Print Medium Classification via Optical Density and Ink Deposition
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Solution Overview
Problem
Current methods for categorizing print media in high-speed production printing systems are prone to human error and are time-consuming, requiring extensive lab analysis for accurate paper type selection, which affects print quality.
Innovation Solution
A method using optical density and ink deposition measurement data to determine distribution function parameters, applied to machine learning logic for classifying print media into categories, optimizing print quality by selecting appropriate calibration settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical and chemical tests are performed on the paper to categorize print medium, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent replaces physical and chemical lab analysis with an optical measurement system using a spectrophotometer to measure ink dot gain on the paper. This substitution of measurement methodology dramatically reduces analysis time while maintaining classification accuracy, as the optical measurement can be performed quickly without extensive laboratory procedures.
Solution Approach 2:
The patent creates a digital copy of the paper's optical properties by measuring ink dot gain characteristics and storing them in a database. This digital representation allows for rapid comparison and classification without needing to perform repeated physical tests, significantly reducing time loss while preserving measurement precision.
2Ease of operation
If operator selects paper setting based on personal judgement, then ease of operation is improved, but reliability worsens
Solution Approach 1:
The system performs self-service by automatically measuring the paper's optical properties and determining the appropriate paper setting without operator intervention. The spectrophotometer measures ink dot gain, and the system automatically queries the database to select the correct paper type, eliminating human judgment while maintaining ease of operation and improving reliability.
Solution Approach 2:
The patent implements a feedback mechanism where the measured optical properties are compared against stored reference data in a database. The system uses this feedback loop to automatically determine the correct paper setting, replacing subjective operator judgment with objective, consistent measurements that improve reliability while keeping the process simple.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces human error and lab analysis time, providing accurate and efficient classification of print media, thereby improving print quality and operational efficiency in high-speed production printing.
Implementation Method 1
A spectrophotometer is used to measure an ink dot gain on a paper
Data Source
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AI summary
A system is disclosed. The system includes at least one physical memory device to store medium classification logic and one or more processors coupled with the at least one physical memory device to execute the medium classification logic to receive optical density (OD) measurement data corresponding to application of a halftone pattern using ink on a print medium in a printing system, receive ink deposition measurement data corresponding to application of the halftone pattern using the ink in a printing system, determine a set of distribution function parameters based on the OD measurement data and the ink deposition measurement data, apply the set of distribution function parameters to machine learning logic trained to classify the print medium and classify the print medium as a first of a plurality of print medium categories based on the machine learning logic.