Optical Tool Setting With AI Coordinate Recognition for Tool Handling
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Solution Overview
Problem
Existing tool setting and measuring systems lack operational reliability, efficiency, and cost-effectiveness due to inefficiencies in tool recognition and handling processes.
Innovation Solution
Implementing a tool setting and measuring system with an optical device and a control unit equipped with a trained machine learning algorithm for coordinate recognition, utilizing CNN techniques to enhance object detection and coordinate determination, enabling precise and efficient tool handling and storage management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional tool recognition and handling processes are used, then the system structure remains simple, but operational reliability and efficiency deteriorate
Solution Approach 1:
The patent replaces traditional mechanical or manual tool recognition and coordinate determination methods with a machine learning-based vision system. The trained machine learning algorithm processes camera images to automatically identify tools, tool chucks, and pallets, and determines their coordinates in the fixed coordinate system, eliminating the need for complex mechanical measurement devices or manual intervention while improving operational reliability
Solution Approach 2:
The system implements self-service through automated coordinate recognition and tool identification. The machine learning algorithm autonomously processes camera images to detect objects and determine their positions without requiring external measurement devices or manual input, enabling the system to self-determine tool coordinates and manage tool handling processes independently
2Productivity
If traditional tool handling processes are used, then the device complexity remains low, but productivity and operating efficiency deteriorate
Solution Approach 1:
The patent replaces traditional mechanical coordinate measurement and tool identification systems with an optical vision system combined with machine learning. The trained machine learning algorithm automatically extracts tool coordinates from camera images, eliminating the need for complex mechanical measurement devices and manual coordinate input processes, thereby significantly improving operating efficiency
Solution Approach 2:
The patent introduces a trained machine learning algorithm as an intermediary between the camera system and the control unit. This intermediary processes camera images to automatically determine tool coordinates and identify tool positions, bridging the gap between visual data and control commands without requiring direct complex interactions between the camera and control systems
3Measurement precision
If precise coordinate determination is implemented through machine learning, then tool recognition accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning algorithm with extensive tool images and coordinate data before actual operation. This pre-training enables the algorithm to rapidly and accurately determine tool coordinates from camera images during runtime without requiring complex real-time computations, thus achieving high measurement precision while minimizing processing time during actual tool handling operations
Data Source
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AI summary
The invention is based on a tool setting and/or tool measuring system (16), with an optical tool setting and/or tool measuring device (10), with at least one camera (12), which is at least provided to record camera images of a tool setting and/or tool measuring area (14) of the tool setting and/or tool measuring device (10) and/or of a tool storage, tool retrieval or tool intermediate storage area of the tool setting and/or tool measuring system (16), and with a, in particular external or internal, control and/or regulating unit (18), which is at least provided to at least temporarily store and evaluate the camera images.It is proposed that the control and/or regulating unit (18) comprises a trained machine learning algorithm which is at least intended to carry out coordinate recognition on the basis of the evaluated camera images, which comprises recognition of tools (20), tool chucks (22), complete tools and/or tool and/or tool chuck pallets (24) and determination of their coordinates in a fixed coordinate system.