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Large size of aircraft assembly tooling structure and complex measurement environment exist. The laid enhanced reference points (ERS) are subject to a combination of nonuniform temperature fields and measurement errors, resulting in increased measurement registration errors. In view of the nonuniform temperature field and measurement errors affecting the ERS point registration problem, the purpose of this paper is to propose a neural network-based ERS point registration compensation method for large-size measurement fields under a nonuniform temperature field.

The approach is to collect ERS point information and temperature data, normalize the collected data to complete the data structure design and complete the construction of the neural network prediction model by data training. The data learning is performed to complete the prediction model construction, and the prediction model is used to complete the compensation analysis of ERS points. Finally, the algorithm is verified through experiments and engineering practice.

Experimental results show that the proposed neural network-based ERS point prediction and compensation method for nonuniform temperature fields effectively predicts ERS point deformation under nonuniform temperature fields compared with the conventional method. After the compensation analysis, the registration error is effectively reduced to improve registration accuracy. Reducing the combined effect of environmental nonuniform temperature field and measurement error has apparent advantages.

The method reduces the registration error caused by combining a nonuniform temperature field and measurement error. It can be used for aircraft assembly site prediction and registration error compensation analysis, which is essential to improve measurement accuracy further.

This paper develops the statistical error analysis model for assembling, to derive measures of controlling the geometric variations in assembly with multiple assembly stations, and to provide a statistical tolerance prediction/distribution toolkit integrated with CAD system for responding quickly to market opportunities with reduced manufacturing costs and improved quality. First the homogeneous transformation is used to describe the location and orientation of assembly features, parts and other related surfaces. The desired location and orientation, and the related fixturing configuration (including locator position and orientation) are automatically extracted from CAD models. The location and orientation errors are represented with differential transformations. The statistical error prediction model is formulated and the related algorithms integrated with the CAD system so that the complex geometric information can be directly accessed. In the prediction model, the manufacturing process (joining) error, induced by heat deformation in welding, is taken into account.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that generates case-level predictions on an item or a construct level. This paper offers guidelines for applying PLSpredict and explains the key choices researchers need to make using the procedure.

The authors discuss the need for prediction-oriented model evaluations in PLS-SEM and conceptually explain and further advance the PLSpredict method. In addition, they illustrate the PLSpredict procedure’s use with a tourism marketing model and provide recommendations on how the results should be interpreted. While the focus of the paper is on the PLSpredict procedure, the overarching aim is to encourage the routine prediction-oriented assessment in PLS-SEM analyses.

The paper advances PLSpredict and offers guidance on how to use this prediction-oriented model evaluation approach. Researchers should routinely consider the assessment of the predictive power of their PLS path models. PLSpredict is a useful and straightforward approach to evaluate the out-of-sample predictive capabilities of PLS path models that researchers can apply in their studies.

Future research should seek to extend PLSpredict’s capabilities, for example, by developing more benchmarks for comparing PLS-SEM results and empirically contrasting the earliest antecedent and the direct antecedent approaches to predictive power assessment.

This paper offers clear guidelines for using PLSpredict, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses.

This research substantiates the use of PLSpredict. It provides marketing researchers and practitioners with the knowledge they need to properly assess, report and interpret PLS-SEM results. Thereby, this research contributes to safeguarding the rigor of marketing studies using PLS-SEM.

Numerical simulation of the single point incremental forming (SPIF) processes can be very demanding and time consuming due to the constantly changing contact conditions between the tool and the sheet surface, as well as the nonlinear material behaviour combined with non-monotonic strain paths. The purpose of this paper is to propose an adaptive remeshing technique implemented in the in-house implicit finite element code LAGAMINE, to reduce the simulation time. This remeshing technique automatically refines only a portion of the sheet mesh in vicinity of the tool, therefore following the tool motion. As a result, refined meshes are avoided and consequently the total CPU time can be drastically reduced.

