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.

Machines and Testing Equipment

National Quality Supervision ＆ Inspection Center for Refractories Business scope： Selective examination for national quality supervision; Identification of production license; Arbitration inspection and technical achievements evaluation; Commodities inspection and otherquality inspections .

Real-time embedded software testing method based on extended finite state machine

The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded sys- tem, and the effective software testing is an important way to ensure software quality and reliability. Based on the analysis of the characteristics of real-time embedded software, the formal method is introduced into the real-time embedded software testing field and the real-time extended finite state machine （RT-EFSM） model is studied firstly. Then, the time zone division method of real-time embedded system is presented and the definition and description methods of time-constrained transition equivalence class （timeCTEC） are presented. Furthermore, the approaches of the testing sequence and test case generation are put forward. Finally, the proposed method is applied to a typical avionics real- time embedded software testing practice and the examples of the timeCTEC, testing sequences and test cases are given. With the analysis of the testing result, the application verification shows that the proposed method can effectively describe the real-time embedded software state transition characteristics and real-time requirements and play the advantages of the formal methods in accuracy, effectiveness and the automation supporting. Combined with the testing platform, the real-time, closed loop and automated simulation testing for real-time embedded software can be realized effectively.

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model:A cohort study

BACKGROUND Liver transplant(LT)patients have become older and sicker.The rate of post-LT major adverse cardiovascular events(MACE)has increased,and this in turn raises 30-d post-LT mortality.Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients.AIM To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort.METHODS This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center.We developed a predictive model for post-LT MACE(defined as a composite outcome of stroke,new-onset heart failure,severe arrhythmia,and myocardial infarction)using the extreme gradient boosting(XGBoost)machine learning model.We addressed missing data(below 20%)for relevant variables using the k-nearest neighbor imputation method,calculating the mean from the ten nearest neighbors for each case.The modeling dataset included 83 features,encompassing patient and laboratory data,cirrhosis complications,and pre-LT cardiac assessments.Model performance was assessed using the area under the receiver operating characteristic curve(AUROC).We also employed Shapley additive explanations(SHAP)to interpret feature impacts.The dataset was split into training(75%)and testing(25%)sets.Calibration was evaluated using the Brier score.We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting.Scikit-learn and SHAP in Python 3 were used for all analyses.The supplementary material includes code for model development and a user-friendly online MACE prediction calculator.RESULTS Of the 537 included patients,23(4.46%)developed in-hospital MACE,with a mean age at transplantation of 52.9 years.The majority,66.1%,were male.The XGBoost model achieved an impressive AUROC of 0.89 during the training stage.This model exhibited accuracy,precision,recall,and F1-score values of 0.84,0.85,0.80,and 0.79,respectively.Calibration,as assessed by the Brier score,indicated excellent model calibration with a score of 0.07.Furthermore,SHAP values highlighted the significance of certain variables in predicting postoperative MACE,with negative noninvasive cardiac stress testing,use of nonselective beta-blockers,direct bilirubin levels,blood type O,and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level.These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE,making it a valuable tool for clinical practice.CONCLUSION Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE,using both cardiovascular and hepatic variables.The model demonstrated impressive performance,aligning with literature findings,and exhibited excellent calibration.Notably,our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data,reinforcing the model’s value as a reliable tool for predicting post-LT MACE in clinical practice.

结合GUM和MCM的R-Test在机标定不确定度评定方法

R-Test因其高精度、操作便捷等的优点而被广泛应用于五轴数控机床转动轴误差测量,但其测量精度受其标定不确定度的影响。针对原有通用的评定方法不能对接触式R-Test标定不确定度进行准确评定,基于接触式R-Test标定原理,提出一种结合GUM和MCM的在机标定不确定度评定方法,首先分析了不确定度的影响因素并采用变参数方法确定了其灵敏度系数,然后根据测量不确定度导则(GUM)计算传感器位置的标准不确定度分量,最后采用蒙特卡洛方法(MCM)实现对标定方法最终的不确定度评定。通过具体的评定实例,与独立使用GUM的评定结果进行对比,验证了该方法具有更高的评定精度。

Software Development for Digital Control of WDW Series Testing Machine and Measurement of K_(IC)

Software has been developed for digital control of WDW series testing machine and the measurement of fracture toughness by modularized design. Development of the software makes use of multi-thread and serial communication techniques, which can accurately control the testing machine and measure the fracture toughness in real-time. Three-point bending specimens were used in the measurement. The software operates stably and reliably, expanding the function of WDW series testing machine.

