Performance Verification Research Articles

Seismic performance verification and theoretical stiffness calculation for a novel assembled concrete dry-connected beam-column node

This paper proposes a novel assembled concrete dry-connected beam-column node, which consists of a precast concrete beam with steel haunch plates at the end and a precast concrete column connected by ordinary strength long bolts. The quasi-static test study and finite element numerical simulation of the node are used to analyze the hysteresis curve, skeleton curve, and other performance indexes and investigate mechanical properties under low-cycle reciprocating load. The results demonstrate that the overall stress performance of the column specimen is outstanding; the specimen node possesses large deformation and good ductility, presenting semi-rigid characteristics; the damage of the specimen mainly includes the bending deformation of the connecting bolts. By analyzing the damage and force mechanisms of the node, an "equivalence method" for the initial rotational stiffness calculation of the beam-column combination node is established. Moreover, the formula for calculating the stiffness of this semi-rigid node is deduced.

Advanced intelligent monitoring technologies for animals: A survey

Effective animal intelligent monitoring is of great value in terms of ecological protection and endangered specie conservation. At present, computer vision technologies have shed light on animal intelligent monitoring. Especially, numerous deep learning-based models and methods have been developed to address various challenges, and have made substantial strides in this field. However, there are still several problems to be solved and related areas to be mined, such as exploring new strategies to enhance the robustness and generalization ability of models, designing novel models for complex environments, and establishing large-scale publicly available animal datasets for performance verification of models. Therefore, we comprehensively elaborated and analyzed existing works on animal intelligent monitoring based on advanced information science technologies, so as to provide useful information assistance for relevant researchers. In this paper, we focus on three primary task fields: precise animal localization, tracking and individual identification. Specifically, we elucidate the definition and significance of each monitoring task, and summarize the baseline models for addressing different problems. We provide a specific analysis of strategies and prototypes of the models and methods employed in each tasks following by the technical progression from traditional machine learning to deep learning. In addition, we make a comparison and analysis of the relevant methods, summarize their similarities and differences between them, and point out the advantages and disadvantages of these methods. Finally, we present several unresolved challenges and problems in animal intelligent monitoring and provide potential research directions in the future. We expect that our review can serve as reference and guidance for related research fields.

Performance evaluation and dimensional optimization design of planar 6R redundant actuation parallel mechanism

AbstractAiming at the problems of small good workspace, many singular configurations, and limited carrying capacity of non-redundant parallel mechanisms, a full-redundant drive parallel mechanism is designed and developed, and its performance evaluation, good workspace identification, and scale optimization design are studied. First, the kinematics analysis of the planar 6R parallel mechanism is completed. Then, the motion/force transmission performance evaluation index of the mechanism is established, and the singularity analysis of the mechanism is completed. Based on this, the fully redundant driving mode of the mechanism is determined, and the good transmission workspace of the mechanism in this mode is identified. Then, the mapping relationship between the performance and scale of the mechanism is established by using the space model theory, and the scale optimization of the mechanism is completed. Finally, the robot prototype is made according to the optimal scale, and the performance verification is carried out based on the research of dynamics and control strategy. The results show that the fully redundant actuation parallel mechanism obtained by design optimization has high precision and large bearing capacity. The position repeatability and position accuracy are 0.053 mm and 0.635 mm, respectively, and the load weight ratio can reach 15.83%. The research results of this paper complement and improve the performance evaluation and scale optimization system of redundantly actuated parallel mechanisms.

Molecular dynamics simulation and performance verification of γ-polyglutamic acid/cold water-soluble starch film formation and permeability

Six types of γ-polyglutamic acid (γ-PGA)/cold water-soluble starch (St) composite film models were constructed using molecular dynamics simulation, and their properties were investigated and compared with the corresponding experimental values. The compatibility between the composite film components was analyzed using the radial distribution function and mean square displacement (MSD). The hydrogen-bond number and bond energy were used to track the film-formation process. The mechanical property data of the films were extracted, and MSD was used to analyze the permeability of the film to carbon dioxide, oxygen, water vapor, and carbon-16 saturated fatty acids. Finally, the simulated values of mechanical properties and permeability were compared with the experimental values. The results demonstrated that γ-PGA is well compatible with St. The intramolecular and intermolecular hydrogen bonds of γ-PGA and St did not change considerably during the film-formation process. The simulated values of the mechanical properties exhibited a similar trend as the experimental values; however, in terms of permeability, a difference was observed between the initial values of the simulated design and actual material parameters, as well as the complexity of the experiment.

