Conventional Testing Methods Research Articles

Detection of near-surface defects in viscoelastic material based on focused ultrasound thermal effects

Viscoelastic materials will absorb and dissipate energy under stress, resulting in energy loss and heat generation. The conventional non-destructive testing methods have certain limitations when it comes to detecting near-surface defects in viscoelastic materials. In this paper, a detection method of near-surface defects based on focused ultrasonic thermal effect is proposed. Firstly, the difference in thermal effects caused by defective and non-defective regions of the material under ultrasound is analyzed according to the stress response equation of viscoelastic materials, and the detection principle is elucidated. Secondly, the feasibility of this method is verified through finite element simulation with an example of plexiglass material Subsequently, the variations in the surface temperature distribution of defective specimens with varying diameters and depths are analyzed. Finally, experimental validation reveals that ultrasonic waves operating at 1.12 MHz successfully detect artificial defects with a diameter of 1 mm. With the increase of the equivalent diameter of the defect, the width of the low-temperature depression area in the surface temperature field exhibits a linear increase relationship. With the increase of the defect depth, the surface temperature difference between the corresponding position of the defective and the surrounding non-defective area gradually decreases. This method effectively overcomes the half-wavelength limitation and introduces a novel detection approach for near-surface defect identification in viscoelastic materials such as plexiglass.

Calibration of a constitutive model for polycrystalline diamond sintering process

Polycrystalline diamond is mostly pressed at high temperature in actual production. Thus, circumferential strain data of powder moulding are difficult to collect at high temperatures. As a result, the parameters of DPC constitutive model cannot be obtained using conventional testing methods. Moreover, the use of conventional equipment to test the mechanical properties becomes challenging because of the extremely high hardness and compressive strength of polycrystalline diamond. Sintering tests of polycrystalline diamond were carried out at 1360 °C and 223 Mpa using the spark plasma sintering (SPS) method, and displacement–load data were obtained. The spark plasma sintering experiment of polycrystalline diamond under high temperature and pressure was carried out using the Drucker Prager/Cap (DPC) model with relative density to obtain accurate displacement and load numbers. Combined with USDFLD subroutine, the experiment of polycrystalline diamond sintering was simulated on ABAQUS. Based on the complex method, ABAQUS-MATLAB platform is used for the reverse identification of constitutive parameters. The constitutive model parameters of polycrystalline diamond sintering process are obtained. This approach can describe the mechanical behaviour of polycrystalline diamond powder in the sintering process. In addition, the inverse identification method of the constitutive model in the sintering process of polycrystalline diamond based on ABAQUS and MATLAB solves the problem on the difficulty to observe the sintering of polycrystalline diamond under high temperature and high-pressure environment. Moreover, the constitutive model parameters of the sintering process are obtained, thereby reducing the cost and condition constraints in the actual experiment. A novel technique involves obtaining powder constitutive parameters at high temperature from the sintering point of view. This approach is important to simulate sintering of polycrystalline diamond and its composite products. Foundation itemNational Natural Science Foundation of China (Grant No. 52075438), the Key Research and Development Program of Shanxi Province of China (Grant No. 2020GY-106), the Open Project of State Key Laboratory for Manufacturing Systems Engineering (Grant No. sklms2020010), and the Open Research Fund of Key Laboratory of Oil & Gas Equipment of Ministry of Education (Grant No. OGE201702-110).

Prediction of antimicrobial resistance in Klebsiella pneumoniae using genomic and metagenomic next-generation sequencing data.

