Multi-Station Predictive Process Control for In-Spec Manufacturing
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Solution Overview
Problem
Conventional manufacturing process control systems are limited in dynamically adjusting inputs across multiple stations to ensure consistent production of in-specification final outputs, as they rely on static algorithms and fail to consider trends and interactions between stations, leading to increased variability and inefficiency.
Innovation Solution
A deep learning controller utilizing machine-learning models to predict and adjust control inputs across multiple stations in real-time, based on received control values, station values, and process values, to optimize the manufacturing process and ensure in-specification final outputs.
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 neural network learns optimal control strategies in real-time, adjusting control inputs based on current process states and historical data, thereby improving manufacturing precision while accepting increased system complexity.
Solution Approach 2:
The system changes the control approach by using machine learning models that can dynamically adjust multiple control parameters simultaneously. The neural network processes multiple input variables and generates optimized control outputs, enabling precise manipulation of process parameters to maintain output specifications despite varying conditions.
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 offline on historical process data before deployment, performing preliminary learning of optimal control strategies. This pre-training reduces the computational burden during real-time operation, as the model only needs to infer from learned patterns rather than perform complex calculations from scratch, thereby managing system complexity while maintaining high adaptability.
Solution Approach 2:
The system uses a virtual model (neural network) that copies and learns from historical process behavior and outcomes. This virtual representation allows the system to simulate and predict process responses without physically experimenting, enabling adaptive control while keeping the physical system complexity manageable.
3Productivity
If real-time predictions are made across multiple stations, then productivity and response time improve, but measurement precision requirements and computational load increase
Solution Approach 1:
The machine learning controller is designed as a universal system that handles multiple stations and various types of control inputs simultaneously. The neural network architecture processes diverse data types (process values, control values, station states) through a unified framework, reducing the need for station-specific measurement systems and enabling efficient multi-station control with standardized precision requirements.
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.