Abstract
The manufacturing of a high-precision servo valve belongs to multi-variety, small-batch, and customized production modes. In the process of assembly and commissioning, various characteristic parameters are critical indicators to measure product performance. To meet the performance requirements of a high-precision servo valve, the traditional method usually relies on the test bench and manual experience for continuous trial and error commissioning, which significantly prolongs the whole assembly-commissioning cycle. Therefore, this paper proposed a performance prediction method for a high-precision servo valve supported by digital twin assembly-commissioning. Firstly, the cloud-edge computing network is deployed in the digital twin assembly-commissioning system to improve the efficiency and flexibility of data processing. Secondly, the method workflow of performance prediction is described. In order to improve the accuracy of measurement data, a data correction method based on model simulation and gross error processing is proposed. Aiming at the problem of high input dimension of the prediction model, a key assembly feature parameters (KAFPs) selection method, based on information entropy (IE), is proposed and given interpretability. Additionally, to avoid the poor prediction accuracy caused by small sample data, a performance prediction method based on TrAdaboost was utilized. Finally, the hysteresis characteristic commissioning of a high-precision servo valve is taken as an example to verify the application. The results indicate that the proposed method would enable accurate performance prediction and fast iteration of commissioning decisions.
Highlights
Published: 23 December 2021With the rapid development of high-precision manufacturing technology, high-precision servo valves are widely used in aerospace, watercraft, and automotive industries
In the process of a high-precision servo valve assembly-commissioning, to solve the problem of a long period of traditional performance index commissioning methods, a performance prediction framework supported by digital twin assembly-commissioning technology is proposed in this paper
The network deployment method provides a feasible method for realizing digital twin technology in dynamic industrial cloud-edge networks to ensure high-performance prediction and commissioning decision-making
Summary
With the rapid development of high-precision manufacturing technology, high-precision servo valves are widely used in aerospace, watercraft, and automotive industries. In order to meet the quality requirements of a high-precision servo valve, assembly and commissioning are required. In the assembly-commissioning of a high-precision servo valve, various characteristic parameters (such as hysteresis, nonlinearity, zero bias, etc.). Are important indicators to measure the product performance. The traditional assemblycommissioning method is based on repeated tests and manual experience, resulting in poor quality and low efficiency. It is of great significance to study a fast and accurate performance prediction method to assist commissioning decision-making in improving the assembly-commissioning efficiency of a high-precision servo valve
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