Automatic Test Research Articles

The Curriculum in NDT Training Have to Include Basics Information Technology to Accomplish Reliability of Automated NDT Result's

Automation in Non-Destructive Testing (NDT) is a reality of modern manufacturing and quality assurance. Information technology applications are simultaneously developed and applied to different fields of NDT methods. The future of doing reliability checks using automated system is inevitable. The results are important to make a variety of decisions regarding the quality of a product and/or service, hence affecting safety and reliability. Depending on artificial intelligence and quantum device development, various NDT training schemes have to adopt a new curriculum to address the issue of the basic knowledge of information technology and automation in NDT. NDT operators are basically dependent on the end results of software, in which the software developer uses a set of algorithms. These are obtained in three categories; 1) Real time database; 2) Synthetic database and 3) Hybrid or combination of them. The developers should know how the NDT systems run and NDT operators in the other hand know the mechanism of the program, so that appropriate action can be taken, in case there is a missed detection, or a detected indication is not very much detrimental to the reliability of the part (Over sizing or wrongly evaluated). For example, a set of algorithms are used to create the focal laws in a phased array ultrasonic test software in space of 1˚ beam steering. Each has a limit of maximum up to 2nd Critical angle. Operators try to copy the same algorithm and set up to 89˚ refraction angle, which will just create creep wave with unreliable results to detect effective size the reflector. Programming knowledge of the operator can effectively eliminate this type of unreliability. In various Schemes of NDT Syllabus which are in common place to employ and train plus certify operators does not have it; actually, the operators do the scope of work which does includes application of Artificial Intelligence (AI) and Software. With the development of Q coupler (Quantum Computing) system, it is obvious to adopt the upcoming technology into the application of NDT industry. Training needs to update the curriculum accordingly. The current topic has intended to focus on the training gap and to address related kind of unreliability.

A novel analysis method for tension estimation of cross-linked suspenders based on single vibration measurements

While the use of vibration measurements to detect axially loaded forces is a well-established scheme for the diagnosis of tensioned slender members, the traditional approaches are not suitable for suspenders which are attached with transverse fasteners. As a result, the evaluation process for linked suspenders generally requires unlocking and reconnecting of the fasteners during performing vibration tests. This paper presents an innovated idea of using single vibration measurements with simple analytical models to develop an effective technique to estimate tension of individual member for cross-linked suspenders. The development of the technique relies on the use of simplified string formulas such that iterative approximations with numerical coding can be effectively executed for both the backward estimation and forward verification stages of the assessment procedure. The feasibility of the proposed method has been numerically studied and verified with selected data from field tests. It is anticipated that the procedure can serve as a better on-site tool for the vibration tests of fastener- restrained suspenders and cables.

Selected Aspects of Research on Railway Transport Safety

This article showcases the findings of research conducted at the Faculty of Transport, Electrotechnics, and Informatics of the Technological-Humanistic University in Radom. Initially, it delves into a statistical analysis of measurements procured during automatic ultrasonic testing. By integrating these measurements with distinct features of the diagnostic method and fusing differential coding with traditional lossless compression techniques (like Huffman and dictionary coding), a potent measurement compression algorithm was developed. This algorithm subsequently found application in railway track diagnostics. The primary objective of this article is to illustrate this algorithm's efficiency and benchmark its advantages against other compression techniques. Subsequently, the paper delineates the design of a system tailored for collating operational data and evaluating the reliability and safety of railway automation systems. This concept was actualized under the auspices of a project sponsored by the National Center for Research and Development, PBS3/A6/29/2015. A spotlight will be thrown on the software that acts as an integrative tool, assimilating data from various systems. In its final segments, the article discusses the initiative to harness open systems, premised on the PN-EN 50159:2011 standard, to bolster the safety standards of railway transportation. The first one resulted from the maximum train speed, the second one is the variety of rolling stock used on this line (compartmental and non-compartmental wagons). The part of research concerning collection of measurement material was performed within the framework of completed research work PBS3/A6/29/2015. As a result of conducted in-depth literature analysis as well as performed measurements and calculations, it allowed to develop a model and software for simulating system operation in real conditions. A prototype of this system, accompanied by select research outcomes from railway routes, will be laid out. Ultimately, the article seeks to emphasize the significance of diverse research endeavors that hold the potential to directly enhance the safety and operational efficiency of railway transport.

