Deep Learning Process Control for Multi-Station Variability Reduction
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Solution Overview
Problem
Conventional manufacturing process control systems are limited in dynamically adjusting inputs across multiple stations to optimize final outputs, leading to variability and inefficiency, as they primarily rely on static algorithms and fail to consider trends and interactions between stations.
Innovation Solution
A deep learning controller utilizing machine-learning models to predict and adjust control inputs across multiple stations, identifying key influencers and optimizing the manufacturing process to ensure in-specification final outputs while reducing variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional static algorithms are used for process control, then system simplicity is maintained, but manufacturing precision and adaptability deteriorate due to inability to dynamically adjust to changing conditions
Solution Approach 1:
The control system transitions from static algorithms to dynamic machine learning models that continuously adapt to changing process conditions. The deep learning controller dynamically adjusts control inputs based on real-time predictions of output specifications, enabling the system to respond to varying conditions while maintaining precision.
Solution Approach 2:
The system changes the control parameters dynamically by using machine learning models to predict optimal control inputs. Instead of fixed parameter settings, the system continuously adjusts parameters based on predicted output specifications and actual process measurements, improving manufacturing precision through adaptive parameter optimization.
2Adaptability or versatility
If machine learning models are deployed for predictive control, then manufacturing precision and adaptability improve, but device complexity and computational requirements increase
Solution Approach 1:
The machine learning models are trained in advance on historical process data to learn optimal control strategies. This preliminary training phase allows the models to be deployed with pre-acquired knowledge, reducing the complexity of real-time decision-making while maintaining high adaptability to changing conditions.
Solution Approach 2:
The deep learning controller acts as an intermediary layer between process measurements and control inputs. It processes complex relationships between multiple variables and translates them into actionable control signals, managing system complexity while enhancing adaptability through intelligent mediation.
3Manufacturing precision
If real-time predictions are made across multiple stations, then manufacturing precision improves, but loss of time for data processing increases
Solution Approach 1:
Machine learning models perform predictions based on pre-processed features and historical patterns learned during training. This preliminary preparation of predictive capabilities allows real-time inference to be performed quickly, maintaining manufacturing precision while minimizing computation time during actual control operations.
Solution Approach 2:
The control system is divided into multiple deep learning controllers, each responsible for specific stations or process segments. This segmentation allows parallel processing of predictions across different stations, reducing overall computation time while maintaining accurate predictive control for each segment.
4Productivity
If control inputs are adjusted across multiple stations, then productivity improves through optimized process flow, but device complexity increases due to coordinated control requirements
Solution Approach 1:
The deep learning controllers are designed with universal functionality to handle multiple stations and process variables. Each controller can manage diverse control tasks across different stations, reducing the need for specialized control systems for each station and simplifying the overall coordination complexity while improving productivity.
Data Source
AI summary
Aspects of the disclosed technology encompass the use of a deep learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process, predicting an expected value for an intermediate or final output of an article of manufacture, based on the control values, and determining if the predicted expected value for the article of manufacture is in-specification. In some aspects, the process can further include steps for generating control inputs if the predicted expected value for the article of manufacture is not in-specification. Systems and computer-readable media are also provided.