Tool Life Prediction Using Per-Tool Machine Learning Models
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Solution Overview
Problem
Existing life expectancy prediction systems for tools often inaccurately determine when a tool has reached the end of its life, leading to premature replacement, as they rely on general arithmetic models that do not account for individual tool variations.
Innovation Solution
A life expectancy prediction system that uses machine learning to generate unique models for each tool based on its specific machining data, allowing for accurate prediction of remaining machining times by selecting the most suitable learned model from a plurality of models stored in a learned model storage unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If general arithmetic models are used for life expectancy prediction, then the prediction system is simple to implement, but the prediction accuracy deteriorates due to individual tool variations
Solution Approach 1:
The patent segments the prediction system by creating individual learned models for each tool based on its unique machining data, rather than using a single general model for all tools. This segmentation allows each tool to be predicted with its own characteristics, improving accuracy while maintaining system simplicity through automated model generation.
Solution Approach 2:
Each tool serves itself by generating its own learned model from its own machining data. The system automatically creates personalized prediction models without requiring manual intervention or complex configuration for each individual tool, thus improving accuracy while keeping the implementation simple.
2Measurement precision
If individual learned models are created for each tool, then the prediction accuracy is improved, but the device complexity increases due to multiple models
Solution Approach 1:
Each tool automatically generates its own learned model from its own machining data without requiring manual configuration or complex system setup. This self-service approach creates individualized accurate predictions while keeping the overall system simple through automation.
Solution Approach 2:
The system changes the parameter of model specificity from general to individual by using tool-unique machining data to generate personalized learned models. This parameter change enables high accuracy predictions while the automated process prevents excessive system complexity.
3Ease of operation
If tool replacement is based on predetermined life expectancy, then the operation is simple, but premature replacement occurs leading to increased costs
Solution Approach 1:
The system incorporates feedback by continuously monitoring actual tool machining data and comparing it with predicted life expectancy. This feedback loop enables dynamic adjustment of replacement timing based on actual tool performance, preventing premature replacement and reducing costs while maintaining simple operation through automated predictions.
Solution Approach 2:
The system performs preliminary prediction of tool life expectancy before actual failure occurs, allowing operators to plan replacement timing in advance. This preliminary action prevents premature replacement by providing accurate predictions, reducing tool costs while keeping the replacement process simple.
Data Source
AI summary
A life expectancy prediction system for a target tool includes a processing machine body, a detector to detect a state data, a learned model storage unit to store learned models generated by executing machine learning using training datasets, including an explanatory variable and an objective variable, the explanatory variable being the state data and the objective variable being a number of first remaining machining times, the learned model storage unit being to store the learned models, each for each of the tools and a remaining machining times prediction unit to select, based on the state data, one learned model and predict a number of second remaining machining times, using the one learned model and the state data.