Predictive Material Dynamics Control for Mobile Machines
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Solution Overview
Problem
Mobile machines face challenges with material dynamics, such as instability, increased load on components, and material spillage due to factors like machine operation, terrain characteristics, and material properties, which affect performance and profitability.
Innovation Solution
In-situ sensors detect characteristics like machine orientation, speed, crop moisture, and material mass to generate predictive models that control mobile machines, preventing material movement and spillage by adjusting travel parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the mobile machine operates on varied terrain at higher speeds, then productivity increases, but material spillage and instability increase
Solution Approach 1:
The system performs preliminary detection of material dynamics characteristics (movement, spillage, load) before critical events occur. Sensors continuously monitor material behavior and predict future states, allowing the control system to take preventive action by adjusting travel parameters before spillage or instability occurs
Solution Approach 2:
The system implements closed-loop feedback by continuously detecting material dynamics characteristics and using this information to adjust machine operation. The control system receives real-time data from sensors about material movement and spillage, then modifies travel speed and other parameters to maintain optimal operation while preventing material loss
2Productivity
If the mobile machine operates at higher speeds, then productivity increases, but material movement and instability increase
Solution Approach 1:
The system dynamically adjusts travel parameters based on real-time material dynamics characteristics. Rather than using fixed speed limits, the control system continuously modifies operation parameters in response to changing material behavior, terrain conditions, and machine state to maintain stability while maximizing productivity
Solution Approach 2:
The system changes operational parameters (travel speed, acceleration, route) based on detected material characteristics and dynamics. By adjusting these parameters in response to real-time sensor data about material movement and machine stability, the system maintains optimal operation across varying conditions
3Productivity
If the mobile machine carries larger material loads, then productivity increases, but component load and instability increase
Solution Approach 1:
The system detects material mass and dynamics characteristics in advance to predict component loading conditions. By identifying high-risk situations before they occur, the control system can adjust operation parameters to prevent excessive component loads while maintaining optimal transport capacity
Data Source
AI summary
A first in-situ sensor detects a characteristic value as a mobile machine operates at a worksite. A second in-situ sensor detects a material dynamics characteristic value as the mobile machine operates at the worksite. A predictive model generator generates a predictive model that models a relationship between the characteristic and the materials dynamics characteristic based on the characteristic value detected by the first in-situ sensor and the material dynamics characteristic value detected by the second in-situ sensor. The predictive model can be output and used in automated machine control.