Machine Tool Feed Axis Diagnosis Without Extra Sensors
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Solution Overview
Problem
Existing feed axis diagnostic methods for machine tools require numerous sensors, increasing costs and potential failures, and are disrupted by workpiece-related disturbances, making them inefficient for detecting abnormalities in rolling guide mechanisms.
Innovation Solution
A feed axis diagnostic device and method that detect abnormalities in the rolling guide mechanism by calculating relationships between feed speeds and loads under different conditions using a control signal, without adding additional sensors, and considering weight changes of the workpiece, through linear approximation of acquired data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are mounted on moving blocks to detect end plate deformation, then abnormality detection capability is improved, but device complexity and manufacturing cost increase due to numerous sensors and wiring
Solution Approach 1:
The patent replaces the mechanical sensor-based detection system with a signal processing system that analyzes control signals already present in the feed axis control. Instead of mounting physical sensors on moving blocks to detect end plate deformation, the system processes existing control signals to extract diagnostic information about rolling guide mechanism abnormalities, thereby eliminating the need for additional sensors and wiring.
Solution Approach 2:
The system utilizes the feed axis control system's own control signals for self-diagnosis. By analyzing the control signals that already exist in the system, the rolling guide mechanism can monitor its own condition without requiring external diagnostic equipment, enabling the system to serve its own diagnostic needs.
2Measurement precision
If multiple sensors are mounted on moving blocks, then diagnostic accuracy is improved, but reliability decreases due to increased sensor failures and disturbance from workpiece weight changes
Solution Approach 1:
The patent replaces the vulnerable sensor-based measurement system with a reliable signal processing approach. By processing control signals electronically rather than using physical sensors on moving blocks, the system eliminates sensor failure risks and reduces sensitivity to disturbances from workpiece weight changes, thereby improving reliability while maintaining diagnostic accuracy.
3Measurement precision
If sensors are added for diagnosis, then abnormality detection is improved, but cost increases due to sensor purchases and wiring manufacturing
Solution Approach 1:
The patent replaces the expensive sensor hardware solution with a software-based signal processing approach. By utilizing existing control signals and processing them through algorithms, the system eliminates the need to purchase and install additional sensors and wiring, thereby significantly reducing manufacturing costs while maintaining abnormality detection capability.
4Measurement precision
If special diagnostic operation is implemented, then measurement accuracy is improved, but productivity decreases due to separate operation from usual machining
Solution Approach 1:
The patent merges the diagnostic function with the usual feed axis control operation. By analyzing control signals during normal machining operations, the system performs diagnosis concurrently with production activities, eliminating the need for separate diagnostic operations and thereby maintaining high machining efficiency while achieving accurate abnormality detection.
Data Source
AI summary
A feed axis diagnostic device for a machine tool is provided. The machine tool machines a workpiece while driving a moving body with feed axes along a guide surface of a rolling guide mechanism. The feed axis diagnostic device includes a feed axis diagnostic unit configured to detect an abnormality in the rolling guide mechanism in the machine tool. The feed axis diagnostic unit is configured to acquire a feed speed during non-machining and a load applied to the feed axes at the feed speed. The abnormality is detected based on an approximate function calculated from relationships between a plurality of the feed speeds and a plurality of the loads acquired under a plurality of different feed speed conditions in a predetermined period.