Effects of Comprehensive Eccentricity of Involute Cam on Gear Profile Deviations

The manufacturing accuracy of ultra-precision master gears signifies the technological capability of the ultra-precision gear.Currently,there is little report about the manufacturing technologies of ultra-precision master gears at home and aboard.In order to meet the requirement of grinding ultra precision master gear,the gear grinder with flat-faced wheel Y7125 is chosen as the object machine tool and the geometric model of its precision generating part,the involute cam,is established.According to the structure of the involute cam,the effective working section and its adjustable range of the cam are determined,and the mathematical expressions of the effects of comprehensive eccentricity of the involute cam on gear profile deviations are derived.According to the primary harmonic trends of the deviation curve,it is shown that gear profile form and slope deviations in different work generating sections of the involute cam are different which the latter changes with the cam eccentricity obviously.Then,the issues of extreme values and methods of error compensation are studied and the conclusion that large adjustable range is benefit to search the optimal involute-cam section which is responding to the minimum gear profile deviations is obtained.A group of examples are calculated by choosing master gears with d=120 mm and m=2-6 mm and an involute cam with base diameter djcam =117 mm.And it is found that the maximum gear profile deviation counts for no more than 5% of the cam eccentricity after error compensation.A gear-grinding experiment on the master gear with m=2 mm is conducted by choosing different sections of the involute cam and the differences of gear profile deviations then the existence of the cam eccentricity are verified.The research discloses the rule of gear profile deviations caused by the comprehensive eccentricity of the involute cam and provides the theoretical guidance and the processing methods for grinding profile of the ultra precision master gear.

Prediction of product roughness,profile,and roundness using machine learning techniques for a hard turning process

High product quality is one of key demands of customers in the field of manufacturing such as computer numerical control(CNC)machining.Quality monitoring and prediction is of great importance to assure high-quality or zero defect production.In this work,we consider roughness parameter Ra,profile deviation Pt and roundness deviation RONt of the machined products by a lathe.Intrinsically,these three parameters are much related to the machine spindle parameters of preload,temperature,and rotations per minute(RPMs),while in this paper,spindle vibration and cutting force are taken as inputs and used to predict the three quality parameters.Power spectral density(PSD)based feature extraction,the method to generate compact and well-correlated features,is proposed in details in this paper.Using the efficient features,neural network based machine learning technique turns out to be able to result in high prediction accuracy with R2 score of 0.92 for roughness,0.86 for profile,and 0.95 for roundness.