Process Data Defect Analysis Using Genetic Rule Search
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Solution Overview
Problem
Conventional methods for analyzing the cause of product defects in manufacturing plants rely on subjective specialist opinions, lacking an objective analysis based on collected process data, which is essential for efficient production management and reducing defect rates in smart factories.
Innovation Solution
A method and apparatus that preprocess and analyze process data using a pre-processing unit, a search unit employing genetic algorithms for solution encoding and decoding, and a post-processing unit to remove redundant conditional sentences, ultimately identifying the primary defect cause-conditional sentence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods relying on subjective specialist opinions are used to analyze product defects, then the analysis process is simple and quick, but the analysis lacks objectivity and scientific basis
Solution Approach 1:
The patent replaces the mechanical system of human expert judgment with an automated computational system using genetic algorithms. The system encodes defect analysis rules into computational models that automatically process sensor data from the manufacturing environment, eliminating subjective human judgment while maintaining systematic analysis capabilities.
Solution Approach 2:
The system performs self-service by automatically analyzing defect causes without requiring continuous human intervention. The genetic algorithm autonomously evolves and optimizes analysis rules based on historical data, and the system continuously monitors manufacturing processes to identify defects independently, reducing reliance on specialist expertise.
2Reliability
If process data is collected and analyzed using automated systems, then objective defect analysis is achieved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex defect analysis system into distinct functional modules: data collection from sensors, data preprocessing and cleaning, genetic algorithm-based rule encoding, defect pattern recognition, and result visualization. This modular segmentation reduces overall system complexity by making each component manageable and independently optimizable while maintaining reliable integrated performance.
Solution Approach 2:
The system introduces intermediary components including data preprocessing layers that clean and standardize sensor inputs before analysis, and encoding/decoding mechanisms that translate complex process data into actionable defect rules. These intermediaries buffer the complexity between raw data collection and final analysis, improving reliability while managing system complexity.
3Measurement precision
If genetic algorithms are used to search for defect cause conditional sentences, then comprehensive analysis is achieved, but the computational time and processing resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and cleaning process data before defect analysis, pre-defining relevant conditional sentence structures and encoding initial population for genetic algorithms. This preliminary preparation reduces the computational search space during actual defect analysis, improving accuracy while reducing computational time required for the core analysis task.
Data Source
AI summary
Disclosed are a method and apparatus for analyzing a cause of a product defect. The apparatus includes a pre-processing unit configured to receive process data and perform pre-processing for analyzing a cause of a product defect, a search unit configured to search for a primary defect cause-conditional sentence to represent a primary defect cause through solution encoding and decoding and solution fitness calculation for a plurality of candidate solutions in order to search for a conditional sentence using the pre-processed process data and to output the primary defect cause-conditional sentence, and a post-processing unit configured to receive the primary defect cause-conditional sentence, remove a redundant conditional sentence, and output the final defect cause-conditional sentence.