Predictive Multi-Station Process Control for In-Spec Manufacturing Output
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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 in producing in-specification products, as they rely on static algorithms and fail to consider trends across multiple 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 by dynamically controlling station operations based on real-time data and feedback.
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 varying conditions
Solution Approach 1:
The patent replaces conventional static control algorithms with a deep learning-based predictive process control system. The DNN controller learns optimal control strategies from historical data and dynamically adjusts control inputs to maintain output specifications, substituting rigid mechanical control logic with adaptive intelligent control that improves manufacturing precision without requiring complex hardware modifications
Solution Approach 2:
The system dynamically changes control parameters based on predicted process outcomes. The DNN controller continuously adjusts control inputs (parameters) to compensate for variations in process conditions, material properties, and environmental factors, thereby maintaining output specification compliance while adapting to changing conditions
2Adaptability or versatility
If deep learning models are deployed for predictive control, then manufacturing precision and adaptability improve, but device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary training of the deep learning model using historical process data before deployment. This preliminary action allows the DNN to learn optimal control strategies in advance, so that during actual operation, the controller can quickly adapt to new conditions without requiring complex real-time computation, thereby improving adaptability while managing system complexity
Solution Approach 2:
The patent introduces an intermediary layer between the DNN controller and the physical process stations. This intermediary layer handles data preprocessing, feature extraction, and control signal generation, which simplifies the overall system architecture by separating the complex intelligence functions from the physical control implementation
3Productivity
If real-time predictive control is implemented, then productivity and quality improve, but loss of time for data collection and processing increases
Solution Approach 1:
The system collects and processes historical process data in advance to train the deep learning model. This preliminary data preparation allows the model to be pre-trained with comprehensive patterns and relationships, so that during real-time operation, predictions can be made quickly using the already-learned knowledge, reducing real-time data processing time while maintaining high output quality
Solution Approach 2:
The DNN controller focuses on predicting and controlling only the critical process parameters that have the most significant impact on output quality. By concentrating computational resources on the most influential variables rather than processing all possible data, the system achieves high productivity with reduced data processing time
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.