Camera-Based Tool Presetting for Accurate Chuck Compatibility
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Solution Overview
Problem
Existing tool presetting and measuring systems lack operator comfort, efficiency, and operational reliability due to the inability to accurately recognize and distinguish between various tool types and compatibility, leading to potential errors and damage.
Innovation Solution
Implementing a computer-implemented method using a camera and image recognition algorithm specifically configured to identify tools, tool chucks, tool cutting edges, and tool pallets, with a machine learning algorithm for enhanced object recognition, compatibility evaluation, and feedback mechanisms to assist operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual tool identification and compatibility checking is performed, then operator attention and control are required, but operator comfort decreases and working speed reduces
Solution Approach 1:
The system performs automatic tool identification, classification, and compatibility checking without requiring operator intervention. The image recognition algorithm autonomously processes tool images, determines tool types and characteristics, and checks compatibility with tool chucks, allowing the system to serve itself rather than requiring continuous operator attention.
Solution Approach 2:
The patent replaces manual operator actions with an automated image recognition and machine learning system. The camera-based visual inspection system and algorithmic processing substitute for human eyes and decision-making, enabling both improved operator comfort and maintained productivity.
2Measurement precision
If generic object recognition is used, then system complexity is reduced, but measurement precision and manufacturing precision decrease
Solution Approach 1:
The patent applies specialized image recognition algorithms tailored to specific tool types and characteristics. Different recognition models and parameters are used for different tool categories (drills, milling cutters, reamers, etc.), allowing high precision for each specific tool type while managing overall system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary classification of tools into categories before detailed identification. The machine learning model first determines broad tool types and characteristics, then applies more specific recognition parameters accordingly, reducing the computational complexity while maintaining high measurement precision.
3Productivity
If automated image recognition is implemented, then working speed increases, but reliability decreases due to potential recognition errors
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors recognition results and adjusts its processing accordingly. The machine learning model learns from recognition outcomes and can request additional images or adjust parameters when uncertainty is detected, maintaining high reliability while preserving automated speed.
Solution Approach 2:
The system performs preliminary compatibility checking and validation before final tool-chuck matching decisions. Multiple verification steps and confidence threshold checks are conducted in advance to prevent erroneous recognition from causing operational errors, cushioning against potential reliability issues.
4Reliability
If comprehensive tool and tool chuck compatibility checking is performed, then operational reliability improves, but loss of time increases
Solution Approach 1:
The patent performs compatibility checking in advance by creating and storing compatibility data between tool types and tool chucks. The system pre-processes and catalogs which tool chucks are compatible with which tool types, so that during actual operation, the checking process is rapid and does not cause time loss.
Solution Approach 2:
The system determines tool characteristics and compatibility requirements before the actual tool-chuck matching operation. By pre-analyzing tool images and storing compatibility information, the system avoids time-consuming real-time analysis during production operations.
Data Source
AI summary
A method comprises at least one tool presetting and/or tool measuring apparatus for presetting and/or measuring tools in tool chucks or comprises at least one tool clamping device for clamping or unclamping tools in or from tool chucks, wherein, in at least one capturing step, one or more objects are captured in a field of view of a camera of the tool presetting and/or tool measuring apparatus or of the tool clamping device, and wherein, in at least one object recognition step, an image recognition algorithm is applied to camera images of the camera comprising the object/objects, whereinthe object recognition step is specifically configured at least for application to objects mentioned in the following list: tool, tool chuck, tool cutting edge, mounted complete tool, tool and/or tool chuck pallet.