Generalized Response Model for Sheet Forming Machine Control
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Solution Overview
Problem
Conventional methods for controlling sheet-forming machines with multiple CD actuators are inefficient and prone to errors due to the need for massive discrete points to represent response models, making it cumbersome to implement effective control over sheet properties across the machine width.
Innovation Solution
A computer-implemented method creates a generalized response model using critical points and continuous functions, allowing for efficient representation of sheet property responses with a few critical points and continuous functions, rather than numerous discrete points, to effectively control sheet properties across the machine width.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional discrete point methods are used to represent response models for multiple CD actuators, then measurement precision is maintained, but device complexity and ease of operation deteriorate due to the massive number of points required
Solution Approach 1:
The patent segments the continuous response profile into aĉé number of critical points that capture the essential characteristics. Instead of using hundreds or thousands of discrete points, the method identifies key inflection points, peaks, and valleys that define the response shape, thereby reducing complexity while maintaining measurement precision.
Solution Approach 2:
The patent transforms the representation from a large number of discrete amplitude values to a smaller set of parameterized critical points with associated weights and positions. This parameter change reduces the dimensional complexity from hundreds/thousands of points to just a few critical characteristics that can efficiently represent the response model.
2Measurement precision
If conventional discrete point methods are used to represent response models, then measurement precision is maintained, but ease of operation deteriorates due to cumbersome control implementation
Solution Approach 1:
By segmenting the response model into critical points rather than continuous discrete points, the patent makes the control system more operable. The segmented representation allows operators to work with a manageable number of key parameters instead of numerous discrete values, significantly improving ease of operation while preserving the essential measurement precision.
Solution Approach 2:
Instead of starting with many discrete points and reducing them, the patent inverts the approach by starting with critical characteristic points and building the response model from there. This inversion makes the system more operable by naturally focusing on the most important control parameters first.
3Measurement precision
If massive discrete points are used to represent response models for multiple CD actuators, then response accuracy is improved, but productivity deteriorates due to inefficient processing
Solution Approach 1:
The patent segments the response representation into critical points, dramatically reducing the data volume from hundreds/thousands of discrete points to just a few key characteristics. This segmentation enables faster processing and higher productivity while maintaining the accuracy needed for effective control of multiple CD actuators.
Solution Approach 2:
By changing the parameter representation from numerous discrete amplitude values to a compact set of critical point parameters (positions, weights, shapes), the patent achieves both accuracy and efficiency. This parameter transformation enables rapid processing suitable for real-time or near-real-time control applications.
Data Source
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AI summary
A method and apparatus for creating a generalized response model for a sheet forming machine are provided. Sheet property profiles are measured while the setpoint of an actuator is changed. A response (or change) profile of the sheet property resulting from a setpoint change is calculated. A finite set of critical points are selected from the property response profile. Using the selected critical points, the property response profile is classified in one of a finite number of response types. Under each of the response types, the property response profile is fitted with a plurality of continuous functions associated therewith. These continuous functions are combined to form the response model that minimizes the deviation between the property response and the fitted combination of continuous functions.