Development of a Front End Array for Broadband Phased Array Receiver

The receiver is a signal receiving device placed at the focus of the telescope. In order to improve the observation efficiency, the concept of phased array receiver has been proposed in recent years, which places a small phased array at the focal plane of the reflector, and flexible pattern and beam scanning functions can be achieved through a beamforming network. If combined with the element multiplexing, all beams within the entire field of view can be observed simultaneously to achieve continuous sky coverage. This article focuses on the front-end array of phased array receiver at 0.7–1.8 GHz for QiTai Telescope, and designs a Vivaldi antenna array of PCB structure with dual line polarization. Each polarization antenna is designed to arrange in a rectangle manner by 11 × 10. Based on the simulation results of the focal field, 32, 18, and eight elements were selected to form one beam at 0.7, 1.25, and 1.8 GHz. An analog beamforming network was constructed, and the measured gains of axial beam under uniform weighting were 19.32, 13.72, and 15.22 dBi. Combining the beam scanning method of reflector antenna, the pattern test of different position element sets required for PAF beam scanning was carried out under independent array. The pattern optimization at 1.25 GHz was carried out by weighting method of conjugate field matching. Compared with uniform weighting, the gain, sidelobe level, and main beam direction under conjugate field matching have been improved. Although the above test and simulation results are slightly different, which is related to the passive array and laboratory testing condition, the relevant work has accumulated experience in the development of the front-end array for the phased array receiver, and has good guiding significance for future performance verification after the array is installed on the telescope.

Divide-and-Aggregate Learning for Evaluating Performance on Unlabeled Data

Artificial Intelligence (AI) models have become an integral part of modern society, significantly improving human lives. However, ensuring the reliability and safety of these models is of paramount importance. One critical aspect is the continuous monitoring and verification of model performance to prevent any potential risks. Real-time online evaluation of AI models is necessary to maintain their effectiveness and mitigate any harm caused by performance degradation. The traditional approach to model evaluation involves supervised methods that rely on manual labeling to compare results with model predictions. Unfortunately, this method is not suitable for online model monitoring due to its inherent lag and high cost. While there have been attempts to explore free-label model evaluation, these approaches often consider only the global features of the entire dataset. Additionally, they can only perform model evaluation based on a single dimension of model confidence or features. In this paper, we propose a novel approach called Divide-and-Aggregate Learning (DAL) for unsupervised model evaluation. Our method addresses the limitations of previous approaches by dividing the output of the model into buckets, capturing local information of the distribution. We then aggregate this local information to obtain global information and further represent the relationship between the distribution and model performance. Importantly, our method can simultaneously handle the confidence distribution and feature distribution of the model output. Extensive experiments have been conducted to demonstrate the effectiveness of our DAL model. The results show that our approach outperforms previous methods on four widely used datasets. We will make our source code publicly available.

Investigation of select radionuclides stability in urine under various conditions for liquid scintillation counting (LSC).

Liquid Scintillation Counting (LSC) gross alpha/beta screening is a valuable tool for providing rapid laboratory response for the analysis of human clinical urine samples during a large-scale radiation incident event. Verification of method performance, as required for clinical laboratory testing, is accomplished by the evaluation of routine, periodic measurements of radioactive spiked samples for quality control, performance testing, and accuracy checks. Radionuclide stability of alpha and beta emitters in urine for LSC analysis is an important consideration. The purpose of this work is to demonstrate optimal preparations and storage conditions of samples used for method verification.

Structural optimization calculation of methanol spiral tube reformer based on waste heat utilization and experimental verification of reactor performance

Spiral tube is compact, efficient in heat transfer and can make full use of the annular space in the engine, so it can be applied to heat recovery under a variety of operating conditions. This paper developed a three-rate, one-dimensional model combined with simulations, experiments. It can be found that in the hydrogen prediction, the model has an error of 10 % from experiment and 0.91 % from literature; the increase in length from 0.5 m to 1.35 m increases methanol conversion by 7.97 %; the increase in spiral diameter from 4 mm to 7 mm increases methanol conversion by 9.35%; spiral radius from 40 mm to 70 mm, methanol conversion rate decreased by 5%; mass fraction of methanol decreased by 16 % when the pitch was increased from 5 mm to 20 mm. When diameter is 4 mm, length is 1 m, the MSR reaction rate reach 24.1 mol/m3/s, methanol conversion rate is 87.91 %. When spiral pitch is 10 mm, spiral diameter is 40 mm, methanol conversion rate can reach 91.83 %. The spiral tube reactor has a compact structure and can achieve 87.19 % methanol conversion and 0.0955 L/min H2 production at WHSV = 0.9 h−1 and S/C = 1.7.