Experimental Study on Thermal Stress of Concrete with Different Expansive Minerals Using a Temperature Stress Testing Machine

In order to compare the compensation effect of expansive materials with different mineral sources on the temperature stress of concrete,we investigated the temperature stress of concrete when adding calcium sulfoaluminate type expansive materials(CSA)or CaO and calcium sulfoaluminate mixed type expansive materials(HCSA)at different temperatures by temperature-stress testing machine(TSTM)considering the influence of temperature history on the expansion.The experimental results show that the expansion characteristics of the two kinds of expansive materials with different mineral sources significantly vary.When adding expansive materials,the growth rate of compressive stress during the heating stage increases obviously,the maximum compressive stress is higher,while the decline rate of tensile stress in the late cooling stage becomes slow,and finally cracking temperature decreases.It is proved that concrete with HCSA has lower cracking temperatures and better temperature shrinkage compensation effect.Therefore,it is rational to choose HCSA when preparing concrete with high expansion energy to reduce thermal cracking.

Estimation of Fatigue Strength of Reinforced Complete Upper Denture Using a Newly Designed Testing Machine: A Laboratory Research Project

In the present study, an aero pneumatic fatigue testing machine for complete dentures was designed, fabricated, and tested for the evaluation of the fatigue life of reinforced complete upper denture (CUD). On completion and testing, it was observed that the machine has the potential of generating reliable number of cyclic data. The machine’s performance was evaluated using test specimens of identical CUDs that were machined in conformity with standard procedures. The fatigue machine compressed the lower dental arch over the upper denture-specimen in centric occlusion, in the same way that the two masticatory muscles pull the lower jaw over the upper jaw during chewing. The incorporation of glass fibres into the CUD using a sandwich technique quadruples the lifespan of the denture (P = 0.004). The low standard deviation, along with the low coefficient of variation (CV) of the group of unreinforced dentures shows the repeatability of the results and the reliability of the machine. The high standard deviation and coefficient of variation of reinforced dentures was expected, since a high variation of results is usually recorded in fibre reinforcement cases. This research confirmed the view that the crack during denture fracture initiates in the anterior palatal area and propagates to the posterior.

Test Selection on Extended Finite State Machines with Provable Guarantees

Building high confidence regression test suites to validate new system versions is a challenging problem. A modelbased approach to build a regression test suite from a given test suite is described. The generated test suite includes every test that will traverse a change performed to produce the new version, and consists of only such tests to reduce the testing costs. Finite state machines extended with typed variables (EFSMs) are used to model systems and system changes are mapped to EFSM transition changes adding/deleting/replacing EFSM transitions and states. Tests are a sequence of input and expected output messages with concrete parameter values over the supported data types. An invariant is formulated to characterize tests whose runtime behavior can be accurately predicted by analyzing their descriptions along with the model. Incremental procedures to efficiently evaluate the invariant and to select tests for regression are developed. Overlaps among the test descriptions are exploited to extend the approach to simultaneously select multiple tests to reduce the test selection costs. Effectiveness of the approach is demonstrated by applying it to several protocols, Web services, and model programs extracted from a popular testing benchmark. Our experimental results show that the proposed approach is economical for regression test selection in all these examples. For all these examples, the proposed approach is able to identify all tests exercising changes more efficiently than brute-force symbolic evaluation.

Web Testing Generation: A Stream <i>X-Machine</i>Based Approach

To ensure the quality of Web applications, Web testing is one of the effective methods. The testing is a process of revealing errors that is used to give confidence that the implementation of a Web application meets its original specification. This work proposes a Web testing framework based on Stream X-Machines (SXMs), which provides a way to derive test cases for a Web application. It starts from constructing the SXM model, from which a test translator is employed to extract the test paths and then translates them into an XML-style test specification, which is the input of test engine. The test engine generates test cases and then executes them, and finally produces test report. This testing method is a significant contribution to informed research.

