Rubber Composition Microscopy Segmentation for Accurate Formulation Regions

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing techniques fail to accurately define regions corresponding to formulations in microscopic images of rubber compositions, making it difficult to extract quantitative data on physical properties and structure.

Innovation Solution

A machine learning-based analysis method using a trained model, such as a TransUNet, to segment and define regions in microscopic images of rubber compositions, enabling precise region division and feature extraction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If conventional image analysis techniques are used to calculate indices from microscopic images, then processing simplicity is maintained, but measurement precision of formulation regions deteriorates

Engineering Contradiction:

Improveprocessing simplicityVSAvoidformulation region definition accuracy

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent replaces conventional mechanical/image processing methods with a machine learning-based system. A trained model automatically identifies and segments formulation regions in microscopic images, substituting manual or algorithmic thresholding approaches with intelligent pattern recognition that achieves superior measurement precision while maintaining ease of use through automated processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If machine learning models are introduced to accurately define formulation regions, then measurement precision improves, but device complexity increases

Engineering Contradiction:

Improveformulation region definition accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent uses a trained machine learning model that has been pre-trained on representative data, creating a reusable computational template. This copied knowledge allows the system to accurately define formulation regions without requiring complex real-time processing or extensive computational resources during actual measurement, thereby reducing operational device complexity while maintaining high measurement precision.

Inventive Principle:

Principle #26Copying

3Loss of time

If quantitative data extraction is attempted without proper region definition, then processing time is reduced, but measurement precision deteriorates

Engineering Contradiction:

Improveprocessing timeVSAvoidphysical property estimation accuracy

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The patent performs preliminary action by automatically and accurately defining formulation regions before quantitative data extraction. The trained model pre-segments the microscopic image into distinct formulation regions, creating a structured basis for subsequent analysis. This preliminary region definition enables efficient quantitative measurement without sacrificing precision, as the groundwork for accurate property estimation is already in place.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20240192192A1Analysis method for rubber composition and generation method for trained model

Publication Date: 2024.06.13 SUMITOMO RUBBER INDUSTRIES LTD

AI summary

An analysis method for a rubber composition includes the followings: (1) acquiring input data generated from a microscopic image obtained by image-capturing a rubber composition with a microscope, the microscopic image showing a formulation contained in the rubber composition; (2) inputting the input data to a trained machine learning model; and (3) deriving output data from the trained machine learning model. The output data is data defining a region that appears in the microscopic image corresponding to the formulation.