Research on seedling sowing method based on high voltage electrostatic characteristics

In response to the difficult problem of high leakage rate and low seed pick-up rate of traditional air-suction seed dischargers. This study proposes a new high-voltage electrostatic vegetable seedling sowing method and system based on phase sucking of opposite charges and repulsive seeding of same charges. This method does away with small orifices and long conductance suction holes. It can solve the technical bottleneck of the traditional air-suction seed rower at the source, which causes a decrease in seed rowing accuracy due to clogging of the suction holes when facing irregular small vegetable seeds that are not pelleted. Combining high voltage electrostatic polarisation for seed vigour with precision seeding technology. Improving seed vigour and crop yield while precision sowing. Modelling the mechanics of adsorption seeding and repulsive seeding systems. Identify the main factors affecting electrostatic adsorption and phase repulsion forces and their interrelationships; Analysis of the decay mechanism of the electrostatic adsorption force upon repeated action. Proposing measures for the rapid restoration of the original equilibrium state of the dielectric insulation by reverse electric field polarization; Analysis of the migration pattern of electric particles during polarisation. Solving the equations for seed dynamics during sowing; At positive input voltages of 16 kV and negative input voltages of 0, 4, 8, 12, 16 and 20 kV. Separate dielectric insulation layer adsorption seeding tests. The test results show that: The reverse electric field polarisation of the dielectric insulating layer is effective in containing the electrostatic adsorption decay. The best containment was achieved at a positive input voltage of 16 kV and a negative input voltage of 16 kV; Seeding performance verification tests at a positive input voltage of 16 kV and a negative input voltage of 16 kV. The test results show that: The sowing conformity index was 90.25 %, the reseeding index 3.40 % and the missed sowing index 6.35 %. In the evaluation of applied research, the multi-objective optimization design with the objective function of maximum seed discharge qualification index, minimum leakage index and replanting index, the optimal parameter combinations of the test factors can be obtained as follows: the size of the type hole is 5.977 mm, and the output voltage of the positive high-voltage electrostatic generator is 16.423 kV, and at this time, the seeding machine's seed discharge qualification index is 90.78 %, the replanting index is 3.05 %, and the leakage index is 6.17 %. To verify the reliability of the optimal design of structural parameters, the seeding performance verification test is carried out, which can be obtained: the qualified index of row seeding is 90.54 %, the index of reseeding is 2.86 %, and the index of leakage is 6.60 %, which meets the technical requirements of precision seeding. The research not only provides a reference for the development of precision sowing machinery and equipment for irregular small-grain vegetable seeds. It also provides an academic reference for the research of quantitative adsorption and rapid separation methods and theories for irregular small particle materials.

Localizing gradient damage model for anisotropic materials: Focusing on timber

This paper presents a new damage model for transversely isotropic materials, with special focus on timber. The model proposed in this study is formulated by employing the localized gradient form of continuum damage theory. The model accounts for anisotropic damage through two damage variables, delineating damage parallel and perpendicular to the grain, representing isotropic damage within the cross-sectional plane. The conditions assumed in this work pertain to loading scenarios that occur within the plane. An additional fourth-order tensor transformation is incorporated to facilitate the conversion between the global and local reference frames, allowing for arbitrary spatial orientations of the material matrix. In this work we concentrate on damage arising from tensile and shear stresses. The model is implemented as a consistent linearized coupled (monolithic) problem.The performance verification of the model relies on numerous numerical examples, incorporating experimental benchmark problems. The outcomes demonstrate that the suggested damage model adeptly captures both the initiation of failure and the propagation of damage.

Performance evaluation and verification of post-processing methods for TIGGE ensemble data using machine learning approaches

Abstract Ensemble modelling has become a significant technique in the field of machine learning, as it utilises the combined knowledge of multiple base models to improve the accuracy of predictions in different domains. Nevertheless, the effectiveness of ensemble predictions relies on the implementation of post-processing techniques that enhance and optimize the outputs of the ensemble. This study explores the domain of ensemble data post-processing, utilizing a machine learning-focused methodology to thoroughly assess and contrast a variety of post-processing methods. TIGGE Ensemble data from ECMWF and NCEP were used from 2010 to 2020. Research covers machine learning approaches post-processing methods such as BMA, cNLR, HXLR, OLR, logreg, hlogreg, QM were applied. The probabilistic forecasts were validated using the Brier Score (BS), Area Under Curve (AUC) of Receiver Operator Characteristics (ROC) plots and reliability plots. The cNLR and BMA strategies for post-processing performed exceptionally well with BS value of 0.10 and RPS value of 0.11 at all grid points for both methods. The ROC–AUC values for the cNLR and BMA methods were found to be 91.87 and 91.82%, respectively. The results show that improved post-processing techniques can be helpful to predict the flood in advance with accurate precision and warning.

