Cold Rolling Mill Condition Setting for Sheet Shape Stability
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Solution Overview
Problem
Existing methods struggle to maintain stability and productivity during cold rolling of difficult-to-roll materials with high loads and small pre-rolling sheet thickness, often leading to shape defects and breakage due to fluctuations in sheet crown and bending.
Innovation Solution
A cold rolling mill rolling condition setting method using a prediction model that transforms three-dimensional steel sheet information into multi-dimensional data to estimate steering roll and press position variables, enabling precise control through a neural network model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic control is used for cold rolling, then productivity is improved, but stability deteriorates when bending sharply fluctuates in the longitudinal direction
Solution Approach 1:
The system introduces feedback by detecting the actual shape of the rolled material and comparing it with the target shape. The shape control actuator is adjusted based on the difference between actual and target shapes, creating a closed-loop control system that maintains stability while enabling automatic operation. This resolves the contradiction by allowing automatic control to maintain productivity while using feedback to ensure stability despite fluctuations in bending.
Solution Approach 2:
The invention replaces manual operator judgment and adjustment with an automated shape detection and control system. The detection device measures the actual shape, and the control system automatically adjusts the shape control actuator, substituting the mechanical decision-making process with an automated sensing and actuation system. This enables consistent automatic control that maintains both productivity and stability.
2Reliability
If operator experience is used to set rolling conditions, then stability is improved, but productivity varies depending on operator subjectivity
Solution Approach 1:
The system enables self-service by allowing the cold rolling mill to automatically detect its own performance status and adjust its control parameters without external operator intervention. The shape detection device monitors the rolled material's shape, and the control system automatically modifies operating conditions to maintain optimal performance. This eliminates variability from operator subjectivity while maintaining the stability that experienced operators previously provided.
Solution Approach 2:
The invention substitutes the human operator's experiential knowledge with an automated detection and control system. The shape detection device and control algorithm replace the operator's subjective judgment, providing consistent, objective control decisions that eliminate variability while maintaining the stability that experienced operators previously achieved through experience.
3Manufacturing precision
If shape control actuator is adjusted to correct shape defects, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The shape control actuator serves multiple functions: it corrects shape defects, maintains sheet crown within specifications, and adapts to varying material properties. By designing the actuator system to perform multiple control tasks simultaneously, the invention achieves high manufacturing precision without proportionally increasing device complexity. The same actuator mechanism handles various control objectives through coordinated adjustment.
Solution Approach 2:
The system achieves improved manufacturing precision by changing control parameters (actuator position, rolling speed, tension) rather than adding complex hardware. The control system dynamically adjusts operational parameters to correct shape defects, utilizing software-based control strategies that improve precision without proportionally increasing physical device complexity.
Data Source
AI summary
A cold rolling mill rolling condition setting method using a prediction model generated with an explanatory variable being first multi-dimensional data obtained by transforming past rolling performance data including three-dimensional steel sheet information including information regarding a portion outside a sheet edge of the roll target material on an entry side of the cold rolling mill into multi-dimensional data, and an objective variable being a controlled variable of a steering roll and a press position of the cold rolling mill, the method includes estimating at least one of the controlled variable of the steering roll and the press position of the cold rolling mill, the estimation performs by inputting second multi-dimensional data to the prediction model, the second multi-dimensional data generates from the three-dimensional steel sheet information including the information regarding the portion outside the roll target material sheet edge on the entry side of the cold rolling mill.