Klebsiella pneumoniae is a significant pathogen with increasing resistance and high mortality rates. Conventional antibiotic susceptibility testing methods are time-consuming. Next-generation sequencing has shown promise for predicting antimicrobial resistance (AMR). This study aims to develop prediction models using whole-genome sequencing data and assess their feasibility with metagenomic next-generation sequencing data from clinical samples. On the basis of 4170 K. pneumoniae genomes, the main genetic characteristics associated with AMR were identified using a LASSO regression model. Consequently, the prediction model was established, validated and optimized using clinical isolate read simulation sequences. To evaluate the efficacy of the model, clinical specimens were collected. Four predictive models for amikacin, ciprofloxacin, levofloxacin, and piperacillin/tazobactam, initially had positive predictive values (PPVs) of 92%, 98%, 99%, 94%, respectively, when they were originally constructed. When applied to clinical specimens, their PPVs were 96%, 96%, 95%, and 100%, respectively. Meanwhile, there were negative predictive values (NPVs) of 100% for ciprofloxacin and levofloxacin, and 'not applicable' (NA) for amikacin and piperacillin/tazobactam. Our method achieved antibacterial phenotype classification accuracy rates of 95.92% for amikacin, 96.15% for ciprofloxacin, 95.31% for levofloxacin and 100% for piperacillin/tazobactam. The sequence-based prediction antibiotic susceptibility testing (AST) reported results in an average time of 19.5 h, compared with the 67.9 h needed for culture-based AST, resulting in a significant reduction of 48.4 h. These preliminary results demonstrated that the performance of prediction model for a clinically significant antimicrobial-species pair was comparable to that of phenotypic methods, thereby encouraging the expansion of sequence-based susceptibility prediction and its clinical validation and application.

Inspection of aluminum sheets using a multi-element eddy current sensor: 2d and 3d imaging of surface defects of various sizes and internal defects at various depths

In the industrial sector, ensuring reliability and durability is of paramount importance. Our research aims to advance beyond conventional non-destructive testing methods by focusing on thorough defect detection and imaging. We utilize advanced, sensor-enhanced eddy current testing, featuring multiple elements arranged in a cutting-edge serial array. This innovative configuration addresses the issue of magnetic repulsion between sensor elements, thereby speeding up the testing process and ensuring precise results through both 3D and 2D imaging. This sophisticated approach allows us to more effectively characterize defects of varying sizes and depths in aluminum sheets. By meticulously collecting and analyzing data from the sensors, we can identify the appearance and nature of these defects with greater clarity. Our findings introduce a pioneering method for defect detection, highlighting the efficacy of our advanced testing technique. Our research underscores the potential of multi-element eddy current sensors in revolutionizing the inspection process. The ability to produce detailed 3D and 2D images of surface and internal defects represents a significant leap forward in non-destructive testing. This comprehensive imaging capability not only accelerates the detection process but also enhances the accuracy and reliability of defect characterization. By employing this state-of-the-art technology, we can detect even the smallest and most deeply embedded defects that traditional methods might miss. The precise imaging provided by our approach ensures that defects of various sizes and depths are accurately identified and characterized. This level of detail is crucial for maintaining the structural integrity and performance of aluminum sheets used in industrial applications. Our research demonstrates a groundbreaking approach to defect detection in aluminum sheets, leveraging the advanced capabilities of multi-element eddy current sensors. The innovative use of a serial array of sensors, combined with sophisticated data analysis techniques, allows for rapid, accurate, and detailed imaging of defects. These findings pave the way for improved reliability and durability in industrial applications, setting a new standard for non-destructive testing.

In vitro evaluation of using ceftazidime/avibactam against carbapenem-resistant Acinetobacter baumannii

ObjectiveCarbapenem-resistant Acinetobacter baumannii (CRAB) is a global concern as effective treatments are very limited. We previously used a modified susceptibility testing approach to predict growth suppression in carbapenem-resistant Enterobacterales, but there are uncertainties about the generalizability of the model. The objective of this study is to verify if a similar approach can be extended to CRAB. MethodA clinical isolate of CRAB resistant to ceftazidime/avibactam (CAZ/AVI, MIC = 32/4 mg/L) was examined. CAZ susceptibility was determined using increasing concentrations of AVI (0–64 mg/L), and MIC reduction was characterized with a sigmoid inhibitory maximum effect (Emax) model. The effectiveness of CAZ/AVI was validated in a hollow fibre infection model (HFIM) over 72 hours, using simulated unbound serum / epithelial lining fluid (ELF) exposures of 2.5 g over 2 hours every 8 hours. Baseline inocula of approximately 5.5 log CFU/mL were examined. ResultsAn AVI concentration-dependent reduction in CAZ MIC was observed (r2 = 0.99). CAZ MIC was dramatically reduced from 512 mg/L (no AVI) to 32 mg/L (AVI = 4 mg/L), and further to 8 mg/L (AVI = 16 mg/L). Pharmacokinetic simulations were satisfactory in the HFIM (r2 > 0.96). Bacterial suppression was observed >24 hours with the serum exposure, but not that from the ELF. ConclusionUsing multiple AVI concentrations within the clinically relevant range, our susceptibility testing approach could have better insights of treatment outcome for infections caused by CRAB. This could potentially lead to effective intervention(s) overlooked by conventional susceptibility testing method. This case highlights the importance of site-specific drug exposures on determining treatment outcome.

