Manufacturing Data Analysis Device for Anomaly Cause Identification
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Solution Overview
Problem
In the manufacturing industry, it is challenging to efficiently analyze and identify the causes of anomalies in products due to the vast amount of manufacturing data generated, particularly with the increasing use of IoT technology, which makes it difficult for users to manually monitor and determine the influence of manufacturing conditions on product quality.
Innovation Solution
A manufacturing data analysis device that acquires and analyzes manufacturing condition data and quality data to calculate the degrees of influence exerted by specific manufacturing conditions on product quality, generating output data that highlights significant influences and assists users in distinguishing between different manufacturing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of manufacturing data pieces is increased through IoT technology, then data completeness and monitoring coverage are improved, but manual monitoring difficulty and device complexity increase significantly
Solution Approach 1:
The system automatically analyzes manufacturing data to identify anomalies and their causes without requiring manual intervention. The anomaly analysis device autonomously processes quality data and manufacturing condition data, calculates influence degrees, and generates notifications, enabling the system to serve itself rather than requiring continuous human monitoring.
Solution Approach 2:
The patent replaces manual monitoring operations with an automated computational system. Instead of humans manually reviewing manufacturing data, the system uses algorithms to analyze data patterns, identify anomalies, and determine causal relationships between manufacturing conditions and product quality.
2Measurement precision
If all quality data is monitored to ensure comprehensive quality control, then measurement precision is improved, but analysis time and processing complexity increase
Solution Approach 1:
The system extracts and focuses analysis on only the critical portions of quality data that show anomalies. Instead of uniformly analyzing all quality data, the system identifies abnormal patterns and extracts only the relevant manufacturing condition data associated with those anomalies for detailed investigation, significantly reducing analysis time while maintaining precision.
Solution Approach 2:
The patent segments the quality data into normal and anomalous portions, and further segments the manufacturing condition data into relevant and irrelevant portions relative to the anomalies. This segmentation allows the system to concentrate computational resources only on the segments that matter for anomaly investigation.
3Measurement precision
If multiple manufacturing conditions are analyzed to distinguish causes, then measurement precision is improved, but device complexity and processing requirements increase
Solution Approach 1:
The system changes the parameter of analysis focus from examining all manufacturing conditions simultaneously to examining conditions in terms of their influence degree on quality data. By transforming the analysis into a measure of influence magnitude, the system can handle multiple conditions systematically without proportionally increasing processing complexity.
Data Source
AI summary
According to one embodiment, a manufacturing data analysis device includes processing circuitry. The processing circuitry acquires manufacturing data including a manufacturing condition data group and a quality data group. The processing circuitry calculates one or more degrees of influence exerted by first manufacturing condition data included in the manufacturing condition data group on respective pieces of quality data included in the quality data group by analyzing the manufacturing data. The processing circuitry, in a case where one or more degrees of influence satisfy a determination condition, generates output data related to at least one of the first manufacturing condition data, one or more pieces of quality data on which the first manufacturing condition data has exerted the degrees of influence satisfying the determination condition, or the degrees of influence satisfying the determination condition.