Process Trajectory Monitoring for Real-Time Quality Adjustment
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Solution Overview
Problem
Existing methods for ensuring reproducibility and repeatability in process execution are costly and time-consuming, especially in small-scale facilities, and lack flexibility for both small- and large-scale processes, with existing optimization methods failing to address the determination of a trajectory path for process variables.
Innovation Solution
A computer-implemented method for determining a preferred trajectory model by measuring and weighting process variables over time, comparing them to an ideal trajectory, and providing real-time feedback to adjust processes for success, using environmental sensors and data management systems to track and analyze process variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional quality control methods (e.g., Six Sigma) are implemented to ensure process reproducibility and repeatability, then process reliability is improved, but implementation cost and time increase significantly
Solution Approach 1:
The system performs preliminary actions by establishing a reference trajectory model before actual process execution. Multiple historical process runs are collected and analyzed to create an ideal trajectory that represents the optimal process path. This preliminary model serves as a benchmark for real-time comparison during subsequent process executions, enabling proactive quality assurance rather than reactive correction.
Solution Approach 2:
The system implements continuous feedback by comparing real-time process variable trajectories against the pre-established reference trajectory model. Deviations from the ideal path are detected and fed back to the control system, which can then adjust process parameters to maintain reproducibility. This closed-loop feedback mechanism ensures consistent process outcomes without requiring extensive quality control personnel.
2Manufacturing precision
If comprehensive quality control methodologies are applied to monitor all process variables, then manufacturing precision is improved, but device complexity and implementation cost increase
Solution Approach 1:
The system extracts only the essential information needed for quality control by comparing process trajectories against a reference model. Instead of analyzing every single process variable in detail, the system focuses on detecting deviations from the ideal trajectory path. This extraction approach maintains manufacturing precision while reducing the complexity of the monitoring system, as it only needs to identify whether the process is following the reference path rather than analyzing all variables comprehensively.
3Reliability
If real-time monitoring and adjustment of process variables is implemented, then process reliability is improved, but loss of time for data collection and analysis increases
Solution Approach 1:
The system applies partial action by monitoring process variables only to the extent necessary for trajectory comparison. Instead of performing exhaustive analysis of all process data, the system focuses on detecting whether the current process path deviates from the reference trajectory. This selective monitoring approach maintains high process success rates while minimizing data collection and processing time, as the system only needs to determine if the process is on or off the ideal path rather than analyzing every detail.
Data Source
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AI summary
A method for determining whether a given run of a process having a defined protocol is on a trajectory for successful completion is provided. The method includes the step of initiating a run of the defined protocol of the process. During the initiated run, obtaining information reflecting variables that may affect the quality of the process. A preferred trajectory model for achieving a successful implementation of a process is also obtained. The information reflecting the variables that affect the quality of the process are compared with the preferred trajectory model. This comparison allows a determination of offset of the value of the determined variables to the value of the same variables of the preferred trajectory model. The magnitude or amount of offset is indicative of the whether the run initiated in step is on a path or trajectory for success.