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Introduction

As the adoption of Materials Informatics (MI) and Process Informatics (PI) advances, many teams may be facing challenges such as:

We have applied machine learning using numerical data such as formulation ratios and process conditions, but prediction accuracy has reached a plateau

Although we have a large number of electron microscope images, evaluation still relies on expert visual judgment, making objective assessment difficult

The final properties of materials (such as strength, conductivity, and adhesion) depend not only on input conditions, but also heavily on the resulting microstructure and dispersion state.

However, image data that capture these aspects are often left unused or remain dependent on subjective evaluation.

In this article, we explain how to transform such “dormant image data” into valuable data assets for materials development, and introduce a practical approach.

1. Image Data in Materials Development

In materials and process development, large volumes of image data are generated daily:

Electron microscope images (SEM / TEM): dispersion state of fillers, degree of aggregation, fracture surface observation

Optical microscope images: surface defects, crystal structures

Visual inspection images: adhesive residue, surface quality of films

These images contain valuable information related to material performance.

However, in many cases, they are simply “viewed and not further utilized.”

Images included in reports or experimental notes are preserved as records, but rarely used as data.

2. Why Image Data Is Not Utilized

There are three main reasons why image data is not effectively utilized:

(1) Difficulty in Quantification

While it is possible to judge visually whether dispersion is good or bad, or whether defects are many or few,

converting these observations into objective numerical values (features) is not easy.

Manual measurement is time-consuming and becomes impractical for complex data.

(2) Lack of Integration with Numerical Data

Even if numerical values can be extracted from images,

they cannot be incorporated into MI/PI frameworks unless they are linked with formulation and process data as input variables.

(3) Dependence on Subjective Evaluation

Although expert visual assessment can be accurate, it introduces challenges such as:

Lack of reproducibility

Variability in evaluation

Difficulty in knowledge transfer

3. What Image Analysis AI Can Do

Image analysis AI addresses these challenges by extracting quantitative features from images.

(1) Quantification of Dispersion and Aggregation

It analyzes particle distribution and aggregation, converting them into measurable indicators.

This transforms subjective evaluations into objective metrics.

(2) Objective Evaluation of Defects

Surface defects and adhesive residue can be classified and quantified, improving the accuracy of quality evaluation.

(3) Ensuring Reproducibility

Automated analysis enables consistent results regardless of who performs the evaluation.

This shifts image evaluation from subjective judgment to standardized, reproducible processes.

4. Potential of Image Features × MI Analysis

By integrating image-derived features with formulation and process data, new possibilities emerge.

(1) Understanding Structure–Property Relationships

By combining features such as particle size, dispersion, and defect density with experimental conditions,

it becomes possible to clarify:

how processing conditions influence microstructure, and how that structure determines final material properties.

Introducing “structure” as an intermediate variable significantly improves model accuracy.

(2) Optimization of Formulation and Process Conditions (Inverse Analysis)

Based on quantitative image data, AI can recommend optimal experimental conditions.

For example:

What mixing conditions achieve the desired dispersion state?

What process minimizes defects or residue?

These questions can be answered based on data-driven insights.

(3) Acceleration of Development

This approach reduces trial-and-error, shortens development time, and improves process efficiency.

Conclusion: Turning Image Data into Assets

Image data in materials development, when properly quantified and integrated, can become a powerful asset.

By combining image analysis with MI, it becomes possible to reveal the relationship between structure and properties,

and advance materials development toward a truly data-driven approach.

Utilizing Image Analysis in Materials Development — Unlocking the Value of Dormant Unstructured Data —

Introduction

1. Image Data in Materials Development

2. Why Image Data Is Not Utilized

(1) Difficulty in Quantification

(2) Lack of Integration with Numerical Data

(3) Dependence on Subjective Evaluation

3. What Image Analysis AI Can Do

(1) Quantification of Dispersion and Aggregation

(2) Objective Evaluation of Defects

(3) Ensuring Reproducibility

4. Potential of Image Features × MI Analysis

(1) Understanding Structure–Property Relationships

(2) Optimization of Formulation and Process Conditions (Inverse Analysis)

(3) Acceleration of Development

Conclusion: Turning Image Data into Assets

Masahiro Fujita

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