Material Dispenser Control via 3D Sensor Feedback
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Solution Overview
Problem
Material dispensing systems face inaccuracies due to variations in flow rate, robotic mechanism speed, temperature, humidity, and material viscosity, leading to inconsistent material bead volumes and requiring lengthy trial-and-error setup processes.
Innovation Solution
A system and method that use sensors to generate three-dimensional data and characterize material dispenser parameters, adjusting flow rate inputs dynamically to maintain consistent bead volumes, incorporating a dynamical model and machine learning to adapt to changes and optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional material dispensing systems are used with fixed flow rates, then the system is simple to operate, but the material bead volume becomes inconsistent due to variations in speed, temperature, and material properties
Solution Approach 1:
The system transitions from fixed flow rate control to dynamic flow rate adjustment. The controller continuously modifies the flow rate based on real-time sensor feedback and robotic speed variations, enabling the system to adapt to changing conditions and maintain consistent bead volume despite variations in temperature, humidity, and material properties.
Solution Approach 2:
The system implements closed-loop feedback control where sensors continuously monitor the material bead characteristics and robotic mechanism speed, and the controller adjusts the flow rate accordingly. This feedback mechanism allows the system to compensate for deviations and maintain manufacturing precision without requiring complex manual intervention.
2Adaptability or versatility
If trial-and-error setup processes are used to calibrate dispensing parameters, then the system can adapt to different materials and conditions, but the setup time and production downtime increase significantly
Solution Approach 1:
The system performs preliminary characterization of the material dispenser by capturing sensor data at multiple different flow rates and storing this information for later use. This pre-characterization work eliminates the need for time-consuming trial-and-error setup processes, as the system already has reference data to guide flow rate adjustments for different materials and conditions.
Solution Approach 2:
The system automatically characterizes and calibrates itself without requiring expert intervention. The controller captures sensor data, processes the information, and adjusts dispensing parameters autonomously, enabling the system to adapt to different materials and environmental conditions while minimizing setup time and eliminating the need for experienced experts.
3Manufacturing precision
If expert operators manually adjust dispensing parameters, then the quality of material bead dispensing can be maintained, but the system requires highly skilled personnel and increases operational complexity
Solution Approach 1:
The system performs self-characterization and self-adjustment, capturing sensor data at multiple flow rates and automatically determining optimal dispensing parameters. This eliminates the need for expert operators to manually tune the system, making operation simpler while maintaining high material bead quality through automated control.
Solution Approach 2:
The system replaces manual expert adjustment with automated electronic control. The controller uses sensor feedback and stored characterization data to automatically adjust flow rates and dispensing parameters, substituting human expertise with an automated control system that maintains precision while simplifying operation.
4Manufacturing precision
If flow rate is increased to compensate for robotic mechanism speed variations, then material bead volume consistency can be maintained, but the system becomes less responsive to dynamic conditions
Solution Approach 1:
The system uses dynamic flow rate adjustment rather than fixed increases. The controller continuously modifies the flow rate in real-time based on actual robotic speed variations and sensor feedback, allowing the system to respond dynamically to changing conditions while maintaining bead volume consistency, rather than using static compensation strategies.
Data Source
AI summary
A method includes receiving a model of the material dispenser and, at a first characterization period of a material bead dispensing operation, communicating, to the material dispenser, a first characterization flow rate input. The method also includes, at a second characterization period of the material bead dispensing operation, communicating, to the material dispenser, a second characterization flow rate input. The method also includes generating, using at least one sensor, three-dimensional data associated with a material bead corresponding to the material bead dispensing operation. The method also includes characterizing at least one parameter of the model of the material dispenser using at least the first characterization flow rate input, the second characterization flow rate input, and the three-dimensional data associated with the material bead.