Consumable Component Life Prediction With Bayesian Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting the life of consumable components in manufacturing machines, such as tools and motors, are impractical due to the complexity of determining constants and the need for large amounts of data, leading to inaccurate predictions and high costs, especially when machining conditions frequently change.
Innovation Solution
A life predicting device using statistical machine learning optimizes a life probability model from limited data through Bayesian inference, selecting relevant data features and updating parameters in real-time, allowing for accurate prediction of component lifespan and reducing unnecessary inspections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the life equation of Taylor is used to estimate tool life, then a constant can be decided based on machining conditions, but the determination of the constant is complicated when machining conditions frequently change
Solution Approach 1:
The patent replaces the mechanical calculation method of Taylor's life equation with a neural network-based system. The neural network automatically learns the relationship between machining conditions and tool life from historical data, eliminating the need for manual constant determination and complex calculations when conditions change.
Solution Approach 2:
The neural network system performs self-learning and self-adjustment by automatically updating its internal parameters based on observed machining data. This allows the system to adapt to changing machining conditions without requiring manual intervention or recalculation of constants.
2Ease of operation
If life estimation is performed according to machining time and number of times of machining, then a rule of thumb can be applied, but the life cannot be predicted at high accuracy when machining conditions frequently change
Solution Approach 1:
The patent transforms the estimation approach by changing from simple counting parameters (time, number of operations) to a comprehensive set of machining condition parameters. The neural network processes multiple input parameters simultaneously, capturing the complex relationships between varying machining conditions and tool life while maintaining operational simplicity.
3Measurement precision
If cluster analysis is performed to improve life prediction accuracy, then a highly accurate estimation can be achieved, but a large amount of consolidated data are necessary
Solution Approach 1:
The neural network is pre-trained with initial data to establish baseline knowledge about tool life relationships. This preliminary learning enables the system to provide accurate predictions even when the amount of new consolidated data is limited, as the pre-trained network can generalize from its initial training.
4Reliability
If data are collected at any time to check component state tendency, then a real operation state estimation can be performed, but a large-capacity storage is necessary
Solution Approach 1:
The system extracts and processes only the essential features and parameters from the collected machining data that are most relevant to predicting component life. By focusing on key features rather than storing and processing all raw data, the system achieves reliable predictions with reduced storage requirements.
Data Source
AI summary
A machine learning device included in a life predicting device observes, as a state variable, life related data related to a life of a consumable component, creates a probability model of a service life for replacement of the consumable component on the basis of the life related data, and predicts, using the created probability model, the service life for replacement of the consumable component based on the life related data.