Multiscale modeling of tempering of AISI H13 hot-work tool steel – Part 1: Prediction of microstructure evolution and coupling with mechanical properties

Eser, Atılım; Broeckmann, Christoph; Şimşir, Caner

doi:10.1016/j.commatsci.2015.11.020

Cited by 40 publications

(13 citation statements)

References 26 publications

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“…Increased demands mean strengthened requirements in terms of the tool material properties, including temperature resistance, strength, shock and fatigue resistance, impact and sliding wear resistance, etc. Ductility and fracture toughness are among the main tool properties essential for the majority of forming applications and the influencing of tool resistance [13,14]. Properties of the tool core material, i.e., tool steel, depend on its chemical composition and production process, but primarily on the heat treatment parameters.…”

Section: Introductionmentioning

confidence: 99%

Properties of Tool Steels and Their Importance When Used in a Coated System

Podgornik¹,

Sedlaček²,

Žužek³

et al. 2020

Coatings

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The introduction of new light-weight high-strength materials, which are difficult to form, increases demands on tool properties, including load-carrying capacity and wear resistance. Tool properties can be improved by the deposition of hard coatings but proper combination and optimization of the substrate properties are required to prepare the tool for coating application. The aim of this paper is to elaborate on tool steel substrate properties correlations, including hardness, fracture toughness, strength and surface quality and how these substrate properties influence on the coating performance. Results show that hardness of the steel substrate is the most influential parameter for abrasive wear resistance and load-carrying capacity, which is true for different types of hard coatings. However, high hardness should also be accompanied by sufficient fracture toughness, especially when it comes to very hard and brittle coatings, thus providing a combination of high load-carrying capacity, good fatigue properties and superior resistance against impact wear. Duplex treatment and formation of a compound layer during nitriding can be used as an additional support interlayer, but its brittleness may result in accelerated coating cracking and spallation if not supported by sufficient core hardness. In terms of galling resistance, even for coated surfaces substrate roughness and topography have major influence when it comes to hard ceramic coatings, with reduced substrate roughness and coating post-polishing providing up to two times better galling resistance.

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Section: Introductionmentioning

confidence: 99%

Properties of Tool Steels and Their Importance When Used in a Coated System

Podgornik¹,

Sedlaček²,

Žužek³

et al. 2020

Coatings

View full text Add to dashboard Cite

show abstract

“…Since 1950s, physical metallurgy principle model has been developed by some researchers to calculate the mechanical properties of steels [4][5][6][7][8][9]. However, the shortcoming of the physical metallurgy principle model is that establishing a model needs to carry out a massive amount of destructive experiments to determine the empirical parameters, which requires substantial time and high cost.…”

Section: Introductionmentioning

confidence: 99%

Elevating Prediction Performance for Mechanical Properties of Hot-Rolled Strips by Using Semi-Supervised Regression and Deep Learning

Yang

Cao

et al. 2020

IEEE Access

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In the present work, to solve the problem of the lacking enough labeled training data for deep learning, a safe semi-supervised regression supporting Bayesian optimization deep neural network (SAFER-BODNN) model was proposed to establish mechanical property prediction model of hot-rