Paint Quality Control Using Temporal Process-State Correlation
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Solution Overview
Problem
Existing quality control systems for paint on large targets like vehicle bodies fail to clearly establish correlations between multiple step control items and their temporal changes with quality control items, leading to inadequate paint quality management.
Innovation Solution
A quality control system and program that utilize a conveyor facility, paint facilities, and inspection facilities to detect and associate step control values with quality control values, calculate state values based on temporal changes, and learn correlations using a learning section, enabling clear correlations and predictive analytics for paint quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional quality control methods are used to monitor painting steps, then individual step control can be maintained, but correlations between multiple step control items and quality control items cannot be clearly established
Solution Approach 1:
The patent combines multiple step control value measurements from different painting steps with quality control value measurements into a single integrated data set. The learning section processes this merged data to identify correlations between step control items (e.g., spray pressure, paint flow rate) and quality control items (e.g., paint thickness, surface defects), establishing relationships that traditional separate monitoring methods cannot detect.
Solution Approach 2:
The system implements feedback by using the learning section to analyze historical data and generate correlations between step control parameters and quality outcomes. This feedback loop allows the system to identify which step control items most influence quality control items, enabling predictive quality control and process optimization based on learned relationships.
2Speed
If only instantaneous step control values are monitored, then real-time control is achieved, but temporal changes and their impact on paint quality are not sufficiently considered
Solution Approach 1:
The system performs preliminary action by calculating state values that represent temporal changes in step control parameters before these changes affect paint quality. The learning section uses this pre-processed temporal information to identify how changes in step control values over time influence quality outcomes, enabling predictive control that prevents quality degradation before it occurs.
3Adaptability or versatility
If manual inspection methods are used for quality control, then flexibility in assessment is maintained, but automation and data integration capabilities are reduced
Solution Approach 1:
The system achieves universality by designing the learning section to process both automatically measured quality control values from inspection apparatus and manually assessed quality values entered through input apparatus. This multi-functional capability allows the system to adapt to different inspection methods while maintaining automated data integration and correlation analysis, combining the flexibility of manual inspection with the data processing power of automation.
Data Source
AI summary
A quality control system for controlling quality of a paint target includes: an individual identification section associating a step control value and a quality control value with identification information for the paint target on the basis of the amount of movement; a state value computing section calculating a state value indicative of a paint state of the paint target on the basis of a history of a temporal change of the step control value; a learning section learning a correlation between the step control value, the state value, and the quality control value using sets of the step control value, the state value, and the quality control value; an input device receiving information on the amount of movement, the step control value, and the quality control value; and a storage device storing a set of the identification information, the step control value, the state value, and the quality control value.