Fabric Feature Analysis for Automated Parameter Optimization
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Solution Overview
Problem
In the context of fabric printing, it is challenging to set appropriate parameters for pre-processing and post-processing devices due to varying fabric features influenced by environmental conditions and fiber types, leading to cumbersome operation and inconsistent image quality.
Innovation Solution
An information processing device that acquires image and status data, transmits it to a server, and receives learned model data to derive recommended parameters for pre-processing and post-processing devices, optimizing fabric treatment based on fabric features and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If appropriate parameters are manually set by repeating the printing process while changing parameters, then image quality can be optimized for different fabric features, but operation becomes cumbersome and time-consuming
Solution Approach 1:
The system automatically acquires fabric feature values through image capture and analysis, and the server automatically determines optimal processing parameters based on the fabric features and environmental conditions. This self-service mechanism eliminates the need for manual parameter setting and repeated trial printing, thereby reducing operation time while maintaining image quality optimization.
Solution Approach 2:
The patent replaces the manual mechanical process of parameter adjustment with an automated information processing system. Image capture devices, analysis algorithms, and server-based parameter determination substitute for manual observation and adjustment, converting a labor-intensive process into an automated computational system that rapidly determines optimal parameters.
2Manufacturing precision
If manual parameter setting is used to adapt to varying fabric features, then image quality can be maintained, but operation complexity increases
Solution Approach 1:
The system performs automatic fabric feature analysis and parameter determination without requiring user intervention. The image capture device automatically captures fabric features, the analysis algorithm automatically extracts relevant characteristics, and the server automatically determines optimal parameters, making the system self-sufficient and easy to operate.
Solution Approach 2:
The system is designed to handle various fabric types universally through automatic feature recognition and analysis. Rather than requiring different manual procedures for different fabrics, the system automatically adapts to any fabric type by analyzing its specific features and determining appropriate parameters, thereby simplifying operation across diverse materials.
3Adaptability or versatility
If comprehensive fabric feature coverage is attempted to handle all fabric types and conditions, then adaptability improves, but system complexity increases
Solution Approach 1:
The patent extracts only the essential fabric features relevant to processing parameter determination from the complete fabric characteristics. Rather than analyzing all possible fabric properties, the system identifies and extracts key features through image analysis that directly influence optimal parameter selection, thereby reducing system complexity while maintaining comprehensive adaptability.
Solution Approach 2:
The system determines processing parameters dynamically based on the extracted fabric features and environmental conditions. Rather than requiring a fixed complex configuration for each fabric type, the system changes parameters adaptively based on real-time fabric特征 analysis, achieving comprehensive coverage through flexible parameter adjustment rather than rigid system complexity.
Data Source
AI summary
An information processing device includes an acquisition unit acquiring image data and status data, a transmission unit transmitting, to a server, the image data and the status data, a reception unit receiving, from the server, first data generated by the server based on the image data and the status data, a storage unit storing the first data received by the reception unit and second data, and a control unit, wherein the first data is data defining a learned model learned by machine learning, the learned model being configured to, when the image data and the status data are input, output fabric data indicating a feature value of the fabric, the second data indicates a correspondence relationship between the fabric data and a recommended parameter, and the control unit derives, based on the first data and the second data, the recommended parameter from the image data and the status data.