Constitutive Modeling for Ti-6Al-4V Alloy Machining Based on the SHPB Tests and Simulation

A constitutive model is critical for the prediction accuracy of a metal cutting simulation. The highest strain rate involved in the cutting process can be in the range of 104-106 s 1. Flow stresses at high strain rates are close to that of cutting are difficult to test via experiments. Split Hopkinson compression bar （SHPB） technology is used to study the deformation behavior of Ti-6Al-4V alloy at strain rates of 10 -4-10 4s- 1. The Johnson Cook （JC） model was applied to characterize the flow stresses of the SHPB tests at various conditions. The parameters of the JC model are optimized by using a genetic algorithm technology. The JC plastic model and the energy density-based ductile failure criteria are adopted in the proposed SHPB finite element simulation model. The simulated flow stresses and the failure characteristics, such as the cracks along the adiabatic shear bands agree well with the experimental results. Afterwards, the SHPB simulation is used to simulate higher strain rate（approximately 3 × 10 4 s -1） conditions by minimizing the size of the specimen. The JC model parameters covering higher strain rate conditions which are close to the deformation condition in cutting were calculated based on the flow stresses obtained by using the SHPB tests （10 -4 - 10 4 s- 1） and simulation （up to 3 × 10 4 s - 1）. The cutting simulation using the constitutive parameters is validated by the measured forces and chip morphology. The constitutive model and parameters for high strain rate conditions that are identical to those of cutting were obtained based on the SHPB tests and simulation.

ANALYSIS AND SIMULATION ON THE MECHANISM OF A NOVEL DUAL-WAVE SHOCK TEST MACHINE

For qualifying the anti-shock performance of shipboard equipments and simulating actual underwater explosion environments, a novel dual-wave shock test machine is proposed to increase testing capability of shock test machines as well as to meet certain shock testing specification. The machine can generate a double-pulse acceleration shock for test articles according to specification defined in BV043/85. On the basis of the impact theory, a nonlinear dynamic model of the hydraulically-actuated test machine is established with thorough analysis on its mechanism which involves conversion of gas potential energy and dissipation of kinetic energy. Simulation results have demonstrated that the machine can produce a double-pulse acceleration shock in the time domain or a desired shock response spectrum in the frequency domain, which sets a theoretical base for the construction of the proposed machine.

Identification of Kinematic Errors of Five-axis Machine Tool Trunnion Axis from Finished Test Piece

Compared with the traditional non-cutting measurement,machining tests can more accurately reflect the kinematic errors of five-axis machine tools in the actual machining process for the users.However,measurement and calculation of the machining tests in the literature are quite difficult and time-consuming.A new method of the machining tests for the trunnion axis of five-axis machine tool is proposed.Firstly,a simple mathematical model of the cradle-type five-axis machine tool was established by optimizing the coordinate system settings based on robot kinematics.Then,the machining tests based on error-sensitive directions were proposed to identify the kinematic errors of the trunnion axis of cradle-type five-axis machine tool.By adopting the error-sensitive vectors in the matrix calculation,the functional relationship equations between the machining errors of the test piece in the error-sensitive directions and the kinematic errors of C-axis and A-axis of five-axis machine tool rotary table was established based on the model of the kinematic errors.According to our previous work,the kinematic errors of C-axis can be treated as the known quantities,and the kinematic errors of A-axis can be obtained from the equations.This method was tested in Mikron UCP600 vertical machining center.The machining errors in the error-sensitive directions can be obtained by CMM inspection from the finished test piece to identify the kinematic errors of five-axis machine tool trunnion axis.Experimental results demonstrated that the proposed method can reduce the complexity,cost,and the time consumed substantially,and has a wider applicability.This paper proposes a new method of the machining tests for the trunnion axis of five-axis machine tool.

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.

An approach to formalizing specification-based class testing

This paper presents a framework that can be used to formalize the specification-based single-class test generation process. Object-Z is used to describe both software requirements and the proposed framework. Using this framework, test engineers can automatically get the testing process model during the test generation process. With this model, properties of test cases （ such as relationship between test cases and methods can easily be captured. Furthermore, with the framework, the test process model can be updated automatically with the test generation process. The properties of test cases can then be updated correspondingly. It will greatly facilitate the regression testing. The main contribution of this paper is that it provides an approach to formalizing testing process by extending existing framework to class testing, and a way to represent test cases as multi-part, multi-step, multi-level artifacts.