CT-based pancreatic radiomics predicts secondary loss of response to infliximab in biologically naïve patients with Crohn’s disease

ObjectivesPredicting secondary loss of response (SLR) to infliximab (IFX) is paramount for tailoring personalized management regimens. Concurrent pancreatic manifestations in patients with Crohn’s disease (CD) may correlate with SLR to anti-tumor necrosis factor treatment. This work aimed to evaluate the potential of pancreatic radiomics to predict SLR to IFX in biologic-naive individuals with CD.MethodsThree models were developed by logistic regression analyses to identify high-risk subgroup prone to SLR. The area under the curve (AUC), calibration curve, decision curve analysis (DCA), and integrated discrimination improvement (IDI) were applied for the verification of model performance. A quantitative nomogram was proposed based on the optimal prediction model, and its reliability was substantiated by 10-fold cross-validation.ResultsIn total, 184 CD patients were enrolled in the period January 2016 to February 2022. The clinical model incorporated age of onset, disease duration, disease location, and disease behavior, whereas the radiomics model consisted of five texture features. These clinical parameters and the radiomics score calculated by selected texture features were applied to build the combined model. Compared to other two models, combined model achieved favorable, significantly improved discrimination power (AUCcombined vs clinical 0.851 vs 0.694, p = 0.02; AUCcombined vs radiomics 0.851 vs 0.740, p = 0.04) and superior clinical usefulness, which was further converted into reliable nomogram with an accuracy of 0.860 and AUC of 0.872.ConclusionsThe first proposed pancreatic-related nomogram represents a credible, noninvasive predictive instrument to assist clinicians in accurately identifying SLR and non-SLR in CD patients.Critical relevance statementThis study first built a visual nomogram incorporating pancreatic texture features and clinical factors, which could facilitate clinicians to make personalized treatment decisions and optimize cost-effectiveness ratio for patients with CD.Key points• The first proposed pancreatic-related model predicts secondary loss of response for infliximab in Crohn’s disease.• The model achieved satisfactory predictive accuracy, calibration ability, and clinical value.• The model-based nomogram has the potential to identify long-term failure in advance and tailor personalized management regimens.Graphical

Development and performance verification of a water-retaining semi-flexible pavement material based on alkali-activated slag system incorporating superabsorbent polymers

Water-retaining semi-flexible pavement (WRSFP) can effectively mitigate the urban heat island (UHI) effect by reducing the pavement temperature through water evaporation. To reduce environmental impacts and enhance the water retaining capacity and mechanical properties of WRSFP, this paper investigated the effect of proportion of water-retaining grout (WRG) on workability of fresh WRG, compressive strength, flexural strength, and water absorption of hardened WRG based on alkali-activated slag (AAS) system. The optimal proportion of WRG was recommended. The microstructure of the hardened WRG was observed with a scanning electron microscope (SEM), which allowed for a better characterization and understanding of the degree of hydration and mechanical properties of WRG. The WRSFP specimens (WRSFP-20%, WRSFP-25% and WRSFP-30%) with different porosities of porous asphalt mixture (PAM) were tested for moisture susceptibility, rutting resistance and low-temperature cracking resistance. WRSFP-25% had the best moisture resistance and rutting resistance, and the worst low-temperature cracking resistance. The maximum difference of upper surface temperature between WRSFP and stone mastic asphalt (SMA), which is commonly used in pavement, was 13.3 °C. The time to reach 60 °C on the upper surface of WRSFP was 3.4 times longer than that of SMA, which showed an excellent cooling effect of WRSFP.

