characterize the thermal coagulation in vitro during radiofrequency ablation was investigated. The changes of ultrasonic textures in the different regions of samples varied with the heating time in the in-vitro experiments, which would result in that the coagulated and noncoagulated regions of tissue had different ultrasonic textures. Using support vector machine to extract the ultrasonic texture features and characterize the state of tissue, the size and boundaries of thermal lesions could be detected and measured more exactly than only using the gray scale information of B-mode ultrasound image. The proposed method would be applied to the image-guided radiofrequency ablation (IGRA) procedure for monitoring the thermal coagulation.

旋转超声加工对TiBw网状钛基复合材料表面残余应力的影响

为研究不同切削条件下旋转超声加工对材料表面残余应力的影响,选取硼化钛晶须(TiB whiskers,TiBw)网状钛基复合材料为实验对象,使用镍基电铸金刚石砂轮进行旋转超声加工,并分析了加工后材料的表面残余应力。结果表明:由于钛基复合材料具有网状结构和优异的高温性能,使切削热和微观相变产生的残余应力较小,磨粒机械作用产生的残余应力更大。随着超声振动的引入,磨粒高频冲击工件表面,使旋转超声加工工件表面残余应力均大于普通磨削。在主轴转速n=9000 r/min,进给速度v f=8 mm/min,加工深度a p=25μm的加工参数下,旋转超声加工工件表面存在-540 MPa的残余压应力,随着主轴转速增大,残余应力显著减小,并且残余应力的影响层深度逐渐减小。超声振动的引入增大工件表面残余压应力,提高材料抗疲劳性能。

基于激光超声的玉米虫蚀粒检测研究

虫蚀影响玉米的外观质量和营养价值,研究采用了一种基于激光超声的玉米虫蚀粒检测方法对玉米虫蚀粒进行检测。首先,利用脉冲激光照射完善粒和虫蚀粒玉米的表面产生激光超声信号。然后,提取了超声信号时域峰值因子和脉冲因子,频域重心频率和均方频率,Hilbert域高频能量作为特征参数。最后,将这5个特征分别作为BP神经网络和粒子群优化支持向量机(PSO-SVM)算法的输入对虫蚀粒和完善粒进行了分类识别。实验结果表明,PSO-SVM算法建立的分类模型对玉米完善粒和虫蚀粒的分类识别更准确,训练集和测试集准确率分别为99.72%和98.33%,所采用的方法是可行的。

超声振动辅助电火花加工表面成形机理研究

以TC4钛合金作为工件材料,通过建立高斯热源在工件表面的分布模型,描述了单脉冲电火花加工凹坑的成形机理。为了验证模型的准确性,进行了仿真和实验验证。结果表明:通过分析仿真得到的温度场云图和实验测得的数据发现,高斯热源分布模型与两者一致。在引入超声振动后,表面粗糙度降低35%~46%,材料表面凹坑明显减少,表面形貌发生显著变化。随振幅增大,高斯热源分布曲线的最大值减小,温度场中心最高温度降低,深度变浅的表面凹坑数量增加,单个凹坑的面积增加,表面粗糙度变好。

难加工材料纵扭超声振动辅助铣削加工研究进展

纵扭复合超声振动辅助铣削(LTUVAM)是在刀具轴向与扭转方向上施加高频微幅振动的一种辅助铣削技术,具有降低切削力与切削热、提高工件表面质量、提高表面残余压应力、减少刀具磨损等诸多优点。本文围绕难加工材料的铣削加工进行了系统综述。在设备制造方面,阐述了纵扭复合超声振动系统的结构设计方法与工作原理;在工艺开发方面,从LTUVAM的切削刃运动轨迹及切削特性入手,分析各类材料的切削加工性能,并总结了LTUVAM的优势及应用。最后,本文对LTUVAM的未来发展趋势进行展望。