A Pragmatic Approach for Rapid, Non-Destructive Assessment of Defect Types in Laser Powder Bed Fusion Based on Melt Pool Monitoring Data.

Process monitoring systems, e.g., systems based on photodiodes, could be used in laser-based powder bed fusion (PBF-LB/M) to measure various process parameters and process signatures to eventually allow for a local, detailed analysis of the produced parts. Here, simple statements only concerning the occurrence of defects in parts are sufficient in many cases, especially with respect to industrial application. Therefore, a pragmatic approach to rapidly infer the occurrence of defects and their types based on in situ data obtained by commercially available process monitoring systems is introduced. In this approach, a color distribution in form of a histogram is determined for each produced part using layer-wise screenshots of the visualized data provided by the monitoring software. Assessment of the histograms of AlSi10Mg samples, which were processed with different parameter combinations, revealed characteristics depending on the prevailing defect types. These characteristics enable the prediction of the occurring defect types without the necessity to apply conventional downstream testing methods, and thus, a straightforward separation of parts with good quality from defective components. Since the approach presented uses the data visualization of the monitoring software, it can be used even when direct access to the raw data is not provided by the machine manufacturer.

Surface characterization of consolidated earthen substrates using an innovative multi-analytical strategy

This study evaluates the combined use of innovative non-destructive methods and standard laboratory techniques for assessing the surface effects of various consolidation treatments (i.e., bacterial biomineralization, alkaline activation, colloidal dispersions containing nanolime or nanosilica, and ethyl silicate as a standard) on earthen building materials. It demonstrates that novel portable equipment (i.e., portable digital microscope, air permeameter, and mobile surface (contact angle) analyzer) and advanced strategies for the application and data interpretation of conventional analytical and testing methods (i.e., scanning electron and confocal microscopy, Leeb hardness testing, and visible light spectrophotometry) can yield complementary and extremely valuable quantitative results for the evaluation of consolidation treatments on such complex substrates. Our multi-analytical approach allows a detailed characterization of surface and near-surface features of treated earthen mock-ups. It is possible to disclose significant differences in the film-forming propensity and in-depth distributions of consolidants and their effect on key properties such as surface roughness and appearance, permeability, water behavior, and mechanical properties. Even though, the substrate's inhomogeneity and surface texture has proven to be especially challenging, this methodology offers a promising strategy for long-term in-situ monitoring of treatment performance and weathering behavior of built heritage materials.

General Applicability of Existing College of American Pathologists Accreditation Requirements to Clinical Implementation of Machine Learning-Based Methods in Molecular Oncology Testing.

