An Accuracy Prediction Method of the RV Reducer to Be Assembled Considering Dendritic Weighting Function

Shousong Jin, Yanxi Chen, Yiping Shao and Yaliang Wang
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Shousong Jin: School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Yanxi Chen: School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Yiping Shao: School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Yaliang Wang: School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Energies, 2022, vol. 15, issue 19, 1-13

Abstract: There are many factors affecting the assembly quality of rotate vector reducer, and the assembly quality is unstable. Matching is an assembly method that can obtain high-precision products or avoid a large number of secondary rejects. Selecting suitable parts to assemble together can improve the transmission accuracy of the reducer. In the actual assembly of the reducer, the success rate of one-time selection of parts is low, and “trial and error assembly” will lead to a waste of labor, time cost, and errors accumulation. In view of this situation, a dendritic neural network prediction model based on mass production and practical engineering applications has been established. The size parameters of the parts that affected transmission error of the reducer were selected as influencing factors for input. The key performance index of reducer was transmission error as output index. After data standardization preprocessing, a quality prediction model was established to predict the transmission error. The experimental results show that the dendritic neural network model can realize the regression prediction of reducer mass and has good prediction accuracy and generalization capability. The proposed method can provide help for the selection of parts in the assembly process of the RV reducer.

Keywords: RV reducer; assembly quality; dendrites; neural network; transmission accuracy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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