Numerical Controller Tool Life Prediction via Machine Learning
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Solution Overview
Problem
Current methods for determining tool life in machining are inaccurate, leading to unnecessary tool replacement, inability to detect cracks in multi-bladed tools, and unsuitability for unattended operations, as they rely on operator expertise and do not account for tool material, abrasion position, and state.
Innovation Solution
A numerical controller that learns the relationship between machining conditions and results through supervised learning, using a machine learning device to construct a model predicting machining outcomes, allowing for improved tool life determination and unattended operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tool life is determined based on number of times or time of use, then tool exchange is performed conservatively to avoid machining defects, but the tool is often still usable and cost is wasted
Solution Approach 1:
The patent replaces manual inspection methods with an automated image processing system that captures images of the tool tip and analyzes them computationally. This substitution enables objective, consistent detection of tool wear and damage without relying on operator judgment or conservative time-based replacement schedules.
Solution Approach 2:
The system creates a visual copy (image) of the tool tip condition and analyzes this copy rather than requiring direct physical inspection. Multiple images are captured from different angles and processed to determine tool status, allowing accurate assessment without physical contact or disassembly.
2Ease of operation
If tool life is determined by measuring tool length, then it is simple to implement, but it is impossible to detect cracks of chip in multi-bladed tools or tools with blades at end positions
Solution Approach 1:
The system segments the tool inspection task into multiple image captures from different angular positions. By dividing the full 360-degree inspection into discrete angular segments (e.g., every 30 or 45 degrees), the system comprehensively examines all blade surfaces including those at end positions or in multi-bladed configurations that would be invisible from a single viewpoint.
Solution Approach 2:
The system transitions from one-dimensional length measurement to two-dimensional image analysis by capturing visual information from multiple angular dimensions. This dimensional expansion enables detection of crack patterns, chipping, and wear on blade surfaces that cannot be detected by linear measurement alone.
3Measurement precision
If tool life is determined by operator visual check, then it can detect various abrasion states, but an operator with technical knowledge is required and it is not suitable for unattended operation
Solution Approach 1:
The system performs self-inspection by automatically capturing images of the tool tip, processing them through image analysis algorithms, and determining tool wear status without human intervention. The numerical controller itself executes the inspection function, enabling unattended operation and continuous monitoring of tool condition.
Solution Approach 2:
The system establishes a feedback loop where image data from the tool tip is continuously analyzed and fed back to the numerical controller. This feedback mechanism automatically triggers alerts or stops machining when wear thresholds are exceeded, eliminating the need for periodic manual inspection while maintaining precise detection capability.
4Reliability
If tool life is determined by checking spindle load, then it can detect breakage or chipping, but slight chipping hardly causes change of spindle load and accurate detection is difficult
Solution Approach 1:
The system performs preliminary visual inspection of the tool tip condition before machining operations proceed. By proactively capturing and analyzing images of the tool tip, the system detects wear, chipping, and damage at early stages when they are still visible but have not yet significantly impacted machining parameters or spindle load.
Solution Approach 2:
The patent replaces indirect mechanical detection methods (spindle load monitoring) with direct visual inspection through image capture and analysis. This substitution enables detection of surface-level damage such as slight chipping and edge wear that do not yet manifest as significant changes in mechanical loading or vibration patterns.
Data Source
AI summary
A numerical controller which controls a machine tool acquires tool information including a shape of a tool, a machining condition in machining, and information related to a machining result of a workpiece after machining. A machine learning device performs machine learning on tendency of the information related to a machining result with respect to the tool information and the machining condition based on the tool information and the machining condition used as input data and based on the information related to a machining result used as teacher data, so as to construct a learning model. The machine learning device determines whether or not a machining result is good by using the learning model based on the tool information and the machining condition before the machine tool machines a workpiece.