Automatic analysis of public health service text based on character level convolutional neural network

The text information processing technology of public health service is one of the hot research topics at present. To improve the defects of public health service texts, such as inaccurate word segmentation, spelling errors and professional vocabulary understanding, this study designed a character-level deep neural network model on the characteristics of public health service texts. In this model, the bidirectional short and short time memory and the attention pooling operation layer are introduced to make the model better classify the text according to the context. In addition, counter perturbation is introduced in this study to improve the robustness and generalization ability of the model, thus improving its classification effect. The performance verification results show that the proposed model has better classification performance on the public health service text data set. The anti-disturbance samples generated by the model are all in the range of 0–0.2 when WMD deviation degree is measured, while most of the other methods are in the range of 0.4–0.6. The experimental object of this study is ultrasonic examination data. The experimental results show that the automatic analysis model of public health service text based on character level convolutional neural network constructed in this study has excellent accuracy and convergence speed, and has excellent performance in the classification of public health service text in different subject areas.

Process optimization and performance verification of CFRP laser surface modification

AbstractAdhesive bonding has gradually replaced mechanical connections as the primary method for composite material bonding, with surface quality being the key factor determining the effectiveness of composite material adhesion. Laser surface treatment, known for its green and precise characteristics, has garnered widespread attention from both academia and industry. However, the process parameters of laser surface treatment are complex, efficiency is low, and the mechanism remains unclear. This paper, based on the response surface methodology (RSM), investigates the influence of laser secondary parameters on the resin removal rate of carbon fiber reinforced polymer/plastic (CFRP) surfaces and the impact of defocusing on surface treatment efficiency. The results indicate that when the total laser energy density (Et) is 69 J/cm2, the overlap ratio in the warp direction (OLs) is 76%, and the overlap ratio in the weft direction (OLh) is 85%, the surface tensile shear strength increases by 90.59% and 27.77% compared to the original surface and polished surface, respectively. When the defocus distance is +8 mm, the efficiency of laser surface treatment is 2.25 times that of non‐defocus, with minimal impact on mechanical performance. The surface treatment mechanism, with changes in Et, primarily involves the pure thermal decomposition of the resin matrix in an oxygen‐free environment, oxidative decomposition, and the synergistic action of the decomposition products' oxidation. Through the coupled optimization of laser process parameters, this work enhances the efficiency and effectiveness of composite material adhesive surface treatment, promoting the engineering application of laser technology.Highlights By establishing a response surface model, the study elucidated the impact patterns of laser secondary parameters (overlap rates in the warp and weft directions, total energy density) on the resin removal rate of CFRP surfaces. Based on the laser surface treatment phenomena at different total energy densities, the study analyzed and revealed the mechanism of near‐infrared (1064 nm) laser resin removal from CFRP surfaces. By increasing the spot size through defocusing, the efficiency of laser surface treatment was enhanced, and the impact on the interfacial properties of CFRP after defocused treatment was validated.

Sensitivity of lateral flow technique for diagnosis of canine parvovirus

In this study, we devised a nanogold lateral flow immunoassay (LFA-CPV antigen test) for detecting canine parvovirus (CPV) in living attenuated CPV vaccines. We conducted instrumental characterization of the prepared nanogold particles and the developed LFA-CPV antigen test was rigorously evaluated for its performance verification including limit of detection, sensitivity, specificity, selectivity and accuracy. The LFA-CPV antigen test demonstrated strong performance when assessed against qPCR using different batches of live attenuated CPV vaccines, indicated a sensitivity of 96.4%, specificity of 88.2%, and an overall accuracy of 95%. These results suggest that the developed LFA-CPV antigen test could serve as a viable alternative for evaluation live attenuated CPV vaccines, and provide it as a point of care test for CPV diagnosis, offering a potential substitute for traditional laboratory methods, particularly qPCR.

Verification of the analytical performance of the serum CA 125 assay on the Abbott Alinity ci®: Experience of the biochemistry laboratory of the Mohammed VI university hospital of Oujda

The aim of our work was to evaluate the analytical performance of CA 125 determination by a two-step immunoassay using microparticle chemiluminescence immunoassay (CMIA) technology, in accordance with the Scope A criteria of the guide of the verification/validation of medical biology methods. We evaluated the repeatability and the intermediate precision of the CA 125 assay.The results obtained are very satisfactory for the three levels (low, medium and high), both for intermediate fidelity, with coefficients of variation (CV) of 2.68%, 1.62% and 2.11% respectively, and for repeatability, with coefficients of variation of CV1 = 2.17%, CV2 = 2.04%, and CV3 = 1.38% respectively. The results obtained made it possible to verify the method's performance and compare it with the analytical objectives set in order to meet the regulatory and normative requirements set by the supplier and learned societies. The achieved results facilitated the verification of the method's performance and its comparison with the analytical objectives established, aligning with regulatory and normative requirements outlined by the supplier and relevant professional learned societies.

