Optimization of annealing cycle parameters of dual phase and interstitial free steels by multiobjective genetic algorithms

Shah, Karimulla; Kumar, Rishav; Sahoo, Sibasis; Pais, R.; Chakrabarti, Debalay; Chakraborti, Nirupam

doi:10.1080/10426914.2016.1257134

Cited by 7 publications

(1 citation statement)

References 33 publications

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“…For instance, in [4] physically-based models for the prediction of tensile properties (Ultimate Tensile Strength and Yield Strength) of cold rolled Al-Killed and Interstitial Free (IF) steels are tuned through Genetic Algorithms (GA). In [5] data-driven models are developed for Ultimate Tensile Strength and percentage elongation, to the aim of achieving an optimum strength-ductility balance in Dual Phase (DP) and IF steels by optimizing the annealing cycle parameters through a multi-objective GA-based approach.…”

Section: Introductionmentioning

confidence: 99%

A Modular Machine-Learning-Based Approach to Improve Tensile Properties Uniformity Along Hot Dip Galvanized Steel Strips for Automotive Applications

Colla

Cateni

Maddaloni

et al. 2020

Metals

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The paper presents a machine learning-based system aimed at improving the homogeneity of tensile properties of steel strips for automotive applications over their strip length in the annealing and hot dip galvanizing lines. A novel modular approach is proposed exploiting process and product data and combining smart data pre-processing and cleansing algorithms, an ensemble of neural networks targeted to specific product classes and an ad-hoc developed iterative procedure for identifying the variability ranges of the most relevant process variables. A decision support concept is implemented through a software tool, which facilitates exploitation by plant managers and operators. The system has been tested on site. The results show its effectiveness in improving the control of the thermal evolution of the strip with respect to the standard operating practice.

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Section: Introductionmentioning

confidence: 99%