Research on Automatic Testing of Pump Turbine Model Test Based on Intelligent Technology

Abstract In order to test the performance of pump turbine, it is necessary to collect and analyze the performance indexes such as energy characteristics, stability performance, cavitation performance and force characteristics of the unit through the pump turbine model test test system. however, the current test systems all use manual operation based on the standard specification requirements to handle the data collection and analysis of the test, and the accuracy of the data relies on human experience and the efficiency is not high. The method described in this paper uses emerging computer test technology means such as big data technology, noise and image recognition technology to automatically and efficiently complete the test data test, and quickly complete the test test, while the test results are more accurate.

Development of a high-speed and ultrasensitive UV/Vis-CM for detecting total triterpenes in traditional Chinese medicine and its application

This study proposes a novel colorimetric method based on the ultraviolet/visible spectrophotometry-colorimetric method (UV/Vis-CM) for detecting and quantifying total triterpenoids in traditional Chinese medicine. By incorporating the colourants 2-hydroxy-5-methylbenzaldehyde and concentrated sulfuric acid, triterpenoid compounds colour development became more sensitive, and the detection accuracy was significantly improved. 2-hydroxy-5-methylbenzaldehyde and concentrated sulfuric acid were incorporated in a 1:3 vol ratio at room temperature to react with the total triterpenes for 25 min, incorporated to an ice bath for 5 min, and then detected at the optimal absorption wavelength. The accuracy and reliability of this method were verified by comparison with high-performance liquid chromatography and four other colorimetric methods. Additionally, this approach has the advantages of not requiring heating during operation, high sensitivity, short usage time, low solvent usage, and low equipment costs. This study not only offers a reliable method for detecting total triterpenes in traditional Chinese medicine but also offers a rapid detection tool for on-site testing and large-scale screening, laying a foundation for the modernization of traditional Chinese medicine research, quality control, and drug development.

Where there is no genetic counselor: An online decision-aid supports the majority of parents’ diagnostic genomic testing choices for their children

PurposeWe evaluated DECIDE, an online pretest decision-support tool for diagnostic genomic testing, in nongenetics specialty clinics where there are no genetic counselors (GCs). MethodsFamilies of children offered genomic testing were eligible to participate. Fifty-six parents/guardians completed DECIDE at home, at their convenience. DECIDE includes an integrated knowledge quiz and decisional conflict screen. Six months later, parents were offered follow-up questionnaires and interviews about their experiences. ResultsForty parents (71%) had sufficient knowledge and no decisional conflict surrounding their testing decision, but 6 of this group had residual questions. These 6, plus 16 with decisional conflict or insufficient knowledge, saw a GC. At follow-up, little-to-no decisional regret and few negative emotions were identified in any parents. Most chose testing and described their decision as easy, yet stressful, and described many motivations for sequencing. Parents appreciated the simple comprehensive information DECIDE provided and the ability to view it in a low-stress environment. ConclusionDECIDE provides adequate decision-support to enable most parents to make value-consistent choices about genetic testing for their child. Parents reported that DECIDE helped to clarify motivations for pursuing (or declining) testing. DECIDE is a timely, well-tested, and accessible tool in clinical settings without GCs.

Test Maturity Model integration (TMMi): Test Maturity in the Financial Domain

Software quality is of utmost importance to the financial sector. Software testing plays a critical role in achieving software product quality. Financial institutions benefit from rigorous testing by having confidence in the reliability and performance of the software. This can lead to improved customer experience, increased operational efficiency, and reduced risks of system failures or security breaches. A questionnaire-based survey was designed and subsequently an international survey was conducted involving sixty financial institutions, e.g., banking, insurance companies and pension funds, from across the globe to understand their level of test maturity. As a reference framework against which to measure their test maturity, the Test Maturity Model integration (TMMi) was used. In this paper their motivations for doing test process improvement and the benefits they achieved are discussed. Concrete examples of the benefits achieved are provided. The role of test automation with test process improvement at the financial institutions is also reported upon in this paper. The most common level of test maturity achieved, measured against the TMMi, is TMMi level 3 “Defined” which represents a more than average level of test maturity. Benefits are reported by the financial institutions, especially in the areas of software quality and testing productivity. The benefits achieved show a high level of correlation with the motivations for investing in test process improvement. Almost all of financial institutions also use test automation to improve their testing in parallel with process improvement, with test automation at system level being by far the most popular.

