Prediction of hardness at different points of Jominy specimen using quench factor analysis method

Lee

2019

Metals

Recently, in order to improve crashworthiness and achieve weight reduction of car body, a hot stamping process has been applied to the production of the part with tailored properties using tailored tool thermomechanical treatment. In the tailored tool thermomechanical treatment process, process parameters influence the mechanical properties of final product such as strength and hardness. Therefore, the prediction of hardness for final product is very important to manufacture hot-stamped part considering various process parameters. The purpose of this study is to propose a process window, which can predict hardness for various process parameters in tailored tool thermomechanical treatment. To determine the process window, finite element (FE) simulation coupled with quench factor analysis (QFA) has been performed for combinations of various process parameters. Subsequently, the process window was constructed through the training of artificial neural network (ANN) and experiment of tailored tool thermomechanical treatment for hat-shaped part was performed to verify effectiveness of hardness prediction. Then, the process parameters were determined from process window for hot stamping of the hat-shaped part with the required distribution of hardness. Hardness predicted by process window was in good agreement with measured one within 3.1% error in additional experiment. Therefore, the suggested process window can be used efficiently for hardness prediction and determination of process parameters in tailored tool thermomechanical treatment of hot-stamping parts.

Section: Methodology For Hardness Predictionmentioning

confidence: 99%

Construction of Process Window to Predict Hardness in Tailored Tool Thermomechanical Treatment and its Application

Lee

2019

Metals

“…In the same years, a method bases on quench factor analysis [27] was proposed to estimate the hardenability profile from simulated cooling curves, which shows good accuracy mostly for high hardness values. [28] In general, it can be observed that "traditional" numerical models estimating the hardenability curve show good performances only on a narrow range of steel grades considered for the tuning of their parameters, but their generalization properties are quite poor when applied for other grades, as the formulas correlating such parameters with the steel metallurgy are often empirical and hard to extend. Furthermore, often only some points of the profile are estimated with good accuracy, as the interactions among alloying elements are insufficiently addressed, also because they still are not fully known.…”

Section: Doi: 101002/srin202300374mentioning

confidence: 99%

A Deep Learning‐Based Approach to the Estimation of Jominy Profile of Medium‐Carbon Quench Hardenable Steels

Colla,

Vannucci,

Matino

et al. 2023

steel research int.

The possibility to estimate the Jominy profile of steel based on its chemical composition is of utmost importance and high practical relevance for industries, at enables a preliminary assessment of the suitability of a specific steel grade to a particular application or to the requirements of a customer, by saving time and resources as the Jominy end‐quench test is costly and time consuming. More importantly, an estimator can be used in steel grade design, by supporting the investigation of the most suitable chemistry to meet some given specifications. The paper proposes a novel approach to estimate the hardenability profile of medium Carbon quench hardenable steels, which exploits the potential of deep learning to correlate the steel metallurgy to the entire shape of the curve rather than to its single points, by thus being adaptable to a wide range of steel grades while providing very accurate estimates. Moreover, the proposed approach is suitable implement a transfer learning paradigm, as it can exploit the knowledge acquired by training on a specific dataset to adapt the model to different steel grades for which less data or data holding different features are available.This article is protected by copyright. All rights reserved.

“…A different approach from Yazdi et al [28] involves the quench factor analysis (QFA), a proven technique introduced in the early seventies and improved in 1993 by Rometsch et al [29], which correlates the cooling curves to metallurgical response. QFA is applied to estimate hardness from simulated cooling curves, by providing a good correlation between predicted and measured hardness.…”

Section: Data-driven Approaches To Forecast Steel Hardenabilitymentioning

confidence: 99%

Automatic steel grades design for Jominy profile achievement through neural networks and genetic algorithms

Vannucci

Colla

2021

Neural Comput & Applic

The paper proposes an approach to the design of the chemical composition of steel, which is based on neural networks and genetic algorithms and aims at achieving a desired hardenability behavior possibly matching other constraints related to the steel production. Hardenability is a mechanical feature of steel, which is extremely relevant for a wide range of steel applications and refers to the steel capability to improve its hardness following a heat treatment. In the proposed approach, a neural-network-based predictor of the so-called Jominy hardenability profile is exploited, and an optimization problem is formulated, where the optimization function allows taking into account both the desired accuracy in meeting the target Jominy profile and other constraint. The optimization is performed through genetic algorithms. Numerical results are presented and discussed, showing the efficiency of the proposed approach together with its flexibility and easy customization with respect to the user demands and production objectives.