Position Mark Detection for Distortion-Corrected Inkjet Printing
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Solution Overview
Problem
Existing inkjet printing apparatuses face challenges in accurately detecting position detection marks on elongated strip-shaped base materials due to expansion and contraction, leading to distorted printed images and misregistration of ink droplets.
Innovation Solution
A mark detection method that includes multiple stages of detection with varying matching thresholds to accurately identify position detection marks, followed by a distortion measurement method to calculate the amount of distortion, and a learning process to generate an estimation model for correcting ink ejection positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single mark detection step with a fixed matching threshold is used, then the detection process is simple and fast, but the detection accuracy is insufficient when marks are distorted due to base material expansion or contraction
Solution Approach 1:
The mark detection process is divided into multiple sequential detection steps, each with different matching thresholds. The first detection step uses a first matching threshold to detect clearly visible marks, while the second detection step uses a second matching threshold (lower than the first) to detect distorted or faint marks that were missed in the first step. This segmentation of the detection process into multiple stages with varying stringency resolves the contradiction by improving overall detection accuracy without requiring a complete redesign of the detection system.
Solution Approach 2:
The matching threshold is made dynamic rather than fixed. The system automatically adjusts the matching threshold based on the detection results - using a first (stricter) threshold initially, then switching to a second (more lenient) threshold for re-detection of areas that were not successfully detected in the first pass. This dynamic adjustment of detection parameters allows the system to adapt to varying mark quality and distortion levels, improving accuracy while maintaining operational simplicity.
2Measurement precision
If the matching threshold is set low to detect distorted marks, then detection sensitivity increases, but false detection of non-mark regions increases
Solution Approach 1:
The detection process is segmented into multiple steps with different threshold levels. The first detection step uses a strict first matching threshold to reliably identify clear marks with high confidence. The second detection step uses a more lenient second matching threshold specifically targeted at regions that were not detected in the first step. This segmentation allows the system to maintain high reliability in the first pass while improving sensitivity in the second pass without compromising overall detection reliability.
Solution Approach 2:
The first mark detection step acts as a preliminary filtering stage that removes clearly detectable marks from consideration before the second detection step. By performing the strict detection first and then applying the lenient threshold only to remaining undetected regions, the system prevents false detections that would occur if a low threshold were applied uniformly across the entire image. This preliminary action approach maintains reliability while enabling sensitive detection of distorted marks.
Data Source
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AI summary
This mark detection method includes a first mark detection step, a detection abnormality determination step, and a second mark detection step in order to detect a position of a position detection mark on a base material on which a learning image is printed in which a position detection mark is arranged at each of a plurality of mark arrangement positions. In the first mark detection step, a region that meets a predetermined first condition is detected as a region to be detected in a photographic image of the base material on which the learning image is printed. In the detection abnormality determination step, the mark arrangement position is compared with the region to be detected to determine a detection abnormality. In the second mark detection step, a region that meets a predetermined second condition is detected as a new region to be detected around the mark arrangement position determined as the detection abnormality. As a result, a position of the position detection mark can be more accurately detected on the base material on which the learning image is printed.