Planter Seed Depth Actuator Homing by Stall Detection
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Solution Overview
Problem
Manual adjustments to seed depth devices in agricultural planters are prone to human error and require significant resources and time, leading to inconsistencies in actuator positioning.
Innovation Solution
A seed depth auto-learn system that uses a computing processor and storage medium to automatically determine and verify the home position of an actuator by detecting stalls and adjusting its position based on motor revolutions, reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustments are used to set actuator home position, then the device can be operated, but human error and inconsistencies occur leading to poor positioning precision
Solution Approach 1:
The system performs self-learning to automatically determine the actuator home position and travel distance without manual intervention. The controller autonomously executes the learning routine by moving the actuator to extreme positions, detecting stalls, and calculating parameters, thereby eliminating human error and ensuring consistent, precise positioning.
Solution Approach 2:
The system uses feedback from motor current sensors to detect when the actuator stalls against stops or obstructions. This feedback mechanism allows the controller to accurately identify extreme positions and calculate the home position and full travel distance, improving both precision and reliability of actuator positioning.
2Measurement precision
If manual adjustments are performed to achieve correct home position, then positioning can be set, but considerable resources and time are consumed
Solution Approach 1:
The system performs preliminary self-learning during initial setup or after maintenance to automatically establish the actuator home position and full travel distance. This preliminary automated calibration eliminates the need for time-consuming manual adjustments later, saving resources and time while ensuring accurate positioning.
3Ease of operation
If manual adjustment procedures are used, then the actuator can be positioned, but human error leads to inconsistencies
Solution Approach 1:
The system autonomously performs the positioning learning process without requiring manual intervention. The controller automatically moves the actuator, detects stall conditions through current feedback, calculates the home position and travel distance, and stores these parameters, ensuring consistent and reliable positioning while simplifying operation.
Solution Approach 2:
The system replaces manual mechanical adjustment procedures with an automated electronic control system. The controller uses motor current sensing and electronic calculations to determine actuator positions, substituting human-operated mechanical adjustments with reliable electronic automation that eliminates human error.
Data Source
AI summary
A seed depth device for an agricultural planter includes an electric motor. The seed depth device also includes an actuator driven by the electric motor and arranged to move to a home position. The seed depth device further includes a first stop proximate to the actuator when in the home position. The seed depth device yet further includes at least one controller including a computing processor and a computer readable storage medium configured to control the electric motor. The seed depth device also includes a seed depth auto-learn system at least in-part stored and executed by the at least one controller, the seed depth auto-learn system configured to learn the home position based at least in-part on the actuator stalling upon the first stop, wherein the seed depth auto-learn system is configured to move the actuator a prescribed distance away from the first stop to establish the home position.