Facility Control Parameter Tuning Using Model-to-Machine Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge lies in efficiently optimizing control parameters for complex production facilities with numerous parameters, as existing methods are time-consuming and may not robustly handle environmental disturbances due to modeling and evaluation errors.
Innovation Solution
An automatic parameter adjustment system that uses multiple facility models to simulate operations, compares results with actual machine operations, and iteratively adjusts control parameters based on performance evaluations to optimize settings efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If simulation-based parameter optimization is performed sequentially for numerous control parameters, then parameter optimization can be achieved, but the time required becomes extremely long
Solution Approach 1:
The patent divides the parameter optimization process into two segments: simulation-based optimization for initial parameter candidates, and actual machine-based optimization for final refinement. This segmentation allows the system to leverage the speed of simulation while ensuring accuracy on the actual machine, reducing total optimization time.
Solution Approach 2:
The patent performs preliminary parameter optimization through simulation before actual machine operation. By pre-optimizing parameters in the virtual environment, the system narrows down the search space for actual machine trials, significantly reducing the time required for on-machine parameter tuning.
2Ease of operation
If the operation range is limited to a narrower range than simulation candidates, then actual machine operation is constrained, but robustness to environmental disturbances deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where actual machine operation results are compared with simulation results, and the facility model is updated accordingly. This feedback loop allows the system to learn from real-world performance and adjust the model to better reflect actual conditions, improving robustness while maintaining ease of operation.
Solution Approach 2:
The patent dynamically adjusts the operation range parameters based on the comparison between simulation and actual machine results. By modifying the search range and evaluation criteria according to real-world performance, the system maintains both ease of operation and robustness to environmental variations.
3Adaptability or versatility
If multiple control parameters are adjusted empirically by users, then customization is possible, but the complexity of adjustment steps increases significantly
Solution Approach 1:
The patent enables the system to perform parameter optimization automatically without requiring extensive user intervention. The facility model and optimization algorithm work together to self-adjust parameters based on performance criteria, reducing the complexity of the adjustment process while maintaining adaptability.
Solution Approach 2:
The patent systematically manages multiple parameter changes through automated algorithms rather than manual empirical adjustment. By using structured parameter optimization methods, the system handles complexity internally while presenting a simplified interface to users, maintaining versatility without increasing perceived complexity.
Data Source
AI summary
The present disclosure relates to a technology for automatically and efficiently searching for a facility control parameter. One aspect of the present disclosure relates to an automatic parameter adjustment device comprising: a plurality of facility models that model a facility; a control parameter setter that sets a plurality of first control parameters for use in a first trial to the plurality of facility models, and set a second control parameter for use in the first trial to the facility; and a comparer that compares a model operation result of the first trial of the plurality of facility models under the plurality of first control parameters with an actual machine operation result of the first trial of the facility under the second control parameter, wherein the control parameter setter selects a control parameter for use in a second trial based on a first comparison result between the model operation result of the first trial and the actual machine operation result of the first trial.