Database for Microstructure Data via Magnetic Phase Transitions

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Solution Overview

Problem

In materials development, the complexity of microstructure in multi-component materials increases the time, labor, and cost required to achieve desired properties, and the reliability of microstructure data varies significantly with the skill of the measurer, making it difficult to acquire large amounts of accurate data efficiently.

Innovation Solution

A database and material data processing system that stores data associated with unique sample identifiers, including composition, processing, and property data, with microstructure data defined by features such as temperature dependence of magnetization, allowing for the construction of mathematical models that predict material properties and microstructure characteristics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If microstructure data is acquired through manual measurement or observation by researchers, then detailed microstructure information can be obtained, but the reliability varies greatly depending on the skills of the measurer and it is difficult to acquire large amounts of data efficiently

Engineering Contradiction:

Improvemicrostructure data reliabilityVSAvoiddata acquisition efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual measurement methods with automated measurement devices that use magnetic field interactions to detect microstructure features. The measurement device automatically measures magnetic properties and determines microstructure features without human intervention, eliminating skill-dependent variability while maintaining measurement precision and enabling high-throughput data acquisition

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The measurement device performs self-measurement of microstructure features through automated magnetic property detection. The system automatically acquires temperature-dependent magnetic property data, determines microstructure features from this data, and stores the results without requiring operator skills, thereby achieving both high reliability and efficient data acquisition

Inventive Principle:

Principle #25Self-service

2Reliability

If the number of experiments is increased to achieve desired material properties in complex multi-component materials, then the probability of finding optimal composition increases, but the time, labor and cost required for material development significantly increases

Engineering Contradiction:

Improvematerial property achievementVSAvoidmaterial development time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent performs preliminary determination of microstructure features from temperature-dependent magnetic property data before conducting full material property experiments. This preliminary action provides guidance for selecting promising compositions, reducing the number of full experiments needed and accelerating material development while maintaining reliability

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent uses temperature-dependent magnetic property data as an intermediary to infer microstructure features, which then serve as a basis for predicting material properties. This intermediary approach enables indirect assessment of microstructure without requiring extensive direct experimentation, reducing time and cost while maintaining accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

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 enables the efficient acquisition of reliable microstructure data, reduces variability attributed to skill-dependent measurements, and facilitates the development of novel materials by leveraging machine learning models based on temperature-dependent magnetic phase transitions.

Implementation Method 1

The microstructure data includes a feature determined based on a temperature dependence of magnetization for the each sample

Methodology Applied

Scientific EffectTemperature dependence of magnetization: Curie Point (ferromagnetic)

Implementation Method 2

the feature determined based on the temperature dependence of magnetization is a feature regarding a magnetic phase transition

Methodology Applied

Scientific EffectMagnetic phase transition: Curie Point (ferromagnetic)

Implementation Method 3

the feature regarding the magnetic phase transition includes at least one of a Curie temperature and a Néel temperature

Methodology Applied

Scientific EffectCurie temperature: Curie Point (ferromagnetic)

Data Source

PatentUS11934360B2Database, material data processing system, and method of creating database

Publication Date: 2024.03.19 PROTERIAL LTD

AI summary

A database storing data associated with an identifier unique to each sample, the data including first data representative of at least one of composition data, processing data, and property data for the each sample, and second data representative of microstructure data for the each sample. The microstructure data includes a feature determined based on a temperature dependence of magnetization for the each sample.