Wear and chipping of cutting tools used for machining can lead to deterioration of machining accuracy and injury to the user when tools break. Since visual inspection is used to determine the degree of wear of cutting tools, there are many cases where inspectors are affected by work stress or small wear is missed.
In addition, in order to reduce inspection time, some tools that are still usable are sent for regrinding, resulting in unnecessary costs. With this product, the entire process from transportation and inspection to classification can be done automatically, reducing inspector labor costs and improving inspection accuracy, thereby reducing unnecessary regrinding costs. In addition, by reading the ID of each tool, it is possible to manage the inspection results for each tool and automatically place orders for regrinding.
Benefits from installation
By linking each cutting tool to photographic data, data for each tool can be automatically managed.
The excess costs of regrinding due to human judgment can be reduced.
Automated inspection reduces the labor costs associated with visual inspection.
Inspection
The cutting tool is photographed from the front and side, and wear on the blade edge is detected by AI. The threshold separating good and bad products is determined based on criteria set by the user. In addition, the system automatically focuses on and magnifies the blade edge with a high level of detail, making it easy for users to see the condition of the cutting tool.
Transportation and classification
The robot arm creates and equips a hand suitable for each tool, reads the tool ID, places it in the inspection enclosure, and classifies it automatically. Since fifty cutting tools of various types can be inspected at a time, the enclosures for placing and for classifying tools are created according to the requirements.
Recording to database
Since IDs are read, inspection results can be saved and managed by ID. Inspection results and images are saved to the hard disk, and tool status can be managed in CSV file format. Saving data on the degree of wear of tools can lead to improvements in tool usage.
