Deep-Learning Process Control for Multi-Station Quality Stability
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Solution Overview
Problem
Conventional manufacturing process controllers are limited in dynamically adjusting process parameters across multiple stations to optimize final product quality and reduce variability, as they rely on static algorithms and fail to consider trends and interactions between stations.
Innovation Solution
A deep-learning controller using machine-learning models evaluates control and output values to dynamically adjust station inputs, predicting optimal settings for consistent in-specification final outputs and reducing variability by analyzing universal, functional, and experiential priors, as well as real-time data from the manufacturing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional static algorithms are used for process control, then device complexity is reduced, but manufacturing precision and adaptability deteriorate
Solution Approach 1:
The patent replaces conventional mechanical/static control algorithms with an artificial intelligence-based deep-learning controller. The deep-learning controller uses neural networks to dynamically predict optimal control values for process stations, substituting traditional deterministic control mechanisms with adaptive intelligent systems that learn from historical process data to improve manufacturing precision
Solution Approach 2:
The patent implements dynamic parameter changes by continuously adjusting control values based on real-time process conditions and historical data analysis. The deep-learning controller modifies process parameters adaptively rather than using fixed static values, enabling the system to respond to changing conditions and maintain high manufacturing precision throughout the manufacturing process
2Adaptability or versatility
If deep-learning controllers are implemented, then adaptability and manufacturing precision improve, but device complexity increases
Solution Approach 1:
The deep-learning controller is designed as a universal control system that can manage multiple process stations across different manufacturing domains. The controller uses a standardized deep-learning architecture that can be applied to various manufacturing processes, providing multi-functional capability while managing complexity through reuse of the same core technology across different applications
Solution Approach 2:
The patent introduces a deep-learning controller as an intermediary layer between process stations and control algorithms. This intermediary uses historical process data and machine learning models to translate raw process information into optimized control decisions, simplifying the overall system architecture by centralizing intelligence in a dedicated control layer rather than distributing complexity across multiple stations
3Manufacturing precision
If real-time dynamic adjustments are made, then manufacturing precision improves, but loss of time for processing increases
Solution Approach 1:
The deep-learning controller performs preliminary actions by continuously analyzing historical process data and pre-computing optimal control strategies during periods when the manufacturing process is stable. The controller prepares prediction models and control recommendations in advance, so when real-time adjustments are needed, the system can quickly implement pre-planned control actions without extensive real-time computation, reducing processing time while maintaining precision
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.