The College of American Pathologists (CAP) accreditation requirements for clinical laboratory testing help ensure laboratories implement and maintain systems and processes that are associated with quality. Machine learning (ML)-based models share some features of conventional laboratory testing methods. Accreditation requirements that specifically address clinical laboratories' use of ML remain in the early stages of development. To identify relevant CAP accreditation requirements that may be applied to the clinical adoption of ML-based molecular oncology assays, and to provide examples of current and emerging ML applications in molecular oncology testing. CAP accreditation checklists related to molecular pathology and general laboratory practices (Molecular Pathology, All Common and Laboratory General) were reviewed. Examples of checklist requirements that are generally applicable to validation, revalidation, quality management, infrastructure, and analytical procedures of ML-based molecular oncology assays were summarized. Instances of ML use in molecular oncology testing were assessed from literature review. Components of the general CAP accreditation framework that exist for traditional molecular oncology assay validation and maintenance are also relevant for implementing ML-based tests in a clinical laboratory. Current and emerging applications of ML in molecular oncology testing include DNA methylation profiling for central nervous system tumor classification, variant calling, microsatellite instability testing, mutational signature analysis, and variant prediction from histopathology images. Currently, much of the ML activity in molecular oncology is within early clinical implementation. Despite specific considerations that apply to the adoption of ML-based methods, existing CAP requirements can serve as general guidelines for the clinical implementation of ML-based assays in molecular oncology testing.

Prediction of Epidermal Growth Factor Receptor Mutation Subtypes in Non–Small Cell Lung Cancer From Hematoxylin and Eosin–Stained Slides Using Deep Learning

Accurate assessment of epidermal growth factor receptor (EGFR) mutation status and subtype is critical for the treatment of non–small cell lung cancer patients. Conventional molecular testing methods for detecting EGFR mutations have limitations. In this study, an artificial intelligence–powered deep learning framework was developed for the weakly supervised prediction of EGFR mutations in non–small cell lung cancer from hematoxylin and eosin–stained histopathology whole-slide images. The study cohort was partitioned into training and validation subsets. Foreground regions containing tumor tissue were extracted from whole-slide images. A convolutional neural network employing a contrastive learning paradigm was implemented to extract patch-level morphologic features. These features were aggregated using a vision transformer-based model to predict EGFR mutation status and classify patient cases. The established prediction model was validated on unseen data sets. In internal validation with a cohort from the University of Science and Technology of China (n = 172), the model achieved patient-level areas under the receiver-operating characteristic curve (AUCs) of 0.927 and 0.907, sensitivities of 81.6% and 83.3%, and specificities of 93.0% and 92.3%, for surgical resection and biopsy specimens, respectively, in EGFR mutation subtype prediction. External validation with cohorts from the Second Affiliated Hospital of Anhui Medical University and the First Affiliated Hospital of Wannan Medical College (n = 193) yielded patient-level AUCs of 0.849 and 0.867, sensitivities of 79.2% and 80.7%, and specificities of 91.7% and 90.7% for surgical and biopsy specimens, respectively. Further validation with The Cancer Genome Atlas data set (n = 81) showed an AUC of 0.861, a sensitivity of 84.6%, and a specificity of 90.5%. Deep learning solutions demonstrate potential advantages for automated, noninvasive, fast, cost-effective, and accurate inference of EGFR alterations from histomorphology. Integration of such artificial intelligence frameworks into routine digital pathology workflows could augment existing molecular testing pipelines.

Correlated high throughput nanoindentation mapping and microstructural characterization of wire and arc additively manufactured 2205 duplex stainless steel

Duplex stainless steels (DSS) in wire and arc additive manufacturing (WAAM) have attracted significant research attention due to their mechanical properties and corrosion resistance. This study uses conventional and nanomechanical testing methods to compare the mechanical and microstructural behaviors at macroscopic and microscopic length scales. Macro hardness (HV10) testing yielded 259 and 249 in low and high heat input (HI) samples, respectively, while ferrite content averaged 52.7 and 48.5%. However, these results fail to provide conclusive insight into the potential influence of microstructural variations at the macroscopic level, likely due to the composite response of the material. To overcome this limitation, the mechanical response of the DSS samples is assessed at the grain level via high throughput nanoindentation mapping with image processing to track the location of each indent. This approach enabled differentiating the indents landing on ferrite and austenite phases as well as those landing on the interfaces. The results showed that the austenite phase had higher hardness (4.30 and 4.35 GPa) than the ferrite phase (3.89 GPa and 4.03 GPa) for high and low HI samples, respectively. The observed differences in hardness between the phases can be attributed to higher nitrogen content in the austenitic phase.