A new method for motion stability improving of dual-robotic ultrasonic nondestructive testing system

The dual-robotic ultrasonic nondestructive testing (NDT) system equipped with extension arm is an effective means to realize automatic ultrasonic testing of composite semi-enclosed workpieces. However, this cantilever structure amplifies the tiny vibrations of the inspection system, which seriously affects the coupling effect and the resolution of ultrasonic inspection. To solve the problem, this paper proposes a S-shaped acceleration and deceleration feature control algorithm to improve the motion stability and further to improve the inspection resolution of dual-robotic ultrasonic NDT system. Furthermore, the specific steps of S-shaped acceleration and deceleration features control algorithm in trajectory planning of the dual-robotic ultrasonic NDT system are described in detail. Comparative experiments of motion stability show that the acceleration in the x direction at the left and right ends of the detection area decreased significantly, from 0.15 to 0.08 m/s2 and the ultrasonic image of the internal defects have a higher resolution and smoother edges. The improvement plays an important role in promoting the resolution at the left and right ends of the detection area.

A Geant4-based Monte Carlo X-ray imaging simulation platform

X-ray digital radiography plays a pivotal role in accessing the internal structure of objects. Nevertheless, key imaging parameters, such as source-to-target distance or number of projected images needed for three-dimensional construction, mostly rely on real-life trials thus increasing radiation exposure risks. This paper introduces a recently developed software platform known as GMAX (Geant4-based Monte Carlo Advanced X-ray) that helps addressing above issues from a simulation perspective. GMAX employs Geant4, a Monte Carlo simulation code at its core and facilitates high-fidelity X-ray imaging, requiring no prior user experience. Compared to CAD models that only reflect object's geometrical information, GMAX simulates how objects interact with photon and can accurately evaluate important imaging parameters, such as target object to detector distance. It also provides three-dimensional construction functionality for images and therefore could be used as an effective tool for X-ray non-destructive testing and inspection.

Multiphase electroextraction of malachite green from surface water and its determination using digital imaging and chemometric tools.

This study introduces a novel method for the quantification of malachite green (MG), a pervasive cationic dye, in surface water by synergizing multiphase electroextraction (MPEE) with digital image analysis (DIA) and partial least square discriminant analysis. Aimed at addressing the limitations of conventional DIA methods in terms of quantitation limits and selectivity, this study achieves a significant breakthrough in the preconcentration of MG using magnesium silicate as a novel sorbent. Demonstrating exceptional processing efficiency, the method allows for the analysis of 10 samples within 20min, exhibiting remarkable sensitivity and specificity (over 0.95 and 0.90, respectively) across 156 samples in both training and test sets. Notably, the method detects MG at low concentrations (0.2µgL-1) in complex matrices, highlighting its potential for broader application in environmental monitoring. This approach not only underscores the method's cost-effectiveness and simplicity but also its precision, making it a valuable tool for the preliminary testing of MG in surface waters. This study underscores the synergy among MPEE, DIA, and chemometric tools, presenting a cost-efficient and reliable alternative for the sensitive detection of water contaminants.

