Surface depression recognition of spring plate based on feature construction and improved AdaBoost algorithm
Source abstract
Abstract
Machine learning has introduced novel solutions for the surface quality inspection of spring plates. Hence, an improved method was proposed based on logistic regression and AdaBoost (LR-RBAdaBoost) for recognizing surface depressions in spring plates. In order to obtain the measured attributes for spring plates, feature construction methods were adopted. Then, logistic regression was used to analyze and rank the attributes of the spring plate and the filter method was used for the feature selection process. To improve the predicted performance of AdaBoost, RBAdaBoost was proposed in this paper, which is used in the issue of recognition of surface defects in spring steel plates. The effectiveness and sophistication of LR-RBAdaBoost are validated on a real spring steel plate sampling dataset. The results show that the accuracy of LR-RBAdaBoost is 0.968 and f1-score is 0.967, which can be better than the results of only using AdaBoost, random forest, and CatBoost. Furthermore, the results prove that the research has a certain reference value for the recognition of spring plate as well.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Infrastructure Maintenance and Monitoring · Surface Roughness and Optical Measurements
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Numerical Control Technology, Xinxiang Vocational and Technical College 1 , Xinxiang 453000, China
Dean’s Office, Xinxiang Vocational and Technical College 2 , Xinxiang 453000, China
College of Communication Engineering, Jilin University 3 , Changchun 130012, China
School of Mechanical and Power Engineering, Henan Polytechnic University 4 , Jiaozuo 454003, China
Funding
Natural Science Foundation of Henan Province · Fundamental Research Funds for the Universities of Henan Province
References (19)
Show source-provided references
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