# Multi-Objective Optimization of Adhesive Joint Strength and Elastic Modulus of Adhesive Epoxy with Active Learning

https://mdr.nims.go.jp/datasets/9d3aacb6-6673-4bdb-b786-aab00c23ede1

## File

- [materials-17-02866.pdf](https://mdr.nims.go.jp/filesets/ff491baf-4210-44cf-8fe3-10d5d8c1d873/download) ([Detail](https://mdr.nims.go.jp/filesets/ff491baf-4210-44cf-8fe3-10d5d8c1d873.md))

## Id

9d3aacb6-6673-4bdb-b786-aab00c23ede1

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-07-16T08:12:29.555897Z

## Updated at

2025-01-06T23:30:26.400305Z

## Published at

2025-01-06T23:30:26.558792Z

## Doi



## First published url

https://doi.org/10.3390/ma17122866

## Date published

2024-06-12

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Multi-Objective Optimization of Adhesive Joint Strength and Elastic Modulus
    of Adhesive Epoxy with Active Learning
  title_type: original
  lang: en

## Description

- description: Studying multiple properties of a material concurrently is essential
    for obtaining a comprehensive understanding of its behavior and performance. However,
    this approach presents certain challenges. For instance, simultaneous examination
    of various properties often necessitates extensive experimental resources, thereby
    increasing the overall cost and time required for research. Furthermore, the pursuit
    of desirable properties for one application may conflict with those needed for
    another, leading to trade-off scenarios. In this study, we focused on investigating
    adhesive joint strength and elastic modulus, both crucial properties directly
    impacting adhesive behavior. To determine elastic modulus, we employed a non-destructive
    indentation method for converting hardness measurements. Additionally, we introduced
    a specimen apparatus preparation method to ensure the fabrication of smooth surfaces
    and homogeneous polymeric specimens, free from voids and bubbles. Our experiments
    utilized a commercially available bisphenol A-based epoxy resin in combination
    with a Poly(propylene glycol) curing agent. We generated an initial dataset comprising
    experimental results from 32 conditions, which served as input for training a
    machine learning model. Subsequently, we used this model to predict outcomes for
    a total of 256 conditions. To address the high deviation in prediction results,
    we implemented active learning approaches, achieving a 50% reduction in deviation
    while maintaining model accuracy. Through our analysis, we observed a trade-off
    boundary (Pareto frontier line) between adhesive joint strength and elastic modulus.
    Leveraging Bayesian optimization, we successfully identified experimental conditions
    that surpassed this boundary, yielding an adhesive joint strength of 25.2 MPa
    and an elastic modulus of 182.5 MPa.
  description_type: abstract
  lang: und

## Creator

- name: Paripat Kraisornkachit
  role: author
  orcid: https://orcid.org/0000-0002-0796-3665
- name: Masanobu Naito
  role: author
  orcid: https://orcid.org/0000-0001-7198-819X
- name: Chao Kang
  role: author
  orcid: https://orcid.org/0000-0002-9567-0976
- name: Chiaki Sato
  role: author

## Contact agent



## Publisher

organization: MDPI AG

## Managing organization



## Keyword

- subject: experimental testing
  schema: not_defined
- subject: multi-objective optimization
  schema: not_defined
- subject: epoxy
  schema: not_defined
- subject: adhesive
  schema: not_defined
- subject: elastic modulus
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: active learning
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Materials
  issn: '19961944'
  volume: '17'
  issue: '12'
  article_number: '2866'

## Conference



## Related item



## Funding

- identifier: JPMJCR19J3
  funder_name: Japan Science and Technology Agency
- identifier: 23H02031
  funder_name: KAKENHI Grant-in-Aid for Scientific Research
- identifier: JPMXP1122714694
  funder_name: 'MEXT Program: Data Creation and Utilization-Type Material Research
    and Development Project'

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## Fileset

- id: ff491baf-4210-44cf-8fe3-10d5d8c1d873
  filename: materials-17-02866.pdf
  content_type: application/pdf
  size: 3426326
  md5: d88dff788ad06fb18a3c77d1ff52ca48

## Thumbnail

fileset_id: ff491baf-4210-44cf-8fe3-10d5d8c1d873
filename: materials-17-02866.pdf