P.135 Automated awake brain mapping with eloquentaid: a novel tool for low-resource settings

Background: Intraoperative testing for awake craniotomies requires a multidisciplinary team which may not be available in low-resource settings. We explored the creation of an AI tool for automated testing. Methods: We developed a NodeJS application, EloquentAid (https://www.eloquentaid.com/), for language testing automation. The workflow was as follows: users select an image-based naming task and verbally identify the image in English. Then, the application transcribes the response using OpenAI’s Whisper transcription service. Finally, the application evaluates response correctness. Feedback is provided through auditory and color signals. To assess its reliability, we tested the EloquentAid versus a human rater using a 57-item test based on the Boston Naming Test. Participants were neurosurgery and neurology residents from the Philippines. Qualitative surveys were obtained post-test. Results: A total of 798 observations were recorded (N=14). Human-application agreement was 60.52%. Cohen’s kappa was 0.31 (fair agreement). There were no false positive identifications by EloquentAid. Noun-type was felt to affect human error (i.e. “knocker,” “yolk,” “trellis”). Accent and pronunciation were felt to affect EloquentAid errors. Conclusions: EloquentAid is a promising tool to facilitate intraoperative testing and brain mapping using AI for speech recognition and response evaluation. Preliminary data shows fair human-app agreements. Improvements in test items and pronunciation recognition may be made.

Automated Test Case Generation for Path Coverage by Using Multi-Objective Particle Swarm Optimization Algorithm with Reinforcement Learning and Relationship Matrix Strategies

Software testing is an important phase in software life cycle, and test case generation occupies the main part of the testing workload. Path coverage is the most stringent and difficult coverage criteria, so automatic test case generation for path coverage (ATCG-PC) is an important task in software testing. ATCG-PC is usually abstracted as an optimization search problem, and the commonly used solution algorithm is swarm intelligence algorithm. This paper proposes a path coverage test case generation algorithm based on improved multi-objective particle swarm optimization (IMOPSO) algorithm. The algorithm uses the multi-objective strategy to generate test cases for a group of similar paths at a time, and is supplemented by the particle action selection strategy based on reinforcement learning and the secondary search algorithm based on relationship matrix, which makes use of the similarity information between paths to speed up the early convergence and help to quickly reach the optimal solution around the local optimal position. By grouping the paths according to the similarity, the algorithm reduces the test case consumption, and improves the efficiency of test case generation. Experimental results show that, compared with the existing methods, the proposed algorithm can achieve higher path coverage with less test case consumption and is also suitable for large-scale programs.

Design and Realization of Fully Automatic Pump Performance Test System

With the development of automation technology, it gradually replaces manual labor in various fields. Pumps are widely used in various production processes in the industrial field. To enhance the automation level of the pump production and testing process will significantly reduce production costs. This paper aims to design and realize a set of fully automatic, high-precision pump performance testing system. The fully automated operation of the performance test is designed according to the flow rate automatic sampling method, to achieve the accurate measurement of the pump operating conditions. Utilizing the PID algorithm, the valve is automatically adjusted through PLC control to realize the accurate positioning of the flow measurement point. With the help of the software program to carry out the calculation and analysis of test data, to achieve the visualization of the test process of the whole performance test data management. At the same time, SQL Server database is used to realize rapid query and management of test data. After the test verification, the system realizes the full-automatic measurement of the pump performance curve, and the measurement accuracy conforms to the standard of ISO 9906:2012.

Experiment and simulation study on the functional fatigue of shape memory alloy beam actuators

The functional fatigue behavior of shape memory alloy (SMA) beam actuators is gaining importance as their utilization in engineering applications becomes more widespread. However, research on the functional fatigue behavior of SMA beam actuators under bending conditions is not as extensive as that on SMA wires. In this paper, an experimental study and theoretical analysis of the functional fatigue behavior of SMA beam actuators were conducted. A measuring method for bending deflection and an automatic thermal cyclic test bench was designed. A series of functional fatigue tests were conducted on SMA beam actuators under different bias load conditions and the functional fatigue patterns were obtained. The material damage factor is defined and calculated through specimen tests. By incorporating the damage factor to modify the existing constitutive model, a model considering functional fatigue and tension-compression asymmetry is obtained. Finite element analysis (FEA) is performed based on this modified model to simulate the actuating performance of SMA beam actuators and to compute the deflection at different numbers of cycles. Additionally, a small parameter study on the actuator shape is conducted using FEA. By comparing the FEA results with functional fatigue experimental data, the effectiveness of the modified model is validated, demonstrating its capability to describe functional fatigue and predict actuator lifespan.

