Sensor-Based Quality Control for Real-Time Anomaly Detection
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Solution Overview
Problem
Current quality control methods in industrial manufacturing are inadequate, as they often rely on superficial inspections, are reactive, and lack uniformity, leading to high costs and potential damage to a manufacturer's reputation due to assumptions about defect prevalence across entire production runs based on limited sampling.
Innovation Solution
A method and system utilizing a deep neural network learning model trained with a meta-learning algorithm to detect anomalies in real-time by comparing current production data with normalized data from sensors, allowing for scalable and automated defect detection without the need for extensive data collection or expensive optical sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-depth quality control is performed on selected single items to achieve high accuracy, then measurement precision is improved, but productivity deteriorates due to the time-consuming nature of manual inspection
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated sensor-based measurement system. Sensors capture production data during manufacturing, and a learning model automatically analyzes this data to detect defects, eliminating the need for manual inspection while maintaining high measurement precision and enabling continuous monitoring of all production items
Solution Approach 2:
The system enables self-service quality control by using the production process's own sensor data to automatically detect defects. The learning model is trained on historical production data and autonomously identifies anomalies without requiring external manual inspection, allowing the system to inspect itself continuously
2Reliability
If comprehensive quality control is performed on every product to ensure high reliability, then reliability is improved, but loss of time increases due to the inability to inspect all items in mass production
Solution Approach 1:
The system implements continuous quality control by capturing sensor data throughout the entire production process without interrupting manufacturing. The learning model continuously analyzes incoming data streams in real-time, enabling uninterrupted monitoring of all production items from start to finish, thereby ensuring comprehensive reliability without time loss
3Measurement precision
If customized software solutions are developed for each production scenario to achieve high measurement precision, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent develops a universal learning model framework that can handle multiple production scenarios and defect types through a single standardized architecture. The model accepts various sensor data inputs and adapts to different production processes without requiring custom software development for each scenario, thereby maintaining high measurement precision while reducing device complexity
Data Source
AI summary
A method for quality control in industrial manufacturing for one or more production processes for producing at least one workpiece and/or product includes creating a learning model for at least one production process for the at least one workpiece and/or product. The learning model is trained and initialized using a meta-learning algorithm, and the learning model is calibrated using normalized data of the at least one production process for the at least one workpiece and/or product. Currently generated data of the at least one production process for at least one currently produced workpiece/product is forwarded to the learning model. The data is generated by sensors. The learning model compares the currently generated data with the normalized data and finds deviations. The learning model scales the deviations between the currently generated data and the normalized data, and the learning model communicates presence of an anomaly for the currently produced workpiece/product.