A bearing classification and performance prediction method based on roughness parameters
By collecting the roughness parameters of the bearing and establishing a regression model, the problems of low efficiency and high cost of bearing quality detection in the existing technology are solved, rapid classification and performance prediction of bearings are achieved, and quality control efficiency and product quality are improved.
CN119740204APending Publication Date: 2025-04-01JM BEARING GRP
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Patent Information
- Application Number
- CN202411841556.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-13
- Publication Date
- 2025-04-01
AI Technical Summary
Technical Problem
The existing bearing quality inspection methods are inefficient, costly and complex in operation, making it difficult to meet the needs of fast and efficient quality control in modern production.
Method used
By collecting the surface roughness parameters of the bearing, a multivariate regression model is established, and the bearings are divided into high-performance, medium-performance and low-performance levels using cluster analysis methods, and the performance of the bearings under different operating conditions is predicted through mathematical models.
Benefits of technology
It realizes rapid classification and performance prediction of bearings, significantly improves quality control efficiency in the production process, and ensures product consistency and high quality.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a method for classifying and predicting the performance of bearings based on roughness parameters, which belongs to the technical field of bearing manufacturing and quality control, and comprises the following steps: S1, collecting roughness parameters, S2, establishing a mathematical model, S3, substituting the parameters into the model, S4, classifying the bearings, and S5, predicting the performance. By establishing a mathematical model and using statistical analysis tools, the surface roughness parameters of the bearings are systematically analyzed, thereby realizing rapid classification and performance prediction of the bearings. The method can significantly improve the quality control efficiency in the production process, and can quickly screen out bearings of different performance levels on the production line to ensure the consistency and high quality of the products. The rapid classification of the bearings is realized by analyzing the surface roughness parameters of the bearings, and the mathematical model and statistical analysis method are used to predict the performance of the bearings under different working conditions, thereby improving the control efficiency of the bearing quality in the production process.
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