Predictive Model Adaptation for Manufacturing Data Drift
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Solution Overview
Problem
Predictive models in manufacturing environments become outdated and inaccurate due to changes in the underlying conditions, leading to reduced predictive power and inefficiencies in part acceptance/rejection decisions.
Innovation Solution
An adaptive tuning system that estimates data drift and generates an updated predictive model using electronic processors to continuously adapt and improve the model's accuracy based on new data points, ensuring accurate part classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a predictive model is trained using historical data, then the model can make accurate determinations initially, but over time the model becomes out-of-date and provides inaccurate determinations due to changes in underlying manufacturing conditions
Solution Approach 1:
The patent implements dynamic model adaptation by continuously updating the predictive model with new data points from the manufacturing process. The system transitions from a static historical model to a dynamic model that evolves with changing manufacturing conditions, maintaining prediction accuracy through ongoing adaptation to new environmental parameters and process variations.
Solution Approach 2:
The system employs feedback mechanisms by comparing actual test results with model predictions, identifying discrepancies, and using these feedback signals to retrain and update the predictive model. This closed-loop approach ensures the model continuously learns from new data and corrects its own inaccuracies, maintaining reliability over time.
2Reliability
If the predictive model is continuously updated with new data, then the model maintains accuracy in changing environments, but the complexity of the system increases due to the need for continuous adaptation mechanisms
Solution Approach 1:
The predictive model performs self-updating by automatically incorporating new data points and adapting to changing conditions without requiring manual intervention. The system autonomously identifies when updates are needed, selects relevant new data, and retrains the model, reducing the operational complexity of managing model updates while maintaining high reliability.
3Measurement precision
If traditional testing methods are used for every part, then accurate classification is achieved, but the production time increases and efficiency decreases
Solution Approach 1:
The system applies partial testing by using the predictive model to classify the majority of parts without full testing, reserving comprehensive testing only for cases where the model's confidence is low or discrepancies are detected. This selective approach maintains high classification accuracy while significantly reducing overall production time and increasing throughput.
Data Source
AI summary
Methods, systems, and apparatuses for adapting a predictive model for a manufacturing process. One method includes receiving, with an electronic processor, a plurality of data points for a plurality of manufactured parts and the predictive model. The predictive model outputs a label for a manufactured part provided by the manufacturing process indicating whether the manufactured part is accepted or rejected. The method also includes estimating, with the electronic processor, a drift for each of the plurality of data points and generating, with the electronic processor, an adapted version of the predictive model based on the predictive model and the drift for each of the plurality of data points. In addition, the method includes outputting, with the electronic processor, a label for each of the plurality of manufactured parts using the adapted version of the predictive model.