Numerical Controller for Automatic Spindle Load Target Extraction
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Solution Overview
Problem
Conventional numerical controllers require significant time and labor for determining target values for spindle load during machining, relying on experienced engineers to set these values based on graphical analysis of spindle load variations, which is inefficient.
Innovation Solution
A numerical controller with a data acquisition unit, feature amount calculation unit, and feature amount output unit that acquires and processes time-series data from sensors to extract feature amounts representative of stable machining conditions, allowing for automatic determination of target values for spindle load control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experienced engineers manually determine target values by analyzing spindle load graphs, then the accuracy of target value determination is improved, but the time and labor required increase significantly
Solution Approach 1:
The numerical controller automatically extracts feature amounts from spindle load data without requiring manual intervention by engineers. The system performs self-service by autonomously analyzing machining data, identifying stable machining periods, and determining target values, thereby eliminating the time-consuming manual graph analysis while maintaining determination accuracy
Solution Approach 2:
The patent replaces the mechanical/manual process of graph analysis with an automated computational system. The feature amount extraction unit uses algorithms to process spindle load data, substitute human engineering judgment with automated pattern recognition, and derive target values through computational analysis rather than manual inspection
2Measurement precision
If conventional manual methods are used to determine target values, then expertise-based accuracy is achieved, but the complexity of the process increases due to requiring skilled operators
Solution Approach 1:
The system encapsulates expert knowledge within the automated feature amount extraction algorithm, allowing the numerical controller to perform previously expert-dependent tasks independently. This reduces process complexity by eliminating the need for skilled operators while maintaining determination accuracy through embedded intelligent algorithms
3Productivity
If automated feature amount extraction is implemented, then productivity is improved by reducing manual work, but the measurement precision of target values may deteriorate without expert judgment
Solution Approach 1:
The system extracts feature amounts from actual spindle load data generated during machining operations, using feedback from real process data to determine target values. This data-driven approach maintains measurement precision by base determinations on actual machining characteristics rather than theoretical models, while simultaneously improving productivity through automation
Solution Approach 2:
The patent replaces expert mechanical judgment with automated computational analysis that processes spindle load data through algorithms. This substitution maintains precision by using objective data analysis rather than subjective human interpretation, while achieving improved productivity through rapid automated processing
Data Source
AI summary
A numerical controller outputs a feature amount of time-series data generated in relation to machining. The numerical controller is provided with a data acquisition unit configured to acquire the time-series data from a sensor, a feature amount calculation unit configured to sort the time-series data by their values, extract a set of the time-series data with variations of the values not higher than a predetermined threshold, and calculate the feature amount representative of the set, and a feature amount output unit configured to output the feature amount.