Search results

This study investigates the fitting techniques for notch fatigue curves, seeking a more reliable method to predict the lifespan of welded structures.

Building on the fatigue test results of butt and cruciform joints, this research delves into the selection of fitting methods for the notch fatigue curve of welded joints. Both empirical formula and finite element methods (FEMs) were employed to assess the notch stress concentration factor at the toe and root of the two types of welded joints. Considering the mean stress correction and weld misalignment coefficients, the notch fatigue life curves were established using both direct and indirect methods.

An engineering example was employed to discern the differences between the direct and indirect approaches. The findings highlight the enhanced reliability of the indirect method for fitting the fatigue life curve.

While the notch stress approach is extensively adopted due to its accurate prediction of component fatigue life, most scholars have overlooked the importance of its curve fitting methods. Existing literature scantily addresses the establishment of these curves. This paper offers a focused examination of fatigue curve fitting techniques, delivering valuable perspectives on method selection.

The purpose of this paper is to apply the concept of equivalent initial flaw size (EIFS) to the anisotropic nickel-based single crystal (SX) material, and to predict the fatigue life on this basis. The crack propagation law of SX material at different temperatures and the weak correlation of EIFS values verification under different loading conditions are also investigated.

A three-parameter time to crack initial (TTCI) method with multiple reference crack lengths under different loading conditions is established, which include the TTCI backstepping method and EIFS fitting method. Subsequently, the optimized EIFS distribution is obtained based on the random crack propagation rate and maximum likelihood estimation of median fatigue life. Then, an effective driving force based on anisotropic and mixed crack propagation mode is proposed to describe the crack propagation rate in the small crack stage. Finally, the fatigue life of three different temperature ESE(T) standard specimens is predicted based on the EIFS values under different survival rates.

The optimized EIFS distribution based on EIFS fitting - maximum likelihood estimation (MLE) method has the highest accuracy in predicting the total fatigue life, with the range of EIFS values being about [0.0028, 0.0875] (mm), and the mean value of EIFS being 0.0506 mm. The error between the predicted fatigue life based on the crack propagation rate and EIFS distribution for survival rates ranges from 5% to 95% and the experimental life is within two times dispersion band.

This paper systematically proposes a new anisotropic material EIFS prediction method, establishing a framework for predicting the fatigue life of SX material at different temperatures using fracture mechanics to avoid inaccurate anisotropic constitutive models and fatigue damage accumulation theory.

Due to the limitation of experimental conditions and budget, fatigue data of mechanical components are often scarce in practical engineering, which leads to low reliability of fatigue data and reduces the accuracy of fatigue life prediction. Therefore, this study aims to expand the available fatigue data and verify its reliability, enabling the achievement of life prediction analysis at different stress levels.

First, the principle of fatigue life probability percentiles consistency and the perturbation optimization technique is used to realize the equivalent conversion of small samples fatigue life test data at different stress levels. Meanwhile, checking failure model by fitting the goodness of fit test and proposing a Monte Carlo method based on the data distribution characteristics and a numerical simulation strategy of directional sampling is used to extend equivalent data. Furthermore, the relationship between effective stress and characteristic life is analyzed using a combination of the Weibull distribution and the Stromeyer equation. An iterative sequence is established to obtain predicted life.

The TC4–DT titanium alloy is selected to assess the accuracy and reliability of the proposed method and the results show that predicted life obtained with the proposed method is within the double dispersion band, indicating high accuracy.

The purpose of this study is to provide a reference for the expansion of small sample fatigue test data, verification of data reliability and prediction of fatigue life data. In addition, the proposed method provides a theoretical basis for engineering applications.

The purpose of this study is to propose a fast method for predicting flow fields with periodic behavior with verification in the context of a radial turbine to meet the urgent requirement to effectively capture the unsteady flow characteristics in turbomachinery. Aiming at meeting the urgent requirement to effectively capture the unsteady flow characteristics in turbomachinery, a fast method for predicting flow fields with periodic behavior is proposed here, with verification in the context of a radial turbine (RT).

Sparsity-promoting dynamic mode decomposition is used to determine the dominant coherent structures of the unsteady flow for mode selection, and for flow-field prediction, the characteristic parameters including amplitude and frequency are predicted using one-dimensional Gaussian fitting with flow rate and two-dimensional triangulation-based cubic interpolation with both flow rate and rotation speed. The flow field can be rebuilt using the predicted characteristic parameters and the chosen model.

