Temperature Uncertainty Analysis of Injection Mechanism Based on Kriging Modeling

Area of Science:

• Materials Science
• Mechanical Engineering
• Computational Modeling

Background:

• Squeeze casting involves complex thermal processes impacting product quality.
• Accurate temperature prediction is crucial for controlling injection mechanisms.
• Uncertainty in temperature can lead to defects and process inefficiencies.

Purpose of the Study:

• To propose a kriging modeling method for temperature uncertainty analysis in squeeze casting injection mechanisms.
• To investigate the influence of temperature uncertainty on the injection process, including friction.
• To evaluate the effectiveness of model calibration using experimental data.

Main Methods:

• Employed a multi-input, single-output mathematical model for temperature spatiotemporal distribution estimation.
• Applied kriging modeling, assigning weights based on spatial location and correlation of sample points to reduce estimation variance.
• Compared kriging model predictions with experimental sample data.

Main Results:

• Significant errors were observed at early injection points due to uncertainty factors.
• Model calibration with experimental samples substantially reduced variance, mean, and standard deviation compared to the initial kriging model.
• The calibrated model demonstrated improved accuracy in predicting temperature distributions.

Conclusions:

• Kriging modeling is an effective approach for temperature uncertainty analysis in squeeze casting.
• Model calibration is essential for achieving accurate predictions and understanding process influences.
• This methodology enhances the reliability of the injection process by addressing temperature uncertainties.