Digital Twin Coating Control for Faster Quality Prediction
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Solution Overview
Problem
Existing coating processes for vehicle bodies are lengthy, expensive, and prone to errors, with conventional quality control methods only capable of localizing issues but not providing measures to optimize the process for enhanced coating quality.
Innovation Solution
A digital twin of the coating process is created through statistical processing of large amounts of classical quality control measurements and coating parameter information, allowing for automatic adjustment of parameters to improve coating quality and predict quality control outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple measurements at selected coordinates are performed manually or automatically using state-of-the-art sensor technology, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the coating process that replicates the physical coating line's behavior. This digital model allows quality control measurements to be performed virtually on copied data rather than requiring physical measurements on every actual coating, significantly reducing time loss while maintaining measurement precision through the use of sensor data from the physical system.
Solution Approach 2:
The system performs preliminary quality control assessments by continuously monitoring coating parameters during the coating process and using the digital twin to predict quality outcomes before the actual coating is completed. This allows potential defects to be identified early, reducing the need for lengthy post-coating measurement procedures.
2Measurement precision
If classical quality control methods are used to localize process critical issues, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The digital twin serves multiple functions simultaneously: it localizes process critical issues, predicts coating quality, optimizes coating parameters, and provides a virtual testing environment. This multi-functionality reduces the need for separate specialized measurement devices and procedures, thereby reducing overall device complexity while maintaining precise issue localization capabilities.
Solution Approach 2:
The digital twin acts as an intermediary between the physical coating process and the quality control analysis. Instead of directly complex measurement systems analyzing every aspect of the coating, the digital twin processes sensor data and provides simplified quality assessments, reducing the complexity of the overall quality control system while maintaining precise issue detection.
3Manufacturing precision
If coating parameters are adjusted to optimize coating quality, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system implements continuous feedback loops where the digital twin monitors coating parameters and quality outcomes in real-time, automatically adjusting coating parameters based on predicted quality results. This automated feedback mechanism improves manufacturing precision while reducing the complexity of manual parameter adjustment by letting the system self-optimize.
Solution Approach 2:
The digital twin enables the coating process to self-optimize by automatically analyzing its own performance data and adjusting parameters to maintain optimal coating quality. This self-service capability reduces the need for complex external control systems and manual intervention, thereby improving manufacturing precision without proportionally increasing device complexity.
Data Source
AI summary
A method for modelling a coating process including a plurality of coating parameters, includes the steps of: dispensing, by the coating process and during K work cycles, a coating on each of K pieces of objects to thereby obtain K pieces of coatings; recording, during each of the K work cycles, coating variable values of p coating parameters at M instances to thereby obtain recording results; and measuring at least one coating property at m locations of each of the K pieces of coatings to thereby obtain measurement results. The method is characterized by the step of determining a digital twin of the coating process on the basis of the recording results and the measurement results. By using results from a large amount of classical quality control measurements together with corresponding coating parameter information, a digital twin of the coating process can be determined through statistical processing of such big data. The digital twin may be used either for automatic adjustment of the coating parameters to obtain an improved coating quality, for prediction of the coating quality right after a work cycle to obtain an improved quality control, or for both.