Sensitivity, robustness, and reproducibility of U-shaped delamination test for evaluation of candidate ultra-high molecular weight polyethylene materials for joint replacements.

The delamination of ultra-high molecular weight polyethylene (UHMWPE) in artificial joints is a major cause limiting the long-term clinical results of arthroplasty. However, the conventional test method using simple reciprocation to evaluate the delamination resistance of UHMWPE materials has insufficient detection sensitivity. To reproduce delamination, the unconformity contact must be maintained throughout the test so that the maximum stress is generated below the surface. Therefore, a test method that applies a U-shaped motion comprising two long-linear and one short linear sliding motion was developed. The sensitivity, robustness, and reproducibility of the U-shaped delamination test were investigated and compared with the traditional test method. The traditional test method could reproduce delamination only in materials that had degraded considerably, whereas the U-shaped delamination test could reproduce delamination in a wide range of materials, demonstrating its superior sensitivity. Additionally, using a higher load helped accelerate the test without affecting the test results. The optimal length of the short linear sliding motion was confirmed to be 1 mm. Finally, the inter-laboratory reproducibility of the U-shaped delamination test was confirmed using the round-robin test. The U-shaped delamination test demonstrates high sensitivity, robustness, and reproducibility and contributes to the selection and development of UHMWPE materials and artificial joints with a lower risk of delamination.

Non-Contact Magnetometric Diagnostics of Pipelines Using the Metal Magnetic Memory Method

The non-contact magnetometric diagnostics (NCMD) of buried pipelines (oil and gas pipelines, heat lines, water supply lines, etc.) has been developed by Energodiagnostika Co. Ltd. since 2000 based on the 25-year experience of the metal magnetic memory (MMM) method application (ISO 24497-1:2007(E)). NCMD is based on measurement along the pipeline route of the Earth’s magnetic field intensity (HEarth) distortions caused by variation of the pipe metal magnetization in stress concentration zones (SCZs) and in zones of developing corrosion-fatigue damages. The paper describes the experience of complex diagnostics of buried pipelines based on NCMD, the MMM method and conventional methods of non-destructive testing. Based on NCMD results, the most strained pipeline sections are determined for their opening and additional control by the MMM method, ultrasonic testing and other NDT methods. On opened pipeline sections the MMM method is used to determine the presence of defects and the mechanical properties of the metal by hardness parameters. Inadmissible defects are removed, and the actual mechanical properties (yield strength and ultimate strength) are taken into account in check strength calculations. The inspection should provide the answer to the following question: “Where and when should a damage or an accident be expected?”. If this task is solved, then the possibility of timely replacement or repair of a potentially hazardous section is provided. Application of NCMD in combination with additional inspection of pipelines (UT, eddy current, etc.) in prospect holes determined by NCMD is aimed exactly at solution of this problem. The paper presents the experience of NCMD of buried polyurethane foam insulated pipelines in order to detect girth welded joints with developing damage.

Using cerebrospinal fluid nanopore sequencing assay to diagnose tuberculous meningitis: a retrospective cohort study in China

ObjectiveThis study aimed to evaluate the efficiency of nanopore sequencing for the early diagnosis of tuberculous meningitis (TBM) using cerebrospinal fluid and compared it with acid-fast bacilli (AFB) smear, mycobacterial...

Evaluation of AAICare®-TB sequence analysis tool for accurate diagnosis of drug-resistant tuberculosis: A comparative study with TB-Profiler and Mykrobe

