Inkjet Printing Defect Correction via Region Analysis
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Solution Overview
Problem
Conventional inkjet printing methods for glass, ceramics, or glass ceramics substrates are inefficient due to time-consuming defect detection and correction processes, which vary with printer configurations and optical assistance means, leading to inconsistent printing quality across different printers and images.
Innovation Solution
A method and system that utilize machine learning and neural networks to optimize the printing process by defining regions of interest, analyzing image metrics, and adjusting printer parameters for enhanced quality, allowing for quicker defect detection and correction, and improved consistency across substrates and images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional inkjet printing methods are used with optical graphic assistance means for defect detection, then printing defects can be detected, but the process becomes very time consuming and inefficient requiring multiple re-processing passes
Solution Approach 1:
The patent applies preliminary action by performing defect detection and correction parameter calculation before the actual printing process. The system analyzes image data and substrate information in advance to pre-determine correction parameters, which are then applied during printing to prevent defects rather than detecting and correcting them after occurrence, thereby eliminating the need for multiple re-processing passes
Solution Approach 2:
The patent implements feedback by using previously stored correction parameters and substrate-specific characteristics to continuously adjust printing parameters during the printing process. This closed-loop approach allows real-time adaptation to substrate variations and printer conditions, ensuring consistent quality without requiring time-consuming post-print inspection and rework
2Measurement precision
If the total printing area of the glass is checked for defect detection, then all defects can be identified, but the process becomes more time consuming
Solution Approach 1:
The patent applies segmentation by dividing the printing area into multiple regions based on substrate characteristics, printer configuration, and image content. Instead of uniformly checking the entire printing area, the system identifies and prioritizes critical regions where defects are most likely to occur, such as areas with high ink deposition rates or specific pattern regions, thereby reducing detection time while maintaining comprehensive coverage
Solution Approach 2:
The system performs preliminary analysis of image data and substrate information to pre-identify regions prone to defects before actual printing occurs. By calculating correction parameters in advance for specific regions based on stored substrate characteristics and printer performance data, the system can focus detection resources on critical areas rather than systematically scanning the entire printing surface
3Adaptability or versatility
If defect definition varies with printer configuration and optical assistance means, then different printers can be accommodated, but printing quality becomes inconsistent across different printers and images
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting detection and correction parameters based on specific printer configuration, substrate type, and image characteristics. The system stores correction parameters for different printer configurations and substrate types, then selects and applies the appropriate parameters for each printing job, ensuring consistent quality definition across all printers while maintaining adaptability to different hardware configurations
Solution Approach 2:
The system implements feedback by continuously monitoring printing results and using this information to refine correction parameters for specific printer-substrate-image combinations. By analyzing actual printing performance and comparing it against target quality specifications, the system automatically adjusts parameters to maintain consistent quality across different printers, bridging the gap between adaptability and precision
4Productivity
If the same image is printed on the same substrate again, then production efficiency is maintained, but the same defects may arise repeatedly
Solution Approach 1:
The patent applies preliminary action by analyzing previous printing results and storing correction parameters specific to each substrate type, printer configuration, and image combination. Before printing the same image again on the same substrate, the system retrieves previously determined correction parameters and applies them in advance, preventing the recurrence of identical defects while maintaining production efficiency through automated parameter application
Solution Approach 2:
The system implements feedback by using results from previous printing jobs to continuously improve future printing quality. Correction parameters are stored and refined based on actual printing performance data, allowing the system to learn from past defects and automatically adjust parameters for subsequent printing of the same image on the same substrate, thereby ensuring reliable quality consistency
Data Source
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AI summary
The present invention relates to a method for printing an image on a substrate, preferably glass, ceramics or glass ceramics, comprising the steps of: - Providing image template data for an image to be printed by an image providing computing entity, - Analyzing said image template data by identifying image components of said image, - Providing a substrate for printing said image thereon by a substrate feeding entity, - Providing an ink for printing said image by an ink supply entity, - Printing said image using said image template data on said substrate with said ink using a printing procedure, preferably Inkjet-Printing-procedure, based on printing parameters with a printer operating using printer configuration parameters, - Capturing said printed image on said substrate by one or more image capturing entities, and providing captured image data of said captured image, - Analyzing said captured image data, using an analyzing procedure by an analysis computing entity, said analyzing procedure comprising ∘ Providing definition parameters for defining at least one region of interest, ROI, ∘ Determining said at least one ROI, within the captured image data based on the provided definition parameters, ∘ Identifying in said at least one ROI at least one image component, and ∘ Determining for said at least one ROI at least one image metric, ∘ Relating said at least one image metric to said at least one image component, - Relating said identified at least one image component in said image template data to said at least one identified image component of said at least one ROI related with said at least one image metric, - Selecting one or more printer configuration parameters and/or printing parameters for correction, preferably based on at least one image metric and/or at least one image component, - Computing at least one actual correction parameter for said selected printer configuration parameters and/or said selected printing parameters based on an optimization computing procedure using said at one image metric, wherein said at least one actual correction parameter being image component specific.