Screwing and Drilling Quality Monitoring With Unsupervised Clustering
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Solution Overview
Problem
Current supervised machine learning methods for controlling the quality of industrial operations, such as screwing and drilling, require extensive labeling of operation results, are time-consuming, and struggle with detecting new or unknown types of results, especially when most initial results are conforming, leading to inefficiencies and potential defects in quality control.
Innovation Solution
An unsupervised machine learning approach using statistical models like Gaussian mixture models or k-means to generate and update multivariate normal laws for clustering operation results, allowing for automatic allocation and alerting of non-conforming results, with a focus on reducing the need for extensive initial labeling and enabling detection of new result types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised machine learning methods are used for quality control, then the model can classify operation results into predefined categories, but extensive manual labeling of training data is required which is time-consuming and labor-intensive
Solution Approach 1:
The patent inverts the traditional supervised learning approach by using unsupervised learning to automatically discover patterns and clusters in operation results without manual labeling. Instead of having operators label data first, the system lets the data speak for itself through statistical modeling, then uses these automatic clusters for quality control classification.
Solution Approach 2:
The system performs self-service by automatically generating statistical models and clustering operation results without requiring external human labeling. The unsupervised learning algorithm independently identifies patterns and categorizes results, eliminating the need for manual intervention in the training phase.
2Adaptability or versatility
If supervised machine learning models are trained on initial operation results, then the model learns from available data, but it struggles to detect new or unknown types of results that were not present in the training set
Solution Approach 1:
The patent implements a dynamic quality control system where statistical models are continuously updated with new operation results. Unlike static supervised models fixed during training, this system adapts over time by incorporating new data, allowing it to detect emerging patterns and new result types while maintaining reliability through ongoing statistical validation.
Solution Approach 2:
The system changes parameters by using statistical distributions that can naturally accommodate new result types. Instead of fixed class labels, the system uses probabilistic models that can identify new clusters as they emerge, allowing parameter adaptation without retraining on labeled data.
3Quantity of substance
If most initial operation results are conforming, then the training data is abundant, but the model lacks sufficient non-conforming examples to learn effective quality discrimination
Solution Approach 1:
The patent extracts quality information directly from the statistical properties of the data itself rather than relying on labeled examples. By using unsupervised clustering and statistical modeling, the system can identify non-conforming results as deviations from the learned distribution, even when non-conforming examples are rare or absent in the initial data set.
4Ease of operation
If unsupervised machine learning is used, then the learning process is simplified and requires minimal labeling, but the model may generate false positives or misclassify results without supervised guidance
Solution Approach 1:
The system implements feedback mechanisms where classification results are continuously monitored and used to refine the statistical models. By incorporating feedback loops that analyze the distribution of classified results and adjust parameters accordingly, the system maintains high reliability while preserving the simplicity of unsupervised learning.
Data Source
AI summary
A method for controlling quality of screwing or drilling operations performed by means of a tool. The method includes machine learning of a statistical model of the unsupervised type.