An EFSM-Based Test Data Generation Approach in Model-Based Testing

Testing is an integral part of software development.Current fastpaced system developments have rendered traditional testing techniques obsolete.Therefore,automated testing techniques are needed to adapt to such system developments speed.Model-based testing(MBT)is a technique that uses system models to generate and execute test cases automatically.It was identified that the test data generation(TDG)in many existing model-based test case generation(MB-TCG)approaches were still manual.An automatic and effective TDG can further reduce testing cost while detecting more faults.This study proposes an automated TDG approach in MB-TCG using the extended finite state machine model(EFSM).The proposed approach integrates MBT with combinatorial testing.The information available in an EFSM model and the boundary value analysis strategy are used to automate the domain input classifications which were done manually by the existing approach.The results showed that the proposed approach was able to detect 6.62 percent more faults than the conventionalMB-TCG but at the same time generated 43 more tests.The proposed approach effectively detects faults,but a further treatment to the generated tests such as test case prioritization should be done to increase the effectiveness and efficiency of testing.

An Ontology Based Test Case Prioritization Approach in Regression Testing

Regression testing is a widely studied research area,with the aim of meeting the quality challenges of software systems.To achieve a software system of good quality,we face high consumption of resources during testing.To overcome this challenge,test case prioritization(TCP)as a sub-type of regression testing is continuously investigated to achieve the testing objectives.This study provides an insight into proposing the ontology-based TCP(OTCP)approach,aimed at reducing the consumption of resources for the quality improvement and maintenance of software systems.The proposed approach uses software metrics to examine the behavior of classes of software systems.It uses Binary Logistic Regression(BLR)and AdaBoostM1 classifiers to verify correct predictions of the faulty and non-faulty classes of software systems.Reference ontology is used to match the code metrics and class attributes.We investigated five Java programs for the evaluation of the proposed approach,which was used to achieve code metrics.This study has resulted in an average percentage of fault detected(APFD)value of 94.80%,which is higher when compared to other TCP approaches.In future works,large sized programs in different languages can be used to evaluate the scalability of the proposed OTCP approach.

NUMERICAL MODELING OF MULTI-CYLINDER ELECTRO-HYDRAULIC SYSTEM AND CONTROLLER DESIGN FOR SHOCK TEST MACHINE

A high fidelity dynamic model of a high-energy hydraulically-actuated shock test machine for heavy weight devices is presented to satisfy the newly-built shock resistance standard and simulate the actual underwater explosion environments in laboratory as well as increase the testing capability of shock test machine. In order to produce the required negative shock pulse in the given time duration, four hydraulic actuators are utilized. The model is then used to formulate an advanced feedforward controller for the system to produce the required negative waveform and to address the motion synchronization of the four cylinders. The model provides a safe and easily controllable way to perform a ＂virtual testing＂ before starting potentially destructive tests on specimen and to predict performance of the system. Simulation results have demonstrated the effectiveness of the controller.

Development of a Test Machine for IVECO Drive Axle

To improve the automation level of the vehicle drive axle test and better simulate a vehicle＇s actual operation, an advanced test machine has been developed. The load system of the machine consists of hand brakes and electric cylinders. It is simple-structured and low-cost. The major motor of the machine is controlled by a transducer and its speed can be adjusted easily. In addition, the programmed machine can automatically test such parameters as the grinding condition, the differential speed, the noise level, etc. It can also adjust the test procedures according to different requirements. Detailed discussion of the structure and mechanism of the test machine is given in this paper.

Support-Vector-Machine-Based False Alarm Filter of Mechatronic Built-in Test

Diagnosing intermittent fault is an important approach to reduce built-in test（BIT） false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit of support vector machines （ SVM） which can be trained with a small-sample, an SVM-based diagnostic model of 3 states that include OK state, intermittent state and faulty state is presented. With the features based on the reflection coefficients of an alarm rate （ AR ） model extracted from small vibration samples, these models are trained to diagnose intermittent faults. The experimental results show that this method can diagnose multiple intermittent faults accurately with small training samples and BIT false alarms are reduced.