ML Process Control for Multi-Station Manufacturing Adjustment
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Solution Overview
Problem
Existing manufacturing systems face challenges in optimizing process parameters across multiple process stations due to batch fluctuations and tool wear, requiring frequent manual adjustments by experienced users, which can be inefficient and prone to errors.
Innovation Solution
A control device with two machine learning algorithms, one for initial optimization and another for continuous adjustment, connected through a combination module to form a global model, enabling decentralized or centralized control of manufacturing plants by refining process parameters and features, using machine learning to adapt to changes in material and tool conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustments are performed by experienced users to optimize process parameters, then manufacturing quality can be maintained, but productivity decreases and the process becomes prone to human error
Solution Approach 1:
The control device automatically monitors process parameters and performs self-adjustments based on machine learning algorithms, eliminating the need for manual intervention by experienced users. The system serves itself by detecting process characteristics and autonomously optimizing parameters to maintain manufacturing quality while improving productivity.
Solution Approach 2:
The patent replaces manual mechanical adjustment operations with an automated control system using machine learning algorithms. The control device substitutes human operators by processing data from detection devices and automatically adjusting process parameters, thereby eliminating human error and increasing production efficiency.
2Manufacturing precision
If manual adjustments are performed frequently to account for batch variations and tool wear, then process optimization is achieved, but loss of time increases
Solution Approach 1:
The control device continuously monitors process characteristics and continuously adjusts process parameters without interruption to production. The machine learning algorithm operates in real-time, eliminating the need for frequent manual stoppages and adjustments, thereby maintaining process optimization while minimizing time loss.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where detection devices continuously monitor process characteristics, feed this information to the control device, which then automatically adjusts process parameters. This continuous feedback loop ensures process optimization is maintained without requiring manual intervention or causing production delays.
3Productivity
If automated control systems are implemented to reduce manual intervention, then productivity increases, but device complexity increases
Solution Approach 1:
The control device is designed as a multi-functional system that combines detection capabilities, machine learning processing, and actuation functions in a single integrated unit. This universal device performs multiple tasks (monitoring, analyzing, adjusting) that would otherwise require separate systems, thereby increasing productivity while managing overall system complexity.
4Adaptability or versatility
If machine learning algorithms are used for continuous optimization, then adaptability to material and tool changes improves, but device complexity increases
Solution Approach 1:
The machine learning algorithm is pre-trained with knowledge about process parameters, material variations, and tool wear patterns before deployment. This preliminary training enables the system to quickly adapt to new conditions without requiring complex real-time calculations, thereby improving adaptability while managing computational complexity.
Data Source
Figure 1
AI summary
The invention relates to a control device for controlling a manufacturing plant (1), the manufacturing plant (1) comprising at least one process station (2a, b, c) for carrying out a manufacturing process, the manufacturing plant (1) and/or the process station (2a, b, c) having at least one process parameter (6) for open-loop and/or closed-loop control, the manufacturing plant (1) and/or the process station (2a, b, c) having at least one detection device for detecting at least one process feature (12). The control device has a first control module (7) and a second control module (11), the first control module (7) and the second control module (11) being designed to define a controlled value and/or a model for the process parameter (6) on the basis of a machine learning algorithm and the process feature (12) for closed-loop control of the manufacturing plant (1) and/or the process station (2a, b, c).