CNC Parameter Optimization Using Correlation-Based Performance Weighting

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Solution Overview

Problem

Existing CNC machining systems require manual input of parameters, which is inefficient and does not guarantee optimal settings, especially when environmental or processing requirements change, leading to low efficiency and lack of coordination in parameter optimization.

Innovation Solution

Establish a one-to-one functional relationship between parameters and performance indices, determine current correlation coefficients, calculate influence coefficients, and optimize parameters based on important optimization and adjustment parameters using a knowledge map and correlation nephogram.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual parameter input is used in CNC machining, then parameter optimization can be performed, but the work efficiency is low and the optimization process is time-consuming

Engineering Contradiction:

Improveparameter optimization efficiencyVSAvoidtime for manual parameter input and adjustment

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system automatically determines important optimization parameters and their adjustment directions based on performance index changes, eliminating the need for manual parameter analysis. The computer executes the optimization algorithm autonomously, allowing the system to serve itself in the parameter optimization process.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical parameter adjustment with an automated computational system. The computer calculates correlation coefficients, determines important parameters, and guides adjustment directions, substituting human manual operations with automated information processing and mathematical calculations.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If all parameters are optimized simultaneously considering multiple performance indices, then comprehensive optimization is achieved, but the complexity of the optimization process increases

Engineering Contradiction:

Improvecomprehensive performance optimizationVSAvoidoptimization system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the complex multi-parameter optimization problem into manageable steps: first calculating correlation coefficients between parameters and performance indices, then determining important optimization parameters, and finally determining adjustment directions. This stepwise segmentation reduces the perceived complexity while maintaining comprehensive optimization.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transforms the complex optimization problem into parameter calculations (correlation coefficients, importance scores, adjustment directions) that can be processed computationally. By changing the representation of the optimization problem into mathematical parameters, the system manages complexity through structured data processing.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If manual parameter adjustment is performed when environmental conditions change, then adaptability is maintained, but the response time is slow and efficiency is reduced

Engineering Contradiction:

Improveadaptation to environmental changesVSAvoidresponse speed to environmental changes

Core Design Contradiction:

Adaptability or versatilityVSSpeed

Solution Approach 1:

The patent implements a feedback mechanism where performance index changes are continuously monitored, and these changes feed back into the parameter determination process. When environmental conditions change and performance indices shift, the system automatically recalculates and adjusts parameters, creating a closed-loop adaptive system that responds quickly to changes.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The optimization system is designed to be dynamic rather than static. The important parameters and adjustment directions are not fixed but are recalculated based on current performance index values. This dynamic recalculation allows the system to adapt rapidly to changing environmental conditions without manual intervention.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12487583B2Parameter optimization method, device, and storage medium

Publication Date: 2025.12.02 SIEMENS AG

AI summary

Examples of the present disclosure provide 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 technical solutions of the present disclosure can improve the parameter optimization efficiency.