CNC Learning Model Switching for Tool Abnormality Detection
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Solution Overview
Problem
Existing numerical control systems face challenges in accurately detecting tool wear or breakage due to variations in machine tool operation conditions and environmental factors, making it difficult to create a general-purpose machine learning device that can handle diverse situations.
Innovation Solution
A numerical control system that switches between multiple learning models based on specific operation and environmental conditions, using a condition designating unit, state amount detection unit, inference computing unit, abnormality detection unit, learning model generation unit, and learning model storage unit to select and generate learning models tailored to the current machining conditions, thereby improving abnormality detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general-purpose machine learning device is created to detect tool wear or breakage across various machining situations, then the detection capability is improved, but the device complexity and data requirements increase significantly
Solution Approach 1:
The patent segments the general-purpose detection task into multiple specialized learning models, each trained for specific machining conditions (e.g., different tools, workpiece materials, spindle speeds). The system selects and switches between these segmented models based on current operating conditions, avoiding the need for a single complex general-purpose model while maintaining broad detection capability.
Solution Approach 2:
The system dynamically selects and switches between different learning models based on real-time machining conditions. This dynamic adaptation allows the system to optimize detection accuracy for each specific situation without requiring a fixed complex structure, as the model configuration changes flexibly with operating parameters.
2Measurement precision
If multiple learning models are maintained for different machining conditions, then the detection accuracy is improved, but the storage requirements and model management complexity increase
Solution Approach 1:
The system performs preliminary organization of learning models by machining conditions before actual detection. Learning models are pre-categorized and stored in association with specific machining parameters, enabling efficient retrieval and switching during operation. This preliminary structuring reduces the management burden of multiple models while maintaining high detection accuracy.
Solution Approach 2:
The patent introduces an intermediary mechanism (learning model switching unit) that manages the selection and switching between multiple learning models. This intermediary layer simplifies model management by handling the complexity of model selection based on machining conditions, allowing the system to maintain multiple specialized models without proportionally increasing overall system complexity.
Data Source
AI summary
A numerical control system detects a state amount indicating a state of machining operation of a machine tool, creates a characteristic amount that characterizes the state of machining operation from the detected state amount, infers an evaluation value of the state of machining operation from the characteristic amount, and detects an abnormality in the state of machining operation on the basis of the inferred evaluation value. The numerical control system generates and updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the machining operation of the machine tool.