SPIF is a dieless manufacturing process in which a sheet is deformed by using a tool with a spherical tip. This dieless feature makes the process appropriate for rapid-prototyping and allows for an innovative possibility to reduce overall costs for small batches, since the process can be performed in a rapid and economic way without expensive tooling. As a consequence, research interest related to SPIF process has been growing over the last years.

In this work, the proposed automatic refinement technique is applied within a reduced enhanced solid-shell framework to further improve numerical efficiency. In this sense, the use of a hexahedral finite element allows the possibility to use general 3D constitutive laws. Additionally, a direct consideration of thickness variations, double-sided contact conditions and evaluation of all components of the stress field are available with solid-shell and not with shell elements. Additionally, validations by means of benchmarks are carried out, with comparisons against experimental results.

It is worth noting that no previous work has been carried out using remeshing strategies combined with hexahedral elements in order to improve the computational efficiency resorting to an implicit scheme, which makes this work innovative. Finally, it has been shown that it is possible to perform accurate and efficient finite element simulations of SPIF process, resorting to implicit analysis and continuum elements. This is definitively a step-forward on the state-of-art in this field.

In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

The purpose of this paper is to investigate whether stock price predictions and investment decisions improve by exposure to increasing price series.

The authors conducted three laboratory experiments in which undergraduates were asked to role‐play being investors buying and selling stock shares. Their task was to predict an unknown closing price from an opening price and to choose the number of stocks to purchase to the opening price (risk aversion) or the closing price (risk taking). In Experiment 1 stock prices differed in volatility for increasing, decreasing or no price trend. Prices were in different conditions provided numerically for 15 trading days, for the last 10 trading days, or for the last five trading days. In Experiment 2 the price series were also visually displayed as scatter plots. In Experiment 3 the stock prices were presented for the preceding 15 days, only for each third day (five days) of the preceding 15 days, or as five prices, each aggregated for three consecutive days of the preceding 15 days. Only numerical price information was provided.

The results of Experiments 1 and 2 showed that predictions were not markedly worse for shorter than longer price series. Possibly because longer price series increase information processing load, visual information had some influence to reduce prediction errors for the longer price series. The results of Experiment 3 showed that accuracy of predictions increased for less price volatility due to aggregation, whereas again there was no difference between five and 15 trading days. Purchase decisions resulted in better outcomes for the aggregated prices.

Investorś performance in stock markets may not improve by increasing the length of evaluation intervals unless the quality of the information is also increased. The results need to be verified in actual stock markets.

The results have bearings on the design of bonus systems.

The paper shows how stock price predictions and buying and selling decisions depend on amount and quality of information about historical prices.

Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope. Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution, the prediction accuracy and convergence speed of the traditional method need to be improved. The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

In this paper, an double hidden layer long-short term memory (LSTM) is presented to predict temperature data for the gyroscope (including single point and period prediction), and the evaluation index of the prediction effect is also proposed, and the prediction effects of shell temperature data are compared by BP network, support vector machine (SVM) and LSTM network. Using the estimated value detects the abnormal change of the gyroscope.

By combined simulation calculation with the gyroscope measured data, the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed, and the LSTM network can be used to predict the temperature (time series data). By comparing the performance indicators of different prediction methods, the accuracy of the shell temperature estimation by LSTM is better, which can meet the requirements of abnormal change detection. Quick and accurate diagnosis of different types of gyroscope faults (steps and drifts) can be achieved by setting reasonable data window lengths and thresholds.

The LSTM model is a deep neural network model with multiple non-linear mapping levels, and can abstract the input signal layer by layer and extract features to discover deeper underlying laws. The improved method has been used to solve the problem of strong non-linearity and random noise pollution in time series, and the estimated value can detect the abnormal change of the gyroscope.

In this paper, based on the LSTM network, an double hidden layer LSTM is presented to predict temperature data for the gyroscope (including single point and period prediction), and validate the effectiveness and feasibility of the algorithm by using shell temperature measurement data. The prediction effects of shell temperature data are compared by BP network, SVM and LSTM network. The LSTM network has the best prediction effect, and is used to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

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.