The Neural Network Estimator for Mechanical Property of Rolled Steel Bar

Huang, Ching‐Wen; Chen, Ying-Tsung; Chen, Yu‐Ju; Chang, Chia‐Hung; Huang, Huang-Chu; Hwang, Rey-Chue

doi:10.1109/icicic.2009.361

Cited by 2 publications

(4 citation statements)

References 10 publications

(13 reference statements)

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“…AI has been used for a variety of studies in materials science, including steel research. For instance, the study by Huang et al [7] effectively utilized 1400 datasets, which included billet compositions, control parameters of the rolling process, and mechanical properties of the rolled steel bars, as inputs to the neural network. The AI analyzer confidently predicted critical factors such as yield strength, tensile strength, and elongation percentage for the rolled steel bars.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The AI analyzer confidently predicted critical factors such as yield strength, tensile strength, and elongation percentage for the rolled steel bars. Their algorithm could precisely set the related control parameters on the bar rolling process to enhance the quality of steel bars while simultaneously reducing production costs [7].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In light of previous research [7][8][9][10][11] showing the effectiveness and suitability of neural network models for solving such challenges, this study aims to develop a neural network model capable of predicting the microstructural phase fractions and hardness values of steel, based on its chemical composition and heat treatment profile. Our focus lies in identifying the optimal continuous cooling transformation that yields the desired mechanical properties of steel.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Modeling of Hardness Values and Phase Fraction Percentages in Micro-Alloyed Steel during Heat Treatment Using AI

Bassi,

Bodas,

Hasan

et al. 2023

Metals

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In this work, we have proposed an AI-based model that can simultaneously predict the hardness and phase fraction percentages of micro-alloyed steel with a predefined chemical composition and thermomechanical processing conditions. Specifically, the model uses a feed-forward neural network enhanced by the ensemble method. The model has been trained on experimental data derived from continuous cooling transformation (CCT) diagrams of 39 different steels. The inputs to the model include a cooling profile defined by a set of time-temperature values and the chemical composition of the steel. Sensitivity analysis was performed on the validated model to understand the impact of key input variables, including individual alloys and the thermomechanical processing conditions. This analysis, which measures the variability in output in response to changes in a specific input variable, showed excellent agreement with experimental data and the trends in the literature. Thus, our model not only predicts steel properties under varied cooling conditions but also aligns with existing theoretical knowledge and experimental data.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive Modeling of Hardness Values and Phase Fraction Percentages in Micro-Alloyed Steel during Heat Treatment Using AI

Bassi,

Bodas,

Hasan

et al. 2023

Metals

View full text Add to dashboard Cite

show abstract

“…Artificial neural network is a technique that involves database training to predict property-parameter (output) evolutions, more quickly [23,24]. This section presents the database construction, implementation protocol and a set of predicted results for coating deposition efficiency [25].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction and Analysis of Deposition Efficiency of Plasma Spray Coating Using Artificial Intelligence Method

Behera¹,

Mishra²

2012

OJCM

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Modern industrial technologies call for the development of novel materials with improved surface properties, lower costs and environmentally suitable processes. Plasma spray coating process has become a subject of intense research which attempts to create functional layers on the surface is obviously the most economical way to provide high performance to machinery and industrial equipments. The present work aims at developing and studying the industrial wastes (Flay-ash, Quartz and illmenite composite mixture) as the coating material, which is to be deposited on Mild Steel and Copper substrates. To study and evaluate Coating deposition efficiency, artificial neural network analysis (ANN) technique is used. By this quality control technique, it is sufficient to describe approximation complex of inter-relationships of operating parameters in atmospheric plasma spray process. ANN technique helps in saving time and resources for experimental trials. The aim of this work is to outline a procedure for selecting an appropriate input vectors in ANN coating efficiency models, based on statistical pre-processing of the experimental data set. This methodology can provide deep understanding of various co-relationships across multiple scales of length and time, which could be essential for improvement of product and process performance. The deposition efficiency of coatings has a strong dependence on input power level, particle size of the feed material, powder feed rate and torch to substrate distance. ANN experimental results indicate that the projection network has good generalization capability to optimize the deposition efficiency, when an appropriate size of training set and network is utilized.

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The Neural Network Estimator for Mechanical Property of Rolled Steel Bar

Cited by 2 publications

References 10 publications

Predictive Modeling of Hardness Values and Phase Fraction Percentages in Micro-Alloyed Steel during Heat Treatment Using AI

Predictive Modeling of Hardness Values and Phase Fraction Percentages in Micro-Alloyed Steel during Heat Treatment Using AI

Prediction and Analysis of Deposition Efficiency of Plasma Spray Coating Using Artificial Intelligence Method

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