A novel hybrid assembly method to reduce operational costs of selective assembly

Mencaroni, Andrea; Claeys, Dieter; Vuyst, Stijn De

doi:10.1016/j.ijpe.2023.108966

Cited by 2 publications

(1 citation statement)

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“…Shin and Jin [17] proposed the density-based prioritization algorithm to reduce computational cost and excess parts while enhancing process capability. Mencaroni et al [18] performed a number of traditional assembly rounds before switching to selective assembly, the technique can significantly reduce operational costs of selective assembly. It is evident that the optimal assembly method can effectively improve the assembly accuracy of the reducer.…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Yao,

et al. 2024

Meas. Sci. Technol.

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The kinematic accuracy and service life of robot precision reducers are directly affected by their assembly accuracy, and the backlash error is one of the most important performance indicators for evaluating the assembly accuracy. A prediction method for the backlash error of robot precision reducers is proposed, which can quickly and accurately calculate the backlash error of the optimal assembly reducers. Based on the optimal assembly process, the sensitive relationship between the errors of the crucial components and the assembly accuracy of the reducer is analysed, and the crucial errors affecting the backlash error are identified. The cumulative backlash of the crucial error along the dimensional chain is clarified, and the corresponding relationship between the backlash and the backlash error is determined. On this basis, backlash prediction models are established for the involute planetary gear and cycloidal-pin gear transmission parts, respectively. The mapping relationship between dimensional errors and backlash is also derived. Furthermore, the backlash error of the reducer is obtained by calculating the backlash error caused by each error term of the high-speed stage and low-speed stage transmission parts. Finally, the effectiveness and correctness of the prediction method are verified by the optimal assembly of RV reducers and the measurement of the backlash error. The theory and method proposed in this paper can quickly and accurately predict the backlash error of the reducer for robots, effectively predict the assembly accuracy of the selective reducers, significantly improve the assembly efficiency of the reducers, and then provide theoretical guidance and technical support for enhancing the assembly quality and kinematic accuracy of robot reducers.

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Section: Introductionmentioning

confidence: 99%