Motor Selection Database Grouping by Load Inertia
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing servo motor selection methods are inefficient and time-consuming when trying to match rotation speed, continuous torque, and load inertia moment requirements.
Innovation Solution
A motor selection method that references a database to categorize motors by upper limit values of load inertia moment, allowing for quick selection of motors that meet specific speed, torque, and inertia moment conditions by dividing data into groups based on these criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the existing servo motor selection method processes torque specification comparison after speed specification comparison, then the selection process follows a conventional sequence, but the time required to select a motor matching all items (rotation speed, continuous torque, and load inertia moment) becomes excessively long
Solution Approach 1:
The patent segments the motor selection process by dividing motors into multiple groups based on load inertia moment ratios. This segmentation allows the selection process to first filter by group based on inertia moment requirements, then select from within the appropriate group based on torque and speed specifications. This segmented approach significantly reduces the time required to find a matching motor compared to checking all motors sequentially.
Solution Approach 2:
The patent performs preliminary classification of motors into groups based on load inertia moment ratios before the actual selection process. This preliminary action organizes the motor database in advance, so that when a selection is needed, the system can quickly identify the appropriate group and make selections without evaluating all motors from scratch, thereby reducing selection time.
2Measurement precision
If the motor selection process evaluates all motors against all specifications (rotation speed, continuous torque, and load inertia moment), then comprehensive matching is achieved, but the complexity of the selection process increases
Solution Approach 1:
By segmenting motors into groups based on load inertia moment ratios, the patent reduces the complexity of evaluating all motors against all specifications. The segmentation creates a hierarchical structure where the first level filters by inertia moment group, and the second level within each group evaluates torque and speed specifications. This maintains comprehensive matching accuracy while reducing process complexity.
Solution Approach 2:
The patent extracts the load inertia moment ratio as a key classification criterion and separates it from the torque and speed evaluation. By taking out the inertia moment criterion and using it for preliminary grouping, the complex multi-parameter selection problem is decomposed into simpler sub-problems: first select the appropriate group, then select the specific motor within that group based on torque and speed.
Data Source
AI summary
A motor selection method includes: referring to a database containing data pertaining to rated speeds, continuous rated torques, and upper limit values of load inertia moment of a plurality of motors; dividing the database, based on the upper limit values of load inertia moment; obtaining information pertaining to a required rotation speed, continuous rated torque, and load inertia moment; selecting one group from a plurality of groups; and selecting a motor meeting the following conditions.rotation speed required of motor≤rated speedcontinuous torque required of motor≤continuous rated torqueload inertia moment required of motor≤upper limit value of load inertia moment.