Deep-Learning Process Control for Multi-Station Manufacturing Precision
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Solution Overview
Problem
Conventional manufacturing process controllers are limited in dynamically adjusting process stations based on inputs and outputs from multiple stations, leading to variability and inefficiency in producing in-specification final outputs, 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 values for process stations, integrating universal, functional, and experiential priors to optimize the manufacturing process, reduce variability, and ensure consistent production of in-specification final outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional static algorithms are used to control process stations, then device complexity is reduced, but manufacturing precision and adaptability deteriorate due to inability to dynamically adjust based on multiple station inputs and outputs
Solution Approach 1:
The patent replaces conventional static control algorithms with a deep-learning-based predictive process control system. The deep-learning controller uses machine-learning models to dynamically predict optimal control values for process stations, substituting traditional mechanical control methods with intelligent algorithms that can adapt to varying conditions and improve manufacturing precision.
Solution Approach 2:
The system dynamically changes control parameters by using the deep-learning controller to predict optimal control values based on current process conditions and historical data. The controller adjusts control values for process stations in real-time, transforming static parameter settings into dynamic, adaptive parameters that respond to changing manufacturing conditions.
2Adaptability or versatility
If deep-learning controllers with machine-learning models are implemented, then adaptability and manufacturing precision improve, but device complexity increases due to integration of multiple control systems and data processing requirements
Solution Approach 1:
The deep-learning controller is designed as a universal control system that can manage multiple process stations with different functions. The controller uses a single machine-learning model framework that adapts to various station types and control requirements, providing multi-functional capability while maintaining a unified control architecture.
Solution Approach 2:
The patent introduces a confidence level assessment mechanism as an intermediary between the deep-learning predictions and actual control actions. The controller determines whether to trust its predictions based on confidence levels, acting as a mediator that filters and validates machine-learning outputs before applying them to process control, thereby managing system complexity through structured decision-making.
3Productivity
If deep-learning controller predictions are trusted without verification, then productivity increases through faster control decisions, but reliability decreases due to potential prediction inaccuracies
Solution Approach 1:
The system implements feedback through confidence level assessment, where the deep-learning controller continuously evaluates the reliability of its own predictions. The controller uses feedback from process measurements and historical performance to adjust confidence levels, enabling faster decision-making when predictions are accurate while triggering verification when confidence is low.
Solution Approach 2:
The controller performs preliminary assessment of prediction confidence before executing control actions. By evaluating confidence levels in advance, the system can quickly proceed with high-confidence predictions while pre-identifying cases that require additional verification, thus maintaining productivity while ensuring reliability.
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 control values associated with a process station in a manufacturing process, predicting an expected value for an article of manufacture output from the process station, and determining if the deep-learning controller can control the manufacturing process based on the expected value. Systems and computer-readable media are also provided.