A method for extracting a gear tooth profile edge based on an engagement-pixel image edge tracking method includes defining a transmission ratio relationship between a cutter and an envelope tooth profile, setting a cutter profile step size and an envelope step size, acquiring instantaneous contact images at different engaging times, and performing a binarization processing on each curve envelope cluster image; sweeping a boundary of an envelope curve cluster, acquiring pixel points of the edge; preliminarily tracking a tooth profile edge, and then performing a secondary extraction and compensation on the pixel points; calibrating coordinates of a cutter profile; extracting a pixel coordinate of an instantaneous engaging point; converting the pixel points among different instantaneous engaging images; extracting a final tooth profile coordinate of the gear, and performing a tooth shape error analysis and a contact line error analysis.

A tooth contact position adjustment amount estimation device that performs processing with respect to estimating a tooth contact position adjustment amount for dimensional data of parts constituting a power transmission mechanism according to the present invention, comprising: a machine learning device that performs processing with respect to estimating the tooth contact position adjustment amount for the dimensional data of parts constituting the power transmission mechanism, wherein the machine learning device observes part dimensional data, which is the dimensional data of parts constituting the power transmission mechanism, as a state variable indicating a current state of an environment, and performs processing with respect to estimating the tooth contact position adjustment amount for the dimensional data of parts constituting the power transmission mechanism in assembling the power transmission mechanism by performing processing with respect to machine learning based on the observed state variable.

A tooth contact position adjustment amount estimation device that performs processing with respect to estimating a tooth contact position adjustment amount for dimensional data of parts constituting a power transmission mechanism according to the present invention, comprising: a machine learning device that performs processing with respect to estimating the tooth contact position adjustment amount for the dimensional data of parts constituting the power transmission mechanism, wherein the machine learning device observes part dimensional data, which is the dimensional data of parts constituting the power transmission mechanism, as a state variable indicating a current state of an environment, and performs processing with respect to estimating the tooth contact position adjustment amount for the dimensional data of parts constituting the power transmission mechanism in assembling the power transmission mechanism by performing processing with respect to machine learning based on the observed state variable.

A method for extracting a gear tooth profile edge based on an engagement-pixel image edge tracking method includes defining a transmission ratio relationship between a cutter and an envelope tooth profile, setting a cutter profile step size and an envelope step size, acquiring instantaneous contact images at different engaging times, and performing a binarization processing on each curve envelope cluster image; sweeping a boundary of an envelope curve cluster, acquiring pixel points of the edge; preliminarily tracking a tooth profile edge, and then performing a secondary extraction and compensation on the pixel points; calibrating coordinates of a cutter profile; extracting a pixel coordinate of an instantaneous engaging point; converting the pixel points among different instantaneous engaging images; extracting a final tooth profile coordinate of the gear, and performing a tooth shape error analysis and a contact line error analysis.

A disclosed method utilizes virtual representations of gear profiles produced in view of accuracies and capabilities of specific machine and tool combinations to validate profile finishing parameters. The virtual representations are utilized to identify modifications needed to account for process capability and are implemented into the process to change the nominal profile utilized for producing the finished gear profiles. The resulting nominal gear profile accounts for process variations and thereby provides a more accurate and repeatable gear tooth profile.