A rapid and comprehensive drug susceptibility test is essential for eliminating drug resistant tuberculosis. Next generation sequencing (NGS) based susceptibility testing is being explored as a potential substitute for the conventional phenotypic and genotypic testing methods. However, the adoption of NGS based genotypic susceptibility testing depends on the availability of simple, accurate and efficient analysis tools. This preliminary study aimed to evaluate the performance of a Mycobacterium tuberculosis (Mtb) genome analysis pipeline, AAICare®-TB, for susceptibility prediction, in comparison to two widely used gDST prediction tools, TB-Profiler and Mykrobe. This study was performed in a National Reference Laboratory in India on presumptive drug-resistant tuberculosis (DR-TB) isolates. Whole genome sequences of the 120 cultured isolates were obtained through Illumina sequencing on a MiSeq platform. Raw sequences were simultaneously analysed using the three tools. Susceptibility prediction reports thus generated, were compared to estimate the total concordance and discordance. WHO mutation catalogue (1st edition, 2021) was used as the reference standard for categorizing the mutations. In this study, AAICare®-TB was able to predict drug resistance status for First Line (Streptomycin, Isoniazid, Rifampicin, Ethambutol and Pyrazinamide) and Second Line drugs (Fluoroquinolones, Second Line Injectables and Ethionamide) in 93 samples along with lineage and hetero-resistance as per the WHO guidelines.

Towards a defossilized building sector with field tests in the lab: Review, development, and evaluation

The transformation process in the building sector towards future city concepts requires changes in our society that aim for significant emission reduction until 2045. Currently, buildings in Germany are constructed and operated in such a way that no changes need to be made to them for decades. Therefore, any upcoming changes that do not achieve the climate goals must be avoided immediately. However, making the optimal decision for a change is complex due to (a) multi-domain (e.g., design and control), (b) multi-scale (e.g., single-family building and urban energy system), (c) multi-stakeholder (e.g., users and practice), and (d) long-term implications (up to decades). Conventional testing facilities and methods do not account for the integration of (a)-(d). Therefore, their technology readiness level is limited by 6. This paper introduces an approach that aims at (1) a multi-domain and multi-scale approach by applying process systems engineering to the building sector and that aims at (2) stakeholder integration already in the research process by applying the living lab approach. We combine the two methods, yielding a field test in the lab infrastructure pushing the technology readiness level to 7–8. In all, it consists of five test benches (Refrigerant Cycle Lab, FlexFass, RT-Lab, Raumklimalabor, and InFis). This work focuses on the Refrigerant Cycle Lab. Its concept is based on two further Hardware-in-the-Loop test benches which are extended by a safety system that ensures automated operation dedicated to heat pumps with flammable refrigerants. In two case studies, we show that field tests in the lab allow multi-domain and multi-scale testing. Both aspects support accelerating the transformation process towards a defossilized building sector. In the next step, the combination of test benches at different locations is promising to reduce the need to set up identical experiments at different locations. In the long term, the laboratory is designed to include external stakeholders to avoid time-consuming iterations and can, therefore, save resources, time, and money for our complex transformation process.

Screening and Diagnosis of Rare Thalassemia Variants: Is Third-Generation Sequencing Enough?

Rare thalassemia subtypes are often undiagnosed because conventional testing methods can only identify 23 common types of α- and β-thalassemia. To assess a comprehensive approach for the screening and diagnosis of rare thalassemia. The study cohort included 72 individuals with suspected rare thalassemia variants. Screening was conducted by next-generation sequencing (NGS) combined with third-generation sequencing (TGS) and chromosomal microarray analysis (CMA)/copy number variation sequencing. Of the 72 individuals with suspected rare thalassemia, 49 had rare α- or β-gene variants. NGS combined with gap polymerase chain reaction detected a total of 42 cases, resulting in a positive detection rate of 58.3%. Additionally, 4 α-globin genetic deletions were identified by TGS, which increased the variant detection rate by 5.6%. Two samples with a microdeletion of chromosome 16 or 11 were detected by CMA, which increased the detection rate by 2.8%. For one sample, reanalysis of the NGS and TGS data confirmed the presence of the β41-42/βN and βN/βN mosaic. The HBB:c.315 + 2delT mutation was initially reported in Guangdong Province, China. Two HBB gene mutations (HBB:c.315 + 5G>C and HBB:c.295G>A) and 4 rare HBA gene deletions (-11.1, -α27.6, -α2.4, and -α21.9) were initially identified in the Zhonshan region. The hematologic phenotypes of all rare cases in this study were clarified. Rare thalassemia variants are more common than previously thought. Despite advancements in TGS, there is still no foolproof method for detection of all types of thalassemia. Thus, a comprehensive approach is necessary for accurate screening and diagnosis of rare thalassemia variants.

