Deep-Learning Process Control for Multi-Station Output Variability
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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 in final product quality and inefficiencies in producing in-specification outputs.
Innovation Solution
A deep-learning controller using machine-learning models evaluates control and output values to dynamically adjust station controllers, predicting and optimizing the manufacturing process to consistently produce in-specification final outputs by identifying key influencers and making real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional controllers are used to control process stations, then the control system is simple and easy to implement, but the manufacturing precision and product quality consistency deteriorate due to inability to dynamically adjust based on multiple station inputs
Solution Approach 1:
A deep-learning controller is introduced as an intermediary between multiple process stations and the final output. This controller receives inputs from multiple stations, processes them through machine learning models to predict output values, and generates control adjustments. The deep-learning controller acts as a mediator that coordinates multiple stations dynamically, improving manufacturing precision without requiring complex modifications to each individual station controller.
Solution Approach 2:
The system dynamically changes control parameters based on real-time inputs from multiple process stations. The deep-learning controller adjusts control values dynamically according to the actual state of stations and predicted output values, enabling adaptive control that maintains high manufacturing precision while managing system complexity through intelligent parameter optimization.
2Productivity
If conventional controllers are used, then the device complexity is low, but the productivity deteriorates due to inefficiencies in producing in-specification outputs
Solution Approach 1:
The deep-learning controller performs preliminary predictions of output values before actual manufacturing completion. By predicting whether the final output will be in-specification based on current station inputs, the system can proactively adjust control parameters to ensure compliance, thereby improving productivity by preventing out-of-specification production and reducing waste.
Solution Approach 2:
The system implements a feedback mechanism where the deep-learning controller continuously receives inputs from process stations, compares predicted output values against specification limits, and generates corrective control adjustments. This closed-loop feedback system improves productivity by automatically correcting deviations and ensuring consistent production of in-specification outputs.
3Manufacturing precision
If deep-learning controller is implemented to dynamically adjust stations, then the manufacturing precision improves, but the device complexity increases
Solution Approach 1:
The deep-learning controller operates autonomously to self-adjust control parameters without requiring manual intervention or complex external control systems. The machine learning model continuously learns from process data and automatically optimizes control decisions, improving manufacturing precision while managing complexity through self-service operation.
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.