Adaptive Calibration Monitoring for Early Process Drift Detection
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Solution Overview
Problem
Existing Process Analytical Technology (PAT) systems face challenges in continuously improving their calibration models to account for process variance and raw material variability, leading to potential product quality drift and inefficiencies in manufacturing processes.
Innovation Solution
A method that involves monitoring process parameters against a calibration model to identify deviations and initiate actions, with the ability to adapt the model over time using real-time data, allowing for improved process robustness, product consistency, and flexibility in handling equipment and material changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed calibration model is used to monitor process parameters, then the monitoring system is simple to implement, but it cannot adapt to process variance and raw material variability, leading to product quality drift
Solution Approach 1:
The calibration model is transformed from a static fixed model to a dynamic adaptive model that automatically adjusts to process changes. The system continuously updates the calibration model using real-time process data, allowing it to adapt to process variance and raw material variability while maintaining operational simplicity through automated adjustments.
Solution Approach 2:
The system implements feedback mechanisms where process monitoring data is continuously fed back into the calibration model for automatic updates. This feedback loop enables the model to learn from actual process variations and adjust accordingly, improving adaptability without requiring manual intervention or complex reconfiguration.
2Reliability
If traditional PAT systems monitor only expected ranges, then false alarms are reduced, but process drift cannot be detected early, leading to quality issues
Solution Approach 1:
The system performs preliminary detection of process drift by monitoring for deviations from expected patterns before they result in actual quality failures. By identifying early signs of drift through the adaptive calibration model, the system can trigger preventive actions to maintain product quality consistency and avoid quality issues before they occur.
3Manufacturing precision
If the calibration model is frequently updated to improve accuracy, then product quality consistency improves, but system complexity and computational requirements increase
Solution Approach 1:
The calibration model updating process is made self-service through automated algorithms that continuously refine the model using incoming process data. The system independently performs model updates without requiring manual intervention, expert analysis, or complex configuration, thereby improving manufacturing precision while keeping the operational complexity manageable through automation.
Data Source
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AI summary
Aspects and embodiments relate to a method of monitoring, a monitoring unit (300) configured to perform the monitoring method and a computer program product configured to perform the monitoring method. The method of monitoring is a method of monitoring at least one device configured to perform one or more process (110, 120, 130, 140) to produce a product (200). At least one process is associated with at least one process parameter. The product producible by the one or more process has at least one product quality attribute which is modifiable in dependence upon the at least one process parameter. The method of monitoring comprises: receiving an indication of the at least one process parameter; checking the indication against a calibration model to determine whether the at least one process parameter results in a product quality attribute within a preselected range (650). The calibration model comprises: a mapping between the at least one process parameter and the at least one product quality attribute; the mapping defining: a first range of the at least one process parameter indicative that a product will have a product quality attribute within the preselected range (650); and a second range (1050, 1060) within the first range , the second range of the at least one process parameter being indicative that the at least one process parameter is within an expected range. The method of monitoring further comprising: initiating one or more action if the received indication of the at least one process parameter checked against the calibration model is determined to map to a position inside the first range and outside the second range.