Under single flow-rate variation conditions, the turbine flow field can be recovered using the first seven modes and fitted amplitude modulus and frequency with less than 5% error in the pressure field and less than 9.7% error in the velocity field. For the operating conditions with concurrent flow-rate and rotation-speed fluctuations, the relative error in the anticipated pressure field is likewise within an acceptable range. Compared to traditional numerical simulations, the method requires a lot less time while maintaining the accuracy of the prediction.

It would be challenging and interesting work to extend the current method to nonlinear problems.

The method presented herein provides an effective solution for the fast prediction of unsteady flow fields in the design of turbomachinery.

A flow prediction method based on sparsity-promoting dynamic mode decomposition was proposed and applied into a RT to predict the flow field under various operating conditions (both rotation speed and flow rate change) with reasonable prediction accuracy. Compared with numerical calculations or experiments, the proposed method can greatly reduce time and resource consumption for flow field visualization at design stage. Most of the physics information of the unsteady flow was maintained by reconstructing the flow modes in the prediction method, which may contribute to a deeper understanding of physical mechanisms.

This study aims to automate the process of converting grading patterns into parametric patterns using artificial intelligence and to objectively evaluate the fitness of the converted patterns.

The developed system consists of a user interface that defines input data by importing multi-size grading patterns, an artificial neural network that learns the relationship between human body size and pattern geometry, and a module that converts training results into parametric patterns. In order to evaluate the fitness of the generated pattern, an objective fitting evaluation method using drape simulation was developed.

The body sizes of the wearer were input to the converted parametric pattern to generate a customized pattern. Resulting pattern showed a better fit than the grading pattern on the off-average body model.

In this study, a method has been developed that enables the users with minimal pattern drafting knowledge to convert grading patterns into parametric patterns using artificial intelligence and drape simulation. The human body's symmetry and the physical properties of fabric were not considered.

The system developed in this study requires less data compared to existing methods that attempt to design clothing patterns with machine learning. In addition, it was possible to evaluate pattern fitness on various body models through drape simulation based fit evaluation process for the first time.

This paper aims to employ a multivariate nonlinear regression analysis to establish a predictive model for the final fracture area, while accounting for the impact of individual parameters.

This analysis is based on the numerical simulation data obtained, using the hybrid finite element–discrete element (FE–DE) method. The forecasting model was compared with the numerical results and the accuracy of the model was evaluated by the root mean square (RMS) and the RMS error, the mean absolute error and the mean absolute percentage error.

The multivariate nonlinear regression model can accurately predict the nonlinear relationships between injection rate, leakoff coefficient, elastic modulus, permeability, Poisson’s ratio, pore pressure and final fracture area. The regression equations obtained from the Newton iteration of the least squares method are strong in terms of the fit to the six sensitive parameters, and the model follow essentially the same trend with the numerical simulation data, with no systematic divergence detected. Least absolutely deviation has a significantly weaker performance than the least squares method. The percentage contribution of sensitive parameters to the final fracture area is available from the simulation results and forecast model. Injection rate, leakoff coefficient, permeability, elastic modulus, pore pressure and Poisson’s ratio contribute 43.4%, −19.4%, 24.8%, −19.2%, −21.3% and 10.1% to the final fracture area, respectively, as they increased gradually. In summary, (1) the fluid injection rate has the greatest influence on the final fracture area. (2)The multivariate nonlinear regression equation was optimally obtained after 59 iterations of the least squares-based Newton method and 27 derivative evaluations, with a decidability coefficient R2 = 0.711 representing the model reliability and the regression equations fit the four parameters of leakoff coefficient, permeability, elastic modulus and pore pressure very satisfactorily. The models follow essentially the identical trend with the numerical simulation data and there is no systematic divergence. The least absolute deviation has a significantly weaker fit than the least squares method. (3)The nonlinear forecasting model of physical parameters of hydraulic fracturing established in this paper can be applied as a standard for optimizing the fracturing strategy and predicting the fracturing efficiency in situ field and numerical simulation. Its effectiveness can be trained and optimized by experimental and simulation data, and taking into account more basic data and establishing regression equations, containing more fracturing parameters will be the further research interests.

The nonlinear forecasting model of physical parameters of hydraulic fracturing established in this paper can be applied as a standard for optimizing the fracturing strategy and predicting the fracturing efficiency in situ field and numerical simulation. Its effectiveness can be trained and optimized by experimental and simulation data, and taking into account more basic data and establishing regression equations, containing more fracturing parameters will be the further research interests.

