OPTIMIZING STAINLESS STEEL TENSILE STRENGTH ANALYSIS - THROUGH DATA EXPLORATION AND MACHINE LEARNING DESIGN WITH STREAMLIT

Aug 2, 2026

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Exploratory Data Analysis and machine learning accelerate material science by enabling systematic dataset exploration and predictive modeling of complex relationships. This study develops an optimization tool for stainless steel tensile strength by integrating EDA and multiple machine learning approaches within a Streamlit framework. The tool supports data visualization, correlation analysis, and 3D visualization, while models use 14 inputs including chemical elements and heat-treatment temperatures. Random Forest delivers strong accuracy with MAE 10.36, RMSE 14.44, and R-squared 0.97.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

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Abstract

1. Introduction

Original Research Article: full paper (2024), «EUREKA: Physics and Engineering» Number 5 OPTIMIZING STAINLESS STEEL TENSILE STRENGTH ANALYSIS: THROUGH DATA EXPLORATION AND MACHINE LEARNING DESIGN WITH STREAMLIT Desmarita Leni Department of Mechanical Engineeringі Universitas Muhammadiyah Sumatera Barat 4 Pasir Jambak str., Pasie Nan Tigo, Kec. Koto Tangah, Kota Padang, Sumatera Barat, Indonesia, 25586 Arwizet Karudin * Department of Mechanical Engineering Universitas Negeri Padang Prof. Dr. Hamka str., Air Tawar Padang, Sumatera Barat, Indonesia, 25132 [arwizet@ft.unp.ac.id](arwizet@ft.unp.ac.id) Muhammad Rabiu Abbas Department of Mechanical Engineering Federal University of Transportation – Daura 28JF+J29, Daura, Katsina, Nigeria, 824101 Jai Kumar Sharma Department of Mechanical Engineering ITM University NH-44, Bypass Burari, Jhansi Road Gwalior (M.P.), India, 475001 AdriansyahAdriansyah Department of Mechanical Engineering Politeknik Negeri Padang Kampus str., Limau Manis, Kec. Pauh, Kota Padang, Sumatera Barat, Indonesia, 25164 *Corresponding author Abstract 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 ofthe Random Forest model, which achieved MAE of 10.36, RMSE of 14.44, and R-squared of 0.97. Keywords: stainless steel, tensile strength, exploratory data analysis, machine learning, Streamlit. DOI: 10.21303/2461-4262.2024.003296 1. Introduction The conventional analysis of the mechanical properties of materials, especially alloy steels, often involves a series of experimental physical tests and laboratory testing [1]. This process is time-consuming and requires significant costs. The initial stages involve the preparation of varied 73 Original Research Article: full paper (2024), «EUREKA: Physics and Engineering» Number 5 samples, requiring a considerable amount. Subsequently, testing is conducted using specialized equipment, such as tensile testing machines and hardness testing machines. The data generated from these tests then needs to be manually analyzed to extract information about the desired mechanical properties of the material, such as strength, toughness, and hardness [2, 3]. This conventional approach is often prone to human errors due to the complexity of the data required to obtain accurate analysis results. Research indicates [4, 5], that when designing t

FAQs

What is the main goal of the study?

To build an optimization tool that enhances stainless steel tensile strength analysis by combining exploratory data analysis with machine learning inside a Streamlit app.

What inputs and machine learning models are used to predict tensile strength?

The model uses 14 input variables, including chemical elements and heat-treatment temperatures, and trains Decision Tree, Random Forest, and Artificial Neural Network models.

How does the Streamlit tool support analysis beyond prediction?

It provides data visualization, correlation analysis between variables and tensile strength, and 3D visualization to help identify optimal chemical element and heat-treatment temperature values.

Aug 2, 2026

Upload by Aria