An engine cylinder head milling surface quality prediction method based on mechanism and data driving

By combining mechanism and data-driven methods, a prediction model for cylinder head milling surface quality was constructed, which solved the problems of low prediction accuracy and long time consumption in the existing technology, and realized accurate prediction of cylinder head milling surface quality and met production quality requirements.

CN114818201BActive Publication Date: 2026-07-17SHENYANG UNIVERSITY OF TECHNOLOGY
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
SHENYANG UNIVERSITY OF TECHNOLOGY
Filing Date
2022-05-25
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing cylinder head milling surface quality prediction technologies suffer from problems such as limited data types of model input variables, improper selection of internal model parameters leading to low prediction accuracy, and long prediction time. These issues result in cylinder head milling surface quality failing to meet production requirements, poor engine sealing performance, and high scrap rates.

Method used

A mechanism- and data-driven method for predicting the surface quality of cylinder head milling is constructed. By determining surface roughness as the key evaluation index, and combining milling force and milling heat data, a semi-analytical method and a heat source method mechanism model are constructed. The adaptive differential evolution algorithm is used to optimize the support vector regression model, thereby improving the prediction accuracy and efficiency.

Benefits of technology

It enables accurate real-time prediction of cylinder head milling surface quality, reduces data acquisition costs and model running time, improves prediction accuracy, and meets production quality requirements.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application relates to a mechanism and data driving-based engine cylinder cover milling surface quality prediction method and belongs to the technical field of automatic prediction. The method comprises the following steps: determining key evaluation indexes and influence factors of cylinder cover milling surface quality; collecting process parameters and surface roughness values, combining with a cylinder cover milling mechanical state to construct a milling force and thermal mechanism model based on a semi-analytical method and a heat source method, obtaining state variable data, and storing the state variable data into a historical database after preprocessing; constructing a surface roughness prediction model based on ADE algorithm optimization SVR, taking process parameters and mechanism model output state variable data as data driving model input, taking surface roughness values as output, and obtaining an optimal parameter combination of SVR by training historical data; and predicting the milling surface roughness by using real-time process parameters and mechanism model output state variable data. The method has the advantages of strong model state representation capability and low state variable data acquisition cost, and can realize accurate prediction of cylinder cover milling surface quality.
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