Verification of the analytical performance of the serum Folate assay on the Abbott Alinity ci®

In the evolving landscape of clinical diagnostics, the significance of folate takes center stage as precision and innovation shape advancements in analytical methodologies. Folate, a pivotal element in cellular metabolism, governs physiological processes and serves as an indicator for various medical conditions. This study employs the Chemiluminescent Microparticle Immunoassay (CMIA), known for its accuracy, to assess the reproducibility and repeatability of serum folate levels. Folate's role in cellular metabolism influences diverse physiological functions, making its precise quantification crucial for identifying and monitoring conditions such as folate deficiency. The CMIA method emerges as a robust approach, leveraging immunological specificity for high precision and reliability. Utilizing the Abbott Alinity ci® Analyzer, a technologically advanced clinical chemistry instrument, this study incorporates a systematic analytical method verification procedure. This involves quantification through a standardized protocol and a comparative analysis against criteria set by esteemed societies (RICOS and FSCB), ensuring comprehensive insights into analysis techniques. The reproducibility test, evaluating the impact of various factors on assay results, reveals low Coefficient of Variation (CV) values (CV1: 10.25%, CV2: 8.58%, CV3: 9.13%) across different levels. The results align with quality control limits, emphasizing the method's reliability. Repeatability assessment demonstrates exceptionally low CV values (CV1: 4.84%, CV2: 3.41%, CV3: 1.89%), highlighting the method's stability and precision under controlled conditions.

Approaches for Performance Verification Toward Standardization of Peripheral Blood Regulatory T-Cell Detection by Flow Cytometry.

Regulatory T-cell (Treg) detection in peripheral blood, based on flow cytometry, is invaluable for diagnosis and treatment of immune-mediated diseases. However, there is a lack of reliable methods to verify the performance, which is pivotal towards standardization of the Tregs assay. To conduct standardization studies and verify the performance of 3 commercially available reagent sets for the Tregs assay based on flow cytometry and agreement analysis for Treg detection across the different reagent sets. The analytical performance of Tregs assay using reagent sets supplied by 3 manufacturers was evaluated after establishing the gating strategy and determining the optimal antibody concentration. Postcollection sample stability was evaluated, as well as the repeatability, reproducibility, reportable range, linearity, and assay carryover. Agreement between the different assays was assessed via Bland-Altman plots and linear regression analysis. The relationship between the frequency of CD4+CD25+CD127low/- Tregs and CD4+CD25+Foxp3+ Tregs was evaluated. The postcollection sample stability was set at 72 hours after collection at room temperature. The accuracy, repeatability, reproducibility, and accuracy all met the requirements for clinical analysis. Excellent linearity, with R2 ≥0.9 and no assay carryover, was observed. For reportable range, a minimum of 1000 events in the CD3+CD4+ gate was required for Tregs assay. Moreover, the results for Tregs labeled by antibodies from the 3 manufacturers were in good agreement. The percentage of CD4+CD25+CD127low/- Tregs was closely correlated with CD4+CD25+Foxp3+ Tregs. This is the first study to evaluate systematically the measurement performance of Tregs in peripheral blood by flow cytometry, which provides a practical solution to verifying the performance of flow cytometry-based immune monitoring projects in clinical practice.

Metrological characterisation of tool pre-setting optical systems based on 2D imaging

Accurate tool geometry is vital in modern computer numerical control machine tools for ensuring the accuracy of the manufactured parts in machining processes. The tool pre-setting optical systems play an important role by establishing accurate, efficient, and repeatable tool data for cutting tools varying from macro to micro size tools. The tool data is later utilised by the numerical control programs to accomplish precision machining operations with the required accuracy. However, the traceability of tool pre-setting optical systems is not guaranteed due to the lack of specific international standards, and there is a strong need of establishing a traceable measurement process which requires calibrated reference artefacts. In this work, we have established the metrological characteristics of an image-based tool pre-setting optical system based on ISO 15530 part 3. For this purpose, an artefact with sharp cutting edges was manufactured from a calibrated cylindrical gauge pin, measured on a coordinate measuring machine by two different approaches, and the impact of the edge radius is incorporated in the measurement process. The artefact was later employed in the performance verification of the optical system for on-machine tool characterization analogous to the production workshop environment, and the measured outcome of the optical sensor was evaluated with the developed metrological approaches. Experiments have shown the applicability of the measurement procedures and the relevance of the proposed reference artefact for the characterisation of image-based tool pre-setting optical systems.