The purpose of this review is to argue that the way that perceived employee misfit (PEM) has been measured in quantitative studies does not capture the construct identified in qualitative studies.

Through reverse citation analysis, this study reveals how low levels of value congruence became the currency of PEM in quantitative studies.

This study finds that in the absence of alternatives, researchers have taken low scores of value congruence as a measure of misfit. However, there is limited evidence to show that PEM relates to values, supplementary conceptualization or interactions with the organization (rather than interactions with other employees, tasks, etc.). In addition, the most commonly used instruments measure degrees of similarity, not disparity, making the interpretation of PEM-related data unclear. Combined, these factors raise construct validity concerns about most quantitative studies of PEM.

Given the upsurge of interest in PEM, there is an urgent need for greater clarification on the nature of the construct. From the analysis, this study identifies two key dimensions of studying PEM that create four distinctly different ways of conceptualizing the construct.

This study highlights a series of major methodological weaknesses in the study of PEM and reveal that almost all published quantitative studies of PEM are actually studying something else; something whose nature is very unclear.

To improve the position tracking efficiency of the upper-limb rehabilitation robot for stroke hemiplegia patients, the optimization Learning rate of the membership function based on the fuzzy impedance controller of the rehabilitation robot is propose.

First, the impaired limb’s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using weighted least squares method based on recursive algorithm. Second, the fuzzy impedance control with the rule has been designed with the optimal impedance parameters. Finally, the membership function learning rate online optimization strategy based on Takagi-Sugeno (TS) fuzzy impedance model was proposed to improve the position tracking speed of fuzzy impedance control.

This method provides a solution for improving the membership function learning rate of the fuzzy impedance controller of the upper limb rehabilitation robot. Compared with traditional TS fuzzy impedance controller in position control, the improved TS fuzzy impedance controller has reduced the overshoot stability time by 0.025 s, and the position error caused by simulating the thrust interference of the impaired limb has been reduced by 8.4%. This fact is verified by simulation and test.

The TS fuzzy impedance controller based on membership function online optimization learning strategy can effectively optimize control parameters and improve the position tracking speed of upper limb rehabilitation robots. This controller improves the auxiliary rehabilitation efficiency of the upper limb rehabilitation robot and ensures the stability of auxiliary rehabilitation training.

This study aims to identify leather type and authenticity through optical coherence tomography.

Optical coherence tomography images taken from genuine and faux leather samples were used to create an image dataset, and automated machine learning algorithms were also used to distinguish leather types.

The optical coherence tomography scan results in a different image based on leather type. This information was used to determine the leather type correctly by optical coherence tomography and automatic machine learning algorithms. Please note that this system also recognized whether the leather was genuine or synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be used to distinguish leather type and determine whether it is genuine.

For the first time to the best of the authors' knowledge, spectral-domain optical coherence tomography and automated machine learning algorithms were applied to identify leather authenticity in a noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated quality monitoring systems in the leather industry. With recent technological progress, optical coherence tomography combined with automated machine learning algorithms will be used more frequently in automatic authentication and identification systems.

Intellectual capital (IC) plays a crucial role in today’s volatile business landscape, yet its measurement remains complex. To better navigate these challenges, the authors propose the Integrated Intellectual Capital Measurement (IICM) model, an innovative, robust and comprehensive framework designed to capture IC amid business uncertainty. This study focuses on IC measurement models, typically reliant on secondary data, thus distinguishing it from conventional IC studies.

The authors conducted a systematic literature review (SLR) and bibliometric analysis across Web of Science, Scopus and EBSCO Business Source Ultimate in February 2023. This yielded 2,709 IC measurement studies, from which the authors selected 27 quantitative papers published from 1985 to 2023.

The analysis revealed no single, universally accepted approach for measuring IC, with company attributes such as size, industry and location significantly influencing IC measurement methods. A key finding is human capital’s critical yet underrepresented role in firm competitiveness, which the IICM model aims to elevate.

This is the first SLR focused on IC measurement amid business uncertainty, providing insights for better management and navigating turbulence. The authors envisage future research exploring the interplay between IC components, technology, innovation and network-building strategies for business resilience. Additionally, there is a need to understand better the IC’s impact on specific industries (automotive, transportation and hospitality), Social Development Goals and digital transformation performance.