Temperature Uncertainty Analysis of Injection Mechanism Based on Kriging Modeling

Abstract

A kriging modeling method is proposed to conduct the temperature uncertainty analysis of an injection mechanism in squeeze casting. A mathematical model of temperature prediction with multi input and single output is employed to estimate the temperature spatiotemporal distributions of the injection mechanism. The kriging model applies different weights to the independent variables according to spatial location of sample points and their correlation, thus reducing the estimation variance. The predicted value of the kriging model is compared with the sample data at the corresponding position to investigate the influence of the temperature uncertainty of the injection mechanism on the injection process including friction. The results indicate that the significant error is observed at a few sample points in the early injection due to the impact of the uncertainty facts. The variance mean and standard deviation obtained by the model calibrated by experimental samples reduce largely in comparison to those obtained from the initial kriging model. This study indicates that model calibration produces more accurate prediction.

A Quantitative Validation Method of Kriging Metamodel for Injection Mechanism Based on Bayesian Statistical Inference

“…The Bayesian statistical inference model is used to analyze the test sample data and quantitatively verify the kriging metamodel constructed from simulation and experimental training samples in previous research [2]. Orig_Krig_Test is the raw data d 1 in the initial model; Orig_Cal_ Test is the raw data d 2 in the calibration model; Trans_Krig_Test is the transformed data T d 1 in the initial model; and Trans_Cal_Test is the transformed data T d 2 in the calibration model.…”

“…The reasonable initial interval threshold , which determines the value of K in Equation (2) should be established before model validation. When the threshold tends to be infinity, the K value is infinitely close to 1, and the uncertainty of the Bayes factor will increase, which indicates that the model is always acceptable.…”

“…The metamodel method is effective and feasible for data analysis and prediction. In our previous study [1,2], an uncertainty analysis based on a kriging metamodel of the injection mechanism in squeeze casting and a literature review were performed. To evaluate and improve the prediction accuracy with uncertainty, the proposed kriging model needs quantitative validation and calibration.…”

Scheduling Under Uncertainty for Industry 4.0 and 5.0

“…The variability in or the uncertainty of these products results in a need for consolidated methodologies, which can predict these properties during the design stage [109]. • Process-inherent uncertainty such as variations in the flow rate and temperature fluctuations in the quality of the stream, processing time [35], [52] and the availability of equipment [110], like in the case of squeeze casting, where the temperatures of the shot sleeve, punch and smelting can be non-uniform [111].…”

Stochastic robust predictive fault-tolerant control for discrete systems with actuator faults

“…In addition, the fault still occurs randomly in the actual process and can be represented as a probability [29][30][31][32], which is consistent with the realities of the industrial world. Taking the injection molding process [33][34][35][36][37] for example, when the manufacturing equipment is put into use, the feed pipeline of an injection machine may become blocked, resulting in partial faults after a while of use. The bloackage could go back to normal once subsequently injected raw ingredients cleansed them.…”