Electronic device and tool detecting method
AssigneeHon Hai Precision Industry Co., Ltd.
InventorYUEH CHANG
Life of this patent
Estimating the term…
Abstract
A method for detecting defects in working CNC tools in real time, implemented in an electronic device, includes acquiring sounds of operation of a tool during a cutting or other operation process and dividing the acquired cutting sounds into a plurality of recordings of audio according to a preset time interval. Time-frequency features of the plurality of recordings of audio are acquired according to multiple feature transformation methods and a fusion feature image of the cutting sound is formed according to the extracted time-frequency features. A tool detection model is generated by training the fusion feature image, and any defects of the tool and any defect types the tool has are detected according to the tool detection model.
Claims18
An electronic device comprising:
- at least one processor;
- and
- a storage device coupled to the at least one processor and storing instructions for execution by the at least one processor to cause the at least one processor to:
- acquire a cutting sound of a tool during a cutting process;
- divide the acquired cutting sound into a plurality of recordings of audio according to a preset time interval;
- extract time-frequency features of the plurality of recordings of audio respectively according to multiple feature transformation methods;
- form a fusion feature image of the cutting sound according to the extracted time-frequency features;
- generate a tool detection model by training the fusion feature image;
- and
- detect a state of an operating tool according to the tool detection model.
A tool detecting method implemented in an electronic device comprising:
- acquiring a cutting sound of a tool during a cutting process;
- dividing the acquired cutting sound into a plurality of recordings of audio according to a preset time interval;
- extracting time-frequency features of the plurality of recordings of audio respectively according to multiple feature transformation methods;
- forming a fusion feature image of the cutting sound according to the extracted time-frequency features;
- generating a tool detection model by training the fusion feature image;
- and
- detecting a state of an operating tool according to the tool detection model.
Dependencies are inferred from the claim text.