Toward granular search-based automatic unit test case generation

Unit testing verifies the presence of faults in individual software components. Previous research has been targeting the automatic generation of unit tests through the adoption of random or search-based algorithms. Despite their effectiveness, these approaches aim at creating tests by solely optimizing metrics like code coverage, without ensuring that the resulting tests have granularities that would allow them to verify both the behavior of individual production methods and the interaction between methods of the class under test. To address this limitation, we propose a two-step systematic approach to the generation of unit tests: we first force search-based algorithms to create tests that cover individual methods of the production code, hence implementing the so-called intra-method tests; then, we relax the constraints to enable the creation of intra-class tests that target the interactions among production code methods. The assessment of our approach is conducted through a mixed-method research design that combines statistical analyses with a user study. The key results report that our approach is able to keep the same level of code and mutation coverage while providing test suites that are more structured, more understandable and aligned to the design principles of unit testing.

Robotic Process Automation Efficiency for Mobile App Testing: An Empirical Investigation

In today’s rapidly evolving software environment, the graphical user interface (GUI) plays a crucial role in providing intuitive, user-friendly interaction. However, traditional intrusive GUI testing methods often face challenges such as interrupting user workflows, requiring significant manual effort and insufficient test scenario coverage. Non-intrusive testing methods, such as Robotic Process Automation (RPA), offer a solution to validate GUI functionality without modifying the application’s code or affecting the user experience. RPA systems automate repetitive tasks by simulating user interactions, becoming valuable tools in GUI testing. However, challenges like limited computational resources, time constraints, or restricted exploration capabilities may limit the efficiency of individual RPA agents, thus restricting coverage and effectiveness. To address this issue, this study explores the performance of a single RPA agent versus an RPA cluster under different testing conditions, using three popular testing methods: Monkey, Stoat and Q-testing. Experimental results indicate that an RPA cluster outperforms a single RPA in GUI coverage and error detection, making a significant contribution to the field of non-intrusive GUI exploration testing. The findings of this study provide directions for future research to ensure the delivery of high-quality mobile applications.

BFTDiagnosis: An automated security testing framework with malicious behavior injection for BFT protocols

In peer-to-peer computing networks, Byzantine Fault Tolerance (BFT) protocols are a popular solution to ensure the consistency security in the presence of some malicious nodes, thus are widely employed in blockchain systems. However, BFT protocols still face various security threats in practice. Currently, testing the security of BFT protocols often requires manual operations. I.e., it lacks comprehensive automation testing techniques. This makes it difficult to cover various scenarios of malicious node behaviors. Therefore, it is an urgent requirement to design an automated testing framework that can comprehensively and efficiently evaluate the security of BFT protocols.This paper proposes BFTDiagnosis, an automated security testing framework with malicious behavior injection for BFT protocols. The framework can automatically configure and initiate protocol testing tasks, and construct consensus nodes to execute different BFT protocols. During testing, consensus nodes can inject malicious behaviors based on pattern matching strategies and malicious behavior triggering mechanisms to simulate various malicious scenarios. Through an Analyzer, BFTDiagnosis can collect protocol runtime data from consensus nodes and calculate four security quantification indicators to evaluate protocol performance.We conducted a load consumption evaluation of BFTDiagnosis. The results indicate its acceptable performance. Additionally, we tested PBFT, Basic-HotStuff, and Chained-HotStuff protocols using the BFTDiagnosis framework, validating the effectiveness of the framework and the rationality of security quantification indicators. Finally, we compared it with nine related technologies and found that BFTDiagnosis is good at testing scenario comprehensiveness and fault localization capability, and can intuitively evaluate the security of BFT protocols through four quantification indicators. The above results show that BFTDiagnosis is an effective and comprehensive security testing framework for BFT protocols.

