Biologic Growth Feedback Control for Final Quality Consistency
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Solution Overview
Problem
Current systems for controlled biological growth lack the ability to dynamically adjust process parameters in real-time based on the biologic's growth state, leading to potential deviations from desired final quality metrics.
Innovation Solution
A controlled growth system comprising a controller, sensor, and computing system that monitors the biologic's growth through image analysis and sensor data, predicts the final quality metric, and adjusts process parameters to achieve a canonical quality metric by providing an updated set of parameters to the controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If real-time monitoring and dynamic adjustment of process parameters is implemented, then manufacturing precision and reliability of final quality metrics improve, but device complexity increases
Solution Approach 1:
The system implements continuous feedback loops where sensors monitor biologic growth parameters in real-time, the computing system analyzes the data and predicts final quality metrics, and the controller dynamically adjusts process parameters based on deviations from desired outcomes. This closed-loop feedback mechanism ensures consistent achievement of quality metrics while managing system complexity through automated decision-making algorithms.
Solution Approach 2:
The computing system performs preliminary analysis of sensor data to predict final quality metrics before the growth process completes. By forecasting potential quality deviations early in the growth cycle, the system can proactively adjust process parameters to prevent quality failures, thereby improving manufacturing precision without requiring complete process interruption or complex end-stage corrections.
2Reliability
If continuous monitoring and adjustment of process parameters is performed, then reliability of achieving canonical quality metric improves, but use of energy increases
Solution Approach 1:
The system employs periodic monitoring and adjustment cycles rather than continuous operation at maximum intensity. Sensors collect data at optimized intervals, the computing system processes information in batches, and controllers adjust parameters at strategic moments during the growth process. This periodic approach maintains reliable quality achievement while significantly reducing energy consumption compared to continuous full-power operation.
Solution Approach 2:
The system dynamically adapts monitoring and adjustment frequency based on growth stage and detected variability. During critical growth phases or when deviations are detected, monitoring intensity increases to ensure quality metrics are met. During stable phases, monitoring frequency decreases, reducing energy consumption. This dynamic resource allocation maintains reliability while optimizing energy use throughout the growth process.
Data Source
AI summary
A controlled growth system is provided herein. The controlled growth system includes a controlled growth environment, a controller, a sensor, and a computing system. The controlled growth environment is configured to grow a biologic. The controller is in communication with the controlled growth environment. The controller is configured to manage process parameters of the controlled growth environment. The sensor is configured to monitor the biologic during a growth process. The computing system is in communication with the sensor and the controller. The computing system is programmed to perform operations for achieving a desired final quality metric for the biologic.