Cutting Tool Wear Prediction Using Pretrained ML Models
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Solution Overview
Problem
Existing methods for predicting the wear amount of cutting tools rely heavily on experiential data and simple prediction formulas, lacking the accuracy and precision provided by advanced machine learning models.
Innovation Solution
A wear amount prediction apparatus utilizing a trained machine learning model that predicts the wear of cutting tools based on input data including tool information, cutting conditions, and workpiece details, generating predictions for processing time, wear amount, and defect probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction formulas based on worker experience are used, then the system is simple and easy to operate, but the prediction accuracy and reliability are insufficient
Solution Approach 1:
The patent replaces conventional mechanical/experiential prediction methods with a machine learning-based prediction model. The system collects cutting tool data, workpiece data, and cutting condition data, then uses a trained prediction model to automatically predict wear amount, initial wear time, and initial wear amount, substituting human experience-based estimation with data-driven intelligent prediction.
2Reliability
If machine learning models are introduced to improve prediction accuracy, then the prediction reliability improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-collecting and organizing training data including cutting tool data, workpiece data, and cutting condition data before deploying the prediction model. The machine learning model is trained in advance with this comprehensive dataset, enabling reliable predictions during actual operation without requiring complex real-time data processing infrastructure.
3Measurement precision
If comprehensive training data including cutting tool information, cutting conditions, and workpiece specifications are collected, then the prediction precision improves, but the data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary data collection and model training by gathering comprehensive training data including cutting tool data, workpiece data, and cutting condition data before deployment. The machine learning model is pre-trained with this extensive dataset, enabling fast and accurate predictions during actual operation without requiring extensive real-time computational resources.
Data Source
AI summary
An apparatus includes: a first unit that predicts, with use of a trained model, an amount of wear of a cutting tool in accordance with a processing time; and a display that displays information which is based on a result of prediction, in which a trained model is generated by performing machine learning with training data using a dataset that includes: information which pertain to a cutting tool, a condition which concerns cutting, and information which pertain to a workpiece; and a processing time for which a cutting tool was used, the amount of wear of the cutting tool which is due to processing, an initial wear time which is from the start of cutting by the cutting tool until the completion of initial wear, and an initial wear amount which indicates the amount of wear of the cutting tool when the initial wear time has elapsed.