Prediction of Mechanical Properties of Austenitic Stainless Steels with the Use of Synthetic Data via Generative Adversarial Networks

Leni, Desmarita; Kesuma, Dytchia Septi; Maimuzar,; Haris,; Afriyani, Sicilia

doi:10.3390/engproc2024063004

The 7th Mechanical Engineering, Science and Technology International Conference 2024

DOI: 10.3390/engproc2024063004

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Prediction of Mechanical Properties of Austenitic Stainless Steels with the Use of Synthetic Data via Generative Adversarial Networks

Desmarita Leni,

Dytchia Septi Kesuma,

Maimuzar

et al.

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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Optimizing stainless steel tensile strength analysis: through data exploration and machine learning design with Streamlit

Leni,

Karudin,

Abbas

et al. 2024

Eureka: PE

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The use of Exploratory Data Analysis (EDA) and machine learning in material science has rapidly advanced in recent years. EDA enables researchers to thoroughly explore and analyze material datasets, while machine learning allows for the development of predictive models capable of understanding complex patterns within the data. This study aims to develop an optimization tool to enhance the analysis of tensile strength in stainless steel by leveraging integrated data exploration and machine learning approaches within the Streamlit framework. The developed tool consists of four main features: data visualization, correlation analysis, 3D visualization, and machine learning. The developed machine learning model has 14 input variables, including chemical elements and heat treatment temperatures. In this research, the machine learning features comprise three models: Decision Tree, Random Forest, and Artificial Neural Network. The research findings indicate that the optimization tool can automatically display stainless steel tensile strength data using available pandas profiling in the visualization feature. The correlation feature can illustrate the relationship between chemical elements and heat treatment temperatures concerning stainless steel tensile strength. The 3D visualization feature can be utilized to identify optimal values of chemical elements and heat treatment temperatures according to desired tensile strength. Meanwhile, the machine learning feature can accurately predict stainless steel tensile strength based on chemical composition and heat treatment temperatures. This is evident from the performance evaluation metrics of the Random Forest model, which achieved MAE of 10.36, RMSE of 14.44, and R-squared of 0.97

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Optimizing stainless steel tensile strength analysis: through data exploration and machine learning design with Streamlit

Leni,

Karudin,

Abbas

et al. 2024

Eureka: PE

View full text Add to dashboard Cite

show abstract

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Prediction of Mechanical Properties of Austenitic Stainless Steels with the Use of Synthetic Data via Generative Adversarial Networks

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 1 publication

References 18 publications

Optimizing stainless steel tensile strength analysis: through data exploration and machine learning design with Streamlit

Optimizing stainless steel tensile strength analysis: through data exploration and machine learning design with Streamlit

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