A Simplified Formulation to Estimate Influence of Gearbox Parameters on the Rattle Noise

Occurrence of gear rattle in transmission systems can result in severe vibration and noise,which in applications such as automobiles is an important source of user discomfort.As a result,the reduction of the rattling noise has attracted lot of concerns.The rattling noise level is affected by several gearbox parameters,an understanding of which is essential to prevent the expensive design modifications at later stages of product development.To develop such understanding at the gearbox design stage,this paper analytically evaluates the gear parameters’effect on the root mean square of the wheel gear acceleration under idling condition,which is known to be linearly correlated to the rattling noise level.Therefore,this evaluation allows for an investigation of the gear parameters’influence on the rattling noise as well.This method is then verified by comparing the analytical results with the simulation results from a dynamic model built in SIMPACK as well as previously published experimental results.Thus,the proposed analytical evaluation method can optimize the gearbox specifications at the design stage to reduce the gear rattle noise level.

An improved binocular localization method for apple based on fruit detection using deep learning

Apple picking robot is now being developed as an alternative to hand picking due to a great demand for labor during apple harvest season.Accurate detection and localization of target fruit is necessary for robotic apple picking.Detection accuracy has a great influence on localization results.Although current researches on detection and localization of apples using traditional image algorithms can obtain good results under laboratory conditions,it is difficult to accurately detect and locate objects in natural field with complex environments.With the rapid development of artificial intelligence,accuracy of apple detection based on deep learning has been significantly improved.Therefore,a deep learningbased method was developed to accurately detect and locate the position of fruit.For different localization methods,binocular localization is a widely used localization method for its bionic principle and lower equipment cost.Hence,this paper proposed an improved binocular localization method for apple based on fruit detection using deep learning.First,apples of binocular images were detected by Faster R-CNN.After that,a segmentation based on chromatic aberration and chromatic aberration ratio was applied to segment apple and background pixels in bounding box of detected fruit.Furthermore,template matching with parallel polar line constraint was used to match apples in left and right images.Finally,two feature points on apples were selected to directly calculate three dimensional coordinates of feature points with the binocular localization principle.In this study,Faster R-CNN achieved an AP of 88.12%with an average detection speed of 0.32 s for an image.Meanwhile,standard deviation and localization precision of depth of two feature points on each apple were calculated to evaluate localization.Results showed that the average standard deviation and the average localization precision of 76 groups of datasets were 0.51 cm and 99.64%,respectively.Results indicated that the proposed improved binocular localization method is promising for fruit localization。