Machine Tool Defect Prediction Using Multi-Sensor Machining Data
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Solution Overview
Problem
Current machining defect prediction systems face challenges in identifying and predicting defects efficiently and accurately, particularly when threshold exceedances are unexpected, requiring significant knowledge and expertise, and the number of skilled personnel is limited.
Innovation Solution
A defect occurrence prediction system for machine tools that accumulates and processes various types of data, including vibration, sound, spindle motor, servomotor, image, and environmental data, using statistical processing, machine learning, and threshold determination to predict defects before they occur, thereby identifying causal factors and preventing defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional threshold-based monitoring is used to detect defects, then defect detection is possible when thresholds are exceeded, but it is difficult to handle defects when thresholds are unexpectedly exceeded and requires considerable knowledge and experience
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between raw measurement data and defect identification. The model accumulates historical measurement data and defect information, then automatically analyzes real-time data to identify defects and their causes, eliminating the need for operators to have extensive knowledge and experience in defect identification
Solution Approach 2:
The patent replaces the manual expert judgment system with an automated machine learning-based analysis system. Instead of relying on human experts to interpret measurement data and identify defects, the system uses algorithms to automatically detect patterns, predict defects, and identify causal factors, thereby improving ease of operation
2Measurement precision
If multiple types of measurement data are collected and analyzed using machine learning and statistical processing, then defect prediction precision is improved, but system complexity increases
Solution Approach 1:
The patent creates a multi-functional integrated system that simultaneously performs data accumulation, statistical processing, machine learning analysis, real-time monitoring, and defect prediction using a single defect occurrence prediction system. This universal approach handles multiple measurement data types (vibration, sound, spindle load, etc.) through unified processing workflows, managing complexity while maintaining high prediction precision
3Reliability
If real-time measurement data of vibration, sound, and motor parameters are processed using statistical methods and machine learning, then defect occurrence can be predicted accurately, but data processing requirements and computational load increase
Solution Approach 1:
The patent performs preliminary processing of measurement data by accumulating historical data and pre-training machine learning models with past defect information before real-time prediction. This preliminary action prepares the system in advance, allowing it to make accurate predictions with reduced computational load during actual machining operations, as the heavy lifting of pattern recognition has already been done during data accumulation and model training phases
Data Source
AI summary
Provided is a defect occurrence prediction system for a machine tool that makes it possible to identify the factors causing the occurrence of defects efficiently and effectively, and predict the occurrence of the defects accurately with good precision. A defect occurrence prediction system includes an information data accumulation unit that accumulates various types of information and various types of data relating to a machining operation of the machine tool; a defective product occurrence information data extraction unit that extracts from the information data accumulation unit the various types of information and the various types of data when the defective product is produced in the machined products; and a defect occurrence prediction unit that performs a defect occurrence prediction on a basis of the various types of information and the various types of data extracted by the defective product occurrence information data extraction unit and various types of information and various types of data relating to a machining operation of the machine tool obtained in real time.