CNC Parameter Optimization Using Correlation and Influence Coefficients
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Solution Overview
Problem
Current CNC machining systems require manual input of parameters, which is inefficient and does not guarantee optimal settings, especially when external environments or processing requirements change.
Innovation Solution
A parameter optimization method that establishes a one-to-one functional relationship between each parameter and performance index, determines current correlation coefficients, and calculates influence coefficients to identify important optimization parameters and adjustment parameters, thereby optimizing CNC machine tool parameters automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual input of parameters is used in CNC machining, then parameter optimization can be performed, but the optimization efficiency is low and the workload is large
Solution Approach 1:
The system automatically determines correlation coefficients, influence coefficients, and identifies optimization parameters without human intervention. The computer automatically performs calculations based on functional relationships between parameters and performance indexes, eliminating the need for manual parameter optimization work while maintaining high precision.
Solution Approach 2:
The patent replaces manual mechanical calculation and adjustment processes with automated computer-based calculations. The system uses algorithms to compute correlation coefficients and influence coefficients, substituting human manual operations with automated computational processes to improve efficiency while maintaining accuracy.
2Manufacturing precision
If manual calculation and adjustment of parameters is performed, then parameter optimization can be achieved, but the workload is large and efficiency is low
Solution Approach 1:
The computer system automatically performs all calculation and adjustment operations without human intervention. It autonomously determines correlation coefficients, calculates influence coefficients, identifies important optimization parameters, and executes parameter adjustments, completely eliminating manual workload and time consumption.
Solution Approach 2:
The system pre-establishes functional relationships between parameters and performance indexes before optimization is needed. These pre-established models enable rapid automatic calculations when optimization is required, reducing the time and effort needed for manual recalculation and adjustment.
3Productivity
If all parameters are optimized simultaneously based on comprehensive performance, then optimization efficiency is improved, but the complexity of determining parameter relationships increases
Solution Approach 1:
The patent segments the complex optimization problem into distinct computational steps: establishing functional relationships, determining correlation coefficients, calculating influence coefficients, identifying important parameters, and performing optimization. This segmentation manages complexity by breaking down the overall process into manageable, automated stages.
Solution Approach 2:
The system transforms the complex parameter optimization problem into a series of parameter changes including correlation coefficients, influence coefficients, and parameter weights. By changing the representation and organization of parameters, the system manages complexity while enabling simultaneous optimization of all parameters through automated calculations.
Data Source
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AI summary
A parameter optimization method, device and computer readable storage medium. The method includes: establishing a one-to-one functional relationship between each parameter and each performance index; determining each current correlation coefficient between the parameter and each performance index based on the one-to-one function relationships; obtaining a current weight of each performance index; according to current weights and current correlation coefficients, obtaining a current influence coefficient of each parameter on a comprehensive performance of the performance indexes; and determining important optimization parameters according to the current influence coefficient; for each two parameters, calculating a current correlation coefficient of the two parameters, and determining an adjustment parameter; and performing parameter optimization based on the important optimization parameters and the adjustment parameters. The method can improve the parameter optimization efficiency.