Inkjet Nozzle Defect Detection with Automated Type Classification
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Solution Overview
Problem
Manual print defect identification in inkjet printers is time-consuming and prone to inaccuracies, especially in distinguishing between different types of nozzle defects, which affects print quality and maintenance efficiency.
Innovation Solution
A defect detection system that automatically classifies nozzle defects using reference data and machine learning algorithms, such as a trained Deep Neural Network, to determine the type and location of defects in printed images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual print defect identification is performed by a trained operator, then defect types can be distinguished, but the process is time-consuming and subject to inaccuracies
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated image processing system that captures printed media images, compares them against reference data, and automatically identifies nozzle defects. This substitution eliminates human time constraints and subjectivity while maintaining high accuracy through systematic image analysis algorithms.
Solution Approach 2:
The system enables self-service defect detection by automatically comparing captured images with stored reference patterns without requiring operator intervention. The automated comparison algorithm independently identifies defect types, locations, and characteristics, freeing operators from time-consuming manual analysis while ensuring consistent accuracy.
2Loss of information
If manual defect analysis is performed to distinguish among various types of print defects, then defect categorization is achieved, but the process is time-consuming
Solution Approach 1:
The system performs preliminary action by pre-storing reference defect patterns and characteristics in a database before actual defect detection occurs. When a defect is detected, the system immediately compares the captured image against these pre-prepared references, enabling rapid categorization without time-consuming manual analysis. This preliminary preparation of reference data allows for instant defect type identification.
3Productivity
If automated defect detection is implemented, then analysis speed increases, but precision in distinguishing defect types may decrease
Solution Approach 1:
The system segments the defect detection process into distinct analytical stages: image capture, feature extraction, reference comparison, and defect classification. Each stage focuses on specific characteristics, allowing the automated system to maintain high precision by systematically analyzing multiple defect attributes rather than relying on a single generalized detection algorithm.
Solution Approach 2:
The system employs parameter changes by adjusting detection thresholds, comparison criteria, and analysis depth based on the specific defect type being detected. The automated algorithm dynamically modifies analysis parameters to optimize both speed and precision for different defect categories, ensuring accurate classification while maintaining rapid processing across varied defect scenarios.
Data Source
AI summary
Systems and methods are provided for. One embodiment is a system that includes an interface configured to receive an image of media printed on with print data, and memory configured to store defect reference data of nozzles belonging to printheads of a printer. The system also includes a print defect controller configured to detect a nozzle defect in the image based on a comparison of the image with the print data, and to determine a type of the nozzle defect based on a comparison of the nozzle defect with the defect reference data.