Predictive Process Control Thresholds for Factory KPI Optimization
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Solution Overview
Problem
Existing manufacturing processes face challenges in consistently meeting design specifications due to the need for constant monitoring and adjustments, which can lead to inefficiencies and waste.
Innovation Solution
A manufacturing system that includes process stations, a station control system, and a controller, utilizing a deep learning processor to simulate the manufacturing process, generate expected process values, and optimize key performance indicators by proposing state changes to processing parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If constant monitoring and adjustments are made to the manufacturing process, then product quality consistency is improved, but system complexity and operational burden increase
Solution Approach 1:
The system performs preliminary simulation and prediction of manufacturing process outcomes before actual production. The machine learning model predicts quality metrics and process parameters in advance, allowing operators to prepare appropriate adjustments beforehand, thereby reducing the need for constant reactive monitoring and adjustments during the actual manufacturing process.
Solution Approach 2:
The system creates a virtual copy or digital twin of the manufacturing process through simulation. This virtual model replicates the behavior and characteristics of the actual manufacturing system, allowing quality assessment and optimization to be performed on the copy rather than requiring continuous direct monitoring of the physical system, thus reducing operational burden while maintaining quality consistency.
2Productivity
If deep learning simulation is used to optimize manufacturing processes, then productivity and efficiency are improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs computational work in advance by training machine learning models during periods when production data is being collected. Once trained, these models can rapidly predict optimal process parameters and quality outcomes during actual production, eliminating the need for time-consuming real-time simulations and enabling fast decision-making that improves productivity without sacrificing accuracy.
3Measurement precision
If machine learning models are trained with extensive stable and unstable data, then prediction accuracy is improved, but data processing complexity and training time increase
Solution Approach 1:
The system segments the training data into stable (normal production) and unstable (abnormal production) categories, and further divides them into different time periods and production conditions. This segmentation allows the model to learn distinct patterns from different data types more efficiently, reducing overall training time while maintaining high prediction accuracy by focusing computational resources on the most relevant patterns for each segment.
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.