AI-Based Spindle Load Status Detection for Machine Tools
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Solution Overview
Problem
Current machine tool spindle systems lack an interface to objectively determine the spindle status, leading to early failure due to unknown load levels and increased error risks, relying solely on operator experience for estimation.
Innovation Solution
A device utilizing detecting means to collect sensor data, a processing unit with artificial intelligence to analyze and categorize spindle load into 'permanently allowed', 'allowed in the medium-term', 'allowed in the short-term', and 'not allowed' categories, and an output means to display the spindle status, employing ensemble decision trees and convolutional neural networks for real-time load assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operator experience is used to estimate spindle status, then no additional detecting means are required, but the determination of spindle status is not objective and reliable
Solution Approach 1:
The patent replaces the mechanical/subjective estimation method (operator experience) with an artificial intelligence-based processing unit that objectively analyzes sensor data. This substitution transforms the spindle status determination from a subjective human judgment to an objective data-driven analysis, significantly improving reliability while accepting the addition of detecting means and processing unit as necessary infrastructure.
2Measurement precision
If detecting means are added to sense sensor data, then objective spindle status determination is achieved, but device complexity increases
Solution Approach 1:
The patent integrates multiple sensor types (temperature, torque, force, rotational speed sensors) into a unified detecting means system that collectively provides comprehensive spindle status monitoring. The processing unit analyzes data from all these sensors simultaneously, making the system multi-functional in assessing various aspects of spindle health through a single integrated AI analysis platform, thereby managing complexity through consolidation rather than proliferation of separate systems.
3Reliability
If real-time sensor data analysis is performed, then continuous spindle status monitoring is achieved, but energy consumption increases
Solution Approach 1:
The patent implements continuous monitoring through the processing unit analyzing sensor data in real-time, but applies partial action by categorizing spindle load into discrete levels (permanently allowed, allowed in medium-term, allowed in short-term, not allowed) rather than processing every possible data point with equal computational intensity. This categorization approach provides continuous monitoring capability while managing energy consumption through optimized analysis depth and frequency based on the four-category framework.
Data Source
AI summary
A device for determining a spindle status of a spindle of a machine tool includes a detector for detecting sensor data of the spindle for a defined time window. A processing unit analyses the sensor data through artificial intelligence by calculating a defined feature of the sensor data for the defined time window and determining a spindle status from the sensor data. An output member outputs the determined spindle status.