Tool Exchange Abnormality Detection Using Weight and Balance Data
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Solution Overview
Problem
Conventional abnormality detection systems for tool exchange devices face challenges in uniformly detecting tool exchange issues due to variations in tool weights and balances, leading to difficulties in accurately identifying abnormalities during the tool exchange process.
Innovation Solution
A machine learning-based abnormality detection apparatus that collects data on tool weights and balances, performs cluster analysis, and integrates operation data such as torque waveform and sound to determine the attachment state of tools, enabling automatic detection of abnormalities by comparing current tool states with stored data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional abnormality detection methods using torque waveform, sound, and vibration are used, then abnormality detection is performed, but detection accuracy deteriorates due to variations in tool weights and balances
Solution Approach 1:
The patent applies parameter changes by incorporating tool weight and balance data as additional parameters for abnormality detection. Instead of relying solely on torque waveform, sound, and vibration, the system integrates these physical parameters of tools to compensate for variations caused by different tool configurations. This allows the detection system to adapt to different tool weights and balances, improving both reliability and measurement precision.
2Adaptability or versatility
If the same tool exchange operation is performed with different tools, then the operation must accommodate various weights and balances, but this causes the torque waveform, sound, and vibration to vary, making uniform abnormality detection difficult
Solution Approach 1:
The system implements feedback by using the known tool weight and balance parameters to adjust and interpret the torque waveform, sound, and vibration signals. The detection system receives feedback about the specific tool being exchanged and uses this information to normalize the detection signals, enabling consistent abnormality detection across different tool configurations while maintaining adaptability.
3Productivity
If tool exchange device operates without accurate abnormality detection, then continuous operation is maintained, but damage to machine body, tools, jig, or workpiece may occur
Solution Approach 1:
The patent applies preliminary action by performing abnormality detection before damage occurs. By integrating tool weight and balance parameters with operational data, the system can identify potential issues during the tool exchange process and alert operators before they lead to machine damage, tool failure, or workpiece defects. This preventive approach maintains productivity while reducing damage risk.
Data Source
AI summary
An abnormality detection apparatus includes: a state observation section that observes tool weight data on weights of tools attached to a tool exchange device, tool balance data on balances of the tools, and tool exchange state data on a state during exchange of the tools; a tool exchange state data storage section that stores the tool weight data, the tool balance data, and the tool exchange state data in association with each other; and a determination result output section that detects an abnormality in the exchange of the tools based on the tool weight data, the tool balance data, and the tool exchange state data observed by the state observation section during the exchange of the tools in the processing machine and the data stored in the tool exchange state data storage section.