Machining Center Spindle Diagnosis With Tool Suitability Verification
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Solution Overview
Problem
Machining centers face challenges in spindle state diagnosis when using different tools, as operators may unknowingly load inappropriate tools, leading to reduced workpiece quality or spindle damage, and existing vibration data methods are tool-dependent and lack versatility.
Innovation Solution
A multi-step spindle diagnosis technology using machine learning models to verify tool suitability and diagnose spindle state, involving tool verification and spindle diagnosis models trained on sensor data during spindle idling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a turret or tool changer is used for small quantity batch production with multiple tools, then productivity and versatility are improved, but the reliability of spindle state diagnosis deteriorates due to tool mismatches and inappropriate tool loading
Solution Approach 1:
The system performs preliminary verification of tool suitability before actual machining operations. The verification unit checks whether the loaded tool matches the required tool for the current machining operation, and the diagnosis unit assesses spindle state before machining begins. This preliminary action prevents inappropriate tools from being used, thereby maintaining spindle diagnosis reliability while enabling multi-tool batch production.
Solution Approach 2:
The patent introduces an intermediary verification and diagnosis system between the tool loading process and the machining operation. This intermediary unit acts as a mediator that validates tool-spindle compatibility and assesses spindle condition, ensuring that only appropriate tools are used with the current spindle state, thus resolving the conflict between using multiple tools and maintaining diagnosis reliability.
2Measurement precision
If conventional vibration data collection methods are used for spindle diagnosis, then measurement capability is achieved, but measurement precision deteriorates when tool types change due to tool-dependent vibration patterns
Solution Approach 1:
The diagnosis system is segmented into distinct functional units: a verification unit that identifies tool type and checks suitability, and a diagnosis unit that performs spindle state assessment. This segmentation allows the system to first determine what tool is loaded, then apply the appropriate diagnosis methodology for that specific tool type, thereby maintaining measurement precision across different tool types.
Solution Approach 2:
The system dynamically changes diagnostic parameters based on the identified tool type. The verification unit detects tool characteristics, and based on this information, the diagnosis unit adjusts its measurement and analysis parameters accordingly. This parameter adaptation enables accurate spindle state measurement regardless of which tool type is currently loaded.
3Ease of operation
If operators manually load tools according to machining sequence, then ease of operation is maintained, but harmful factors increase due to undetected tool mismatches causing workpiece quality degradation and spindle damage
Solution Approach 1:
The system implements automatic feedback mechanisms that monitor tool loading and verify tool suitability before machining operations. The verification unit provides feedback on whether the loaded tool is appropriate for the current operation, and the diagnosis unit provides feedback on spindle condition. This feedback loop prevents harmful outcomes from undetected tool mismatches while requiring minimal changes to the manual tool loading process.
Data Source
AI summary
The present invention relates to verifying the suitability of a tool and diagnosing a spindle for a machining center on which different tools are mounted. Provided are a machining center spindle diagnosis apparatus and method configured to monitor a change of a tool in the machining center; when the change of the tool is recognized, control the machining center to idle the spindle; acquire sensor data from a sensor installed on the machining center during the idling of the spindle; input the acquired sensor data to a tool verifying model pre-trained by a machine learning technology to verify suitability of the tool; and when the tool is verified to be suitable, inputting the acquired sensor data to a spindle diagnosing model pre-trained by the machine learning technology to diagnose an operating state of the spindle.