ISSN 2594-5297
60th Seminar on Rolling, Metal Forming and Products — Vol. 60, Num. 60 (2025)
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Abstract
In response to the growing demand for high-performance materials in the automotive industry, this study presents a hybrid methodology that integrates machine learning and multi-objective genetic algorithms to optimize the mechanical properties of galvanized DP780 steel. Using a dataset of approximately 14,000 industrial coils, predictive models were developed to estimate Yield Strength (YS), Ultimate Tensile Strength (UTS), and Total Elongation (EL) based on chemical composition and process parameters. Among the evaluated models, the LightGBM demonstrated superior predictive accuracy and was further analyzed using SHAP values to identify key influencing variables. Subsequently, the NSGA-II algorithm was employed to optimize processing conditions and alloying elements, aiming to enhance mechanical performance while increasing production speed. The optimized parameters were implemented on an industrial galvanizing line, resulting in a 12.5% increase in line speed and improved material properties. These results validate the effectiveness of AI-driven optimization strategies in industrial steel manufacturing, offering a robust framework for enhancing both product quality and operational efficiency
IN RESPONSE TO THE GROWING DEMAND FOR HIGH-PERFORMANCE MATERIALS IN THE AUTOMOTIVE INDUSTRY, THIS STUDY PRESENTS A HYBRID METHODOLOGY THAT INTEGRATES MACHINE LEARNING AND MULTI-OBJECTIVE GENETIC ALGORITHMS TO OPTIMIZE THE MECHANICAL PROPERTIES OF GALVANIZED DP780 STEEL. USING A DATASET OF APPROXIMATELY 14,000 INDUSTRIAL COILS, PREDICTIVE MODELS WERE DEVELOPED TO ESTIMATE YIELD STRENGTH (YS), ULTIMATE TENSILE STRENGTH (UTS), AND TOTAL ELONGATION (EL) BASED ON CHEMICAL COMPOSITION AND PROCESS PARAMETERS. AMONG THE EVALUATED MODELS, THE LIGHTGBM DEMONSTRATED SUPERIOR PREDICTIVE ACCURACY AND WAS FURTHER ANALYZED USING SHAP VALUES TO IDENTIFY KEY INFLUENCING VARIABLES. SUBSEQUENTLY, THE NSGA-II ALGORITHM WAS EMPLOYED TO OPTIMIZE PROCESSING CONDITIONS AND ALLOYING ELEMENTS, AIMING TO ENHANCE MECHANICAL PERFORMANCE WHILE INCREASING PRODUCTION SPEED. THE OPTIMIZED PARAMETERS WERE IMPLEMENTED ON AN INDUSTRIAL GALVANIZING LINE, RESULTING IN A 12.5% INCREASE IN LINE SPEED AND IMPROVED MATERIAL PROPERTIES. THESE RESULTS VALIDATE THE EFFECTIVENESS OF AI-DRIVEN OPTIMIZATION STRATEGIES IN INDUSTRIAL STEEL MANUFACTURING, OFFERING A ROBUST FRAMEWORK FOR ENHANCING BOTH PRODUCT QUALITY AND OPERATIONAL EFFICIENCY
Keywords
Optimization, Machine Learning, AHSS, Galvanizing
Optimization, Machine Learning, AHSS, Galvanizing
How to cite
Felipe Finamor; ELIAS, EDUARDO POSSA; ALVES, GABRIEL GODINHO; CORRêA, SILVIO; DRUMOND, JULIOVANY; FERREIRA, JETSON LEMOS; Paulo Roberto Mei.
OPTIMIZATION OF THE MECHANICAL PROPERTIES OF DP780 GI STEEL USING GENETIC ALGORITHMS AND MACHINE LEARNING: AN INDUSTRIAL APPLICATION,
p. 343-355.
In: 60th Seminar on Rolling, Metal Forming and Products,
São Paulo, Brasil,
2025.
ISSN: 2594-5297, DOI 10.5151/2594-5297-42302
