Predictive Material Dynamics Control for Mobile Machine Spillage
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Solution Overview
Problem
Mobile machines experience material shifting and spillage due to various factors such as operation speed, terrain characteristics, and material properties, leading to instability, increased load, and reduced profitability.
Innovation Solution
A predictive model is generated using in-situ data from sensors on the mobile machine to predict and control material dynamics, including movement and spillage, by modeling relationships between machine orientation, speed, crop moisture, and material mass, using maps of the worksite to adjust travel speed, acceleration, deceleration, and material fill levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the mobile machine operates at higher speeds, then productivity is improved, but material spillage and movement increase
Solution Approach 1:
The system performs preliminary detection of material dynamics characteristics and predicts future material movement or spillage events before they occur. Based on these predictions, the control system proactively adjusts operational parameters such as reducing speed or modifying acceleration patterns to prevent spillage, rather than reacting after spillage has occurred.
Solution Approach 2:
The system continuously monitors material dynamics characteristics using sensors and feeds this information back to the control system. The control system processes this feedback through the predictive model and adjusts operational parameters in real-time to maintain optimal conditions that prevent material spillage while preserving productivity.
2Adaptability or versatility
If the mobile machine operates on steep terrain, then access to more worksite areas is improved, but material spillage increases due to machine orientation changes
Solution Approach 1:
The system detects changes in machine orientation and worksite topography in advance, predicts the resulting material dynamics changes, and adjusts operational parameters proactively to compensate for the increased spillage risk on steep terrain before the machine enters challenging areas.
Solution Approach 2:
The control system dynamically changes operational parameters such as speed, acceleration, and deceleration rates based on detected worksite conditions and predicted material dynamics. When operating on steep terrain, the system automatically adjusts these parameters to maintain material stability while preserving the ability to access difficult areas.
3Productivity
If the material fill level is increased, then productivity is improved, but material movement and spillage increase
Solution Approach 1:
The system dynamically adjusts the material fill level based on real-time detection of material dynamics characteristics and predictive modeling. Rather than maintaining a fixed fill level, the system optimizes the fill level dynamically to maintain material stability while maximizing capacity utilization, adjusting the balance between productivity and stability as operating conditions change.
Solution Approach 2:
The control system changes the material fill level parameter dynamically based on detected worksite conditions, machine orientation, and predicted material behavior. When conditions indicate high spillage risk, the system automatically reduces fill level; when conditions are favorable, it increases fill level to maximize productivity.
4Loss of substance
If in-situ sensors and predictive modeling are implemented, then material spillage is reduced, but device complexity increases
Solution Approach 1:
The system employs multi-functional sensors that detect multiple characteristics (material position, machine orientation, worksite topography) and a predictive model that handles various material dynamics scenarios. This universal approach reduces the need for multiple specialized sensors and complex dedicated control systems for each specific spillage prevention task.
Solution Approach 2:
The predictive model automatically processes sensor data, predicts material dynamics, and generates control adjustments without requiring complex external intervention or manual calibration. The system self-calibrates and adapts to different operating conditions, reducing the complexity of installation, maintenance, and operation.
Data Source
Figure 1
Figure 2A~2B
Figure 2C
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.