Preliminary study on terahertz non-destructive testing for defect detection in hot melt joints of polyethylene pipes

With the rapid development of the economy and society, non-metallic pipelines are more and more widely used in oil and gas fields, water supply projects, urban gas systems, and so on. Among the conventional nondestructive testing (NDT) methods, only ultrasonic, radiographic, and penetration testing tools are applicable to non-metallic pipe examination from a theoretical perspective, while their actual efficiency is quite low. In this work, terahertz (THz) non-destructive detection of typical welding defects in polyethylene-pipe hot melt joints is carried out. The characteristic THz wave signal is obtained, and the relationship between the THz wave and the defect type is established. Finally, a THz signal processing model for defect detection in polyethylene-pipe hot melt joints is established. Thus, this research lays a foundation for the application of the THz non-destructive testing technology to polyethylene pipeline engineering.

Flow-controlled thermal response test and its comparison with the conventional test methods

A novel flow-controlled (FC) thermal response test (TRT) system is introduced to resolve the recently addressed inconsistency between the constant heat flux (CHF) and constant temperature (CT) TRTs. FC-TRT allows us to keep both inlet and outlet temperatures constant and improve the accuracy of CT-TRT. Using the FC-TRT system, four types of TRT experiments are performed, providing CT, CHF, and constant inlet temperature conditions, besides the novel one keeping both temperature and heat flux constant. Thermal conductivities from these TRT measurements are compared, and a good agreement is observed. FC-TRT offers higher accuracy and various TRT applications in one platform.

Measuring Color Index of Transformer Oil–Enabling Single-Wavelength Spectroscopy With Artificial Neural Network-Fuzzy Logic Model

Conventionally, the color index of transformer oil is determined by a color comparator based on the American Society for Testing and Materials (ASTM) D 1500 standard. The equipment requires humans to operate, which leads to human error and limited number of samples tested per day. This work proposes the utilization of single-wavelength spectroscopy with 405 nm laser diode using artificial neural network (ANN) to determine the color index of transformer oil. Two ANN models were developed using data collected from 50 oil samples with different optical pathlengths of 1 to 10 mm, and laser output powers of 1 to 15 mW. The first model classified the input into different color indices and another model correlated the input parameters through regression analysis to determine the color index. A hybrid ANN-fuzzy logic model was also developed to improve the color index prediction. The root-mean-squared error (RMSE) obtained from the prediction by ANN regressor and ANN classifier are 0.5602 and 0.6416, respectively. The hybrid ANN-fuzzy logic model improves the RMSE especially for optical pathlengths <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula> 5 mm, which is required for measuring samples with high color index. This proposed method reduces the dependency on complex optoelectronic hardware to obtain highly accurate results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Unlike the conventional testing method for color index of transformer oil that requires human observation, the findings of this study enables the possibility of compact and smart portable device through the utilization of single wavelength spectroscopy with machine learning models. With no human involvement, more computational power with lesser hardware dependency, the maintenance cost and error can be reduced. This proposed method can potentially be applied to measure the color of other amber-colored liquid products such as olive oil, honey and others.

Stiffness and pre-stretching estimation from indentation test of hyperelastic membrane

Obtaining precise data on the mechanical properties of soft materials, often in the form of thin, pre-tensioned membranes, is crucial, especially when conventional testing methods have limited applicability. The article focuses on an innovative method for estimating the Young's modulus and pre-stretching the membranes using the indentation method, assuming an isotropic, incompressible hyperelastic material. The parameters were estimated for two models of indenter-membrane contact: with perfect slip and without slip. Experimental tests were performed for pre-stretched latex membranes glued to a stiffer ring to minimize the effect of attachment on membrane deformation. In order to validate the estimation method, the predictions of the model with the determined mechanical parameters were compared with the results of indentation tests with a liquid layer under the membrane. The discussion shows the consistency of the estimation results with the literature results for small latex deformations, and indicates the advantages and numerous limitations of the approach, especially related to the choice of the material model.