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

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.

Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification

“…Our previous studies about DD observed faster speeds of nonlinear mapping using DD [8]. Due to the white-box attribute, controllable precision, and lower time complexity, DD was comprehensively applied in the areas of energy, traffic, weather, and physical design [9][10][11][12][13][14][15]. However, there has still been a redundant calculation in DD.…”

“…Literature [13] An Accuracy Prediction Method of the RV Reducer to Be Assembled Considering Dendritic Weighting Function Literature [14] An Adaptive Dendrite-HDMR Metamodeling Technique for High-Dimensional Problems…”

“…During the year, DD has already been used in multiple areas, such as energy saving [9], spatiotemporal traffic flow data imputation [10], high-dimensional problems [11], image processing [12], multi-objective optimization [13], accuracy prediction of the RV reducer to be assembled [14], and precipitation correction in flood season [15]. Nevertheless, there are still some redundant calculations in DD when the order of the DD model is higher than the number of inputs.…”

A prediction method for the backlash error of robot precision reducers based on optimal assembly

“…Zhang et al [23] based on the design tolerance, the relationship between each error and transmission error is established, and a prediction model of transmission error is established accurately. Jin et al [24] took the key component size parameters that affect the backlash error of the reducer as input and established a dendritic neural network prediction model to predict the backlash error. Jin et al [25] established a prediction model for the manufacturing error and backlash error of key parts in the RV-40E reducer.…”