Manufacturing Data Visualization for Abnormality Cause Analysis
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Solution Overview
Problem
In manufacturing industries, the increasing volume of data from various sources makes it difficult to manually monitor and identify the cause of product abnormalities, especially as the number of manufacturing steps and apparatuses grows, necessitating an automated solution for data processing and visualization.
Innovation Solution
A data processing apparatus that generates visualization data to display estimation results of manufacturing conditions for cause candidates of a specific product status, using relationship data to group and prioritize manufacturing conditions, thereby assisting users in identifying the root cause of abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of manufacturing steps and apparatuses increases to improve manufacturing capability, then productivity is improved, but the number of data items to be monitored increases making manual monitoring difficult
Solution Approach 1:
The patent segments the complex manufacturing data into individual data items, each with unique identifiers. This allows the system to break down the overwhelming volume of data into manageable units that can be processed and analyzed systematically by the data processing apparatus.
Solution Approach 2:
The patent introduces a data processing apparatus as an intermediary between the complex manufacturing data and the user. This apparatus automatically processes, analyzes, and presents the data in a comprehensible format, eliminating the need for manual monitoring while maintaining productivity benefits.
2Measurement precision
If all manufacturing data items are monitored to improve abnormality detection accuracy, then measurement precision is improved, but the complexity of data processing increases
Solution Approach 1:
The patent changes the parameter of data representation by assigning unique identifiers to each data item and transforming raw manufacturing data into a standardized format with timestamps and identification information. This parameter transformation enables automated processing while maintaining comprehensive monitoring capability.
Solution Approach 2:
The patent replaces manual data monitoring and analysis (mechanical human effort) with an automated data processing apparatus that uses computational algorithms to detect abnormalities. This substitution maintains high detection accuracy while reducing processing complexity for users.
3Ease of operation
If manual monitoring of manufacturing data is performed to identify cause of abnormalities, then ease of operation is maintained, but productivity decreases due to time-consuming analysis
Solution Approach 1:
The patent enables the data processing apparatus to perform self-service by automatically analyzing manufacturing data, identifying abnormalities, and determining cause candidates without requiring manual intervention. The system serves itself by processing its own data inputs and generating meaningful outputs autonomously.
Solution Approach 2:
The patent performs preliminary actions by pre-processing manufacturing data into a standardized format with unique identifiers and timestamps before analysis. This preliminary organization of data enables faster and more efficient abnormality detection and cause identification, improving overall productivity.
Data Source
AI summary
A data processing apparatus includes a processor. The processor generates visualization data for displaying estimation results of manufacturing conditions based on estimation results and relationship data. The relationship data includes first relationship data as a relationship between first manufacturing conditions recorded during an analysis, and second relationship data as a relationship between second manufacturing conditions corresponding. The processor divides the estimation results of the manufacturing conditions into a first group based on the first relationship data, and into a second group based on the second relationship data. The processor generates the visualization data based on a change in manufacturing condition relationship between the first group and the second group.