Optimization design and experiment of cam-elliptical gear combined vegetables curved surface labeling mechanism

Front Robot AI. 2024 Dec 13:11:1431078. doi: 10.3389/frobt.2024.1431078. eCollection 2024.

Abstract

To address the problems of the labeling curved surfaces vegetable with long label, such as the label wrinkled and the easy detachment, a cam-elliptical gear combined labeling mechanism with an improved hypocycloid trajectory is proposed. Provide the process of the mechanism, and establish a kinematic model of the mechanism. In order to improve the motion performances of the cam-elliptical gear combined labeling mechanism and avoid labels damage, the NSGA-II algorithm is used to optimize the parameters of the mechanism, resulting in 80 sets of Pareto solutions. The entropy weight TOPSIS method is applied as a quadratic optimization to select an optimal solution from the 80 sets of Pareto solutions and obtain the optimized parameters of the mechanism. A comparative study is conducted with an elliptical-circular planetary gear mechanism using the hypocycloid trajectory. The results show that the improved mechanism reduces the maximum velocity by 7%, the maximum and minimum accelerations by 2% and 18%. After the quadratic optimization the distance error of the center point of suction cup and the labeling point is reduced from 1.3 mm to 0.12 mm, and the velocity during labeling and taking position is reduced from 0.10770 m s-1 to 0.0037 m s-1. The correctness of the proposed method is validated through simulation studies and experiments. This research provides a theoretical basis for the design and optimization of long label and curved surface labeling mechanism for vegetables.

Keywords: NSGA - II; cam-elliptical gear combined mechanism; entropy weight TOPSIS; improved hypocycloid trajectory; quadratic optimization.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Key Research and Development Project of China (Grant No. 2021YFD2000704).