Novel Feature-Based Difficulty Prediction Method for Mathematics Items Using XGBoost-Based SHAP Model

The level of difficulty of mathematical test items is a critical aspect for evaluating test quality and educational outcomes. Accurately predicting item difficulty during test creation is thus significantly important for producing effective test papers. This study used more than ten years of content and score data from China’s Henan Provincial College Entrance Examination in Mathematics as an evaluation criterion for test difficulty, and all data were obtained from the Henan Provincial Department of Education. Based on the framework established by the National Center for Education Statistics (NCES) for test item assessment methodology, this paper proposes a new framework containing eight features considering the uniqueness of mathematics. Next, this paper proposes an XGBoost-based SHAP model for analyzing the difficulty of mathematics tests. By coupling the XGBoost method with the SHAP method, the model not only evaluates the difficulty of mathematics tests but also analyzes the contribution of specific features to item difficulty, thereby increasing transparency and mitigating the “black box” nature of machine learning models. The model has a high prediction accuracy of 0.99 for the training set and 0.806 for the test set. With the model, we found that parameter-level features and reasoning-level features are significant factors influencing the difficulty of subjective items in the exam. In addition, we divided senior secondary mathematics knowledge into nine units based on Chinese curriculum standards and found significant differences in the distribution of the eight features across these different knowledge units, which can help teachers place different emphasis on different units during the teaching process. In summary, our proposed approach significantly improves the accuracy of item difficulty prediction, which is crucial for intelligent educational applications such as knowledge tracking, automatic test item generation, and intelligent paper generation. These results provide tools that are better aligned with and responsive to students’ learning needs, thus effectively informing educational practice.

3022: Automation of the Rotation Period quality control test of a Tomotherapy linear accelerator

3022: Automation of the Rotation Period quality control test of a Tomotherapy linear accelerator

Development of an Automated Morphometric Approach to Assess Vascular Outcomes following Exposure to Environmental Chemicals in Zebrafish.

Disruptions in vascular formation attributable to chemical insults is a pivotal risk factor or potential etiology of developmental defects and various disease settings. Among the thousands of chemicals threatening human health, the highly concerning groups prevalent in the environment and detected in biological monitoring in the general population ought to be prioritized because of their high exposure risks. However, the impacts of a large number of environmental chemicals on vasculature are far from understood. The angioarchitecture complexity and technical limitations make it challenging to analyze the entire vasculature efficiently and identify subtle changes through a high-throughput in vivo assay. We aimed to develop an automated morphometric approach for the vascular profile and assess the vascular morphology of health-concerning environmental chemicals. High-resolution images of the entire vasculature in Tg(fli1a:eGFP) zebrafish were collected using a high-content imaging platform. We established a deep learning-based quantitative framework, ECA-ResXUnet, combined with MATLAB to segment the vascular networks and extract features. Vessel scores based on the rates of morphological changes were calculated to rank vascular toxicity. Potential biomarkers were identified by vessel-endothelium-gene-disease integrative analysis. Whole-trunk blood vessels and the cerebral vasculature in larvae exposed to 150 representative chemicals were automatically segmented as comparable to human-level accuracy, with sensitivity and specificity of 95.56% and 95.81%, respectively. Chemical treatments led to heterogeneous vascular patterns manifested by 31 architecture indexes, and the common cardinal vein (CCV) was the most affected vessel. The antipsychotic medicine haloperidol, flame retardant 2,2-bis(chloromethyl)trimethylenebis[bis(2-chloroethyl) phosphate], and tert-butylphenyl diphenyl phosphate ranked as the top three in vessel scores. Pesticides accounted for the largest group, with a vessel score of , characterized by a remarkable inhibition of subintestinal venous plexus and delayed development of CCV. Multiple-concentration evaluation of nine per- and polyfluoroalkyl substances (PFAS) indicated a low-concentration effect on vascular impairment and a positive association between carbon chain length and benchmark concentration. Target vessel-directed single-cell RNA sequencing of cells from larvae treated with , perfluorohexanesulfonic acid, or benzylbutyl phthalate, along with vessel-endothelium-gene-disease integrative analysis, uncovered potential associations with vascular disorders and identified biomarker candidates. This study provides a novel paradigm for phenotype-driven screenings of vascular-disrupting chemicals by converging morphological and transcriptomic profiles at a high-resolution level, serving as a powerful tool for large-scale toxicity tests. Our approach and the high-quality morphometric data facilitate the precise evaluation of vascular effects caused by environmental chemicals. https://doi.org/10.1289/EHP13214.