Predictive Process Control Thresholds for Real-Time KPI Optimization
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Solution Overview
Problem
Manufacturing processes in factories face challenges in consistently meeting design specifications due to the limitations of traditional process controllers, which rely on static algorithms and fail to optimize key performance indicators (KPIs) in real-time, leading to inefficiencies and waste.
Innovation Solution
A manufacturing system incorporating a deep learning processor that simulates manufacturing processes to generate expected values and target values for KPIs, allowing for dynamic adjustments to processing parameters to optimize KPIs, thereby enhancing process control and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional process controllers with static algorithms are used, then device complexity is reduced, but manufacturing precision and productivity deteriorate due to inability to optimize KPIs in real-time
Solution Approach 1:
The patent implements dynamic process control by training a neural network model on historical process data to predict optimal processing parameters in real-time. The model adapts to changing conditions by continuously learning from new data, transforming the static control system into a dynamic one that optimizes manufacturing precision without requiring overly complex hardware modifications.
Solution Approach 2:
The patent replaces traditional mechanical/statical control algorithms with an intelligent software-based neural network system. This substitution allows the system to process complex patterns and relationships in manufacturing data that static algorithms cannot capture, improving manufacturing precision through data-driven insights while keeping the physical control infrastructure relatively simple.
2Productivity
If real-time optimization of KPIs is implemented, then productivity is improved, but device complexity increases due to need for advanced control systems
Solution Approach 1:
The neural network model operates autonomously to predict optimal processing parameters and guide process adjustments without requiring constant human intervention or complex centralized control. The system serves itself by continuously learning from process data and automatically adapting to optimize productivity, reducing the need for overly complex control architectures.
Solution Approach 2:
The system implements feedback loops where process outcomes are continuously monitored and fed back into the neural network model. This feedback mechanism allows the model to learn from actual results and refine its predictions, enabling real-time productivity optimization through a relatively simple feedback-based architecture rather than complex predictive modeling infrastructure.
3Manufacturing precision
If deep learning simulation is used to generate expected values, then manufacturing precision is improved, but loss of computation time increases during training phase
Solution Approach 1:
The patent performs the computationally intensive neural network training in advance, during a preliminary phase using historical process data. Once trained, the model can rapidly predict optimal parameters during actual manufacturing operations. This preliminary action separates the heavy computation from real-time operations, maintaining high manufacturing precision while minimizing time loss during production.
Solution Approach 2:
The system uses a sufficiently large historical dataset for training to achieve high prediction accuracy, rather than attempting to process all possible data in real-time. By using partial data (historical samples) for training and then applying the learned model efficiently during operations, the system achieves high manufacturing precision without excessive computation time during production.
Data Source
AI summary
A deep learning process receives desired process values associated with the one or more process stations. The deep learning processor receives desired target values for one or more key performance indicators of the manufacturing process. The deep learning processor simulates the manufacturing process to generate expected process values and expected target values for the one or more key performance indicators to optimize the one or more key performance indicators. The simulating includes generating a proposed state change of at least one processing parameter of the initial set of processing parameters. The deep learning processor determines that expected process values and the expected target values are within an acceptable limit of the desired process values and the desired target values. Based on the determining, the deep learning processes causes a change to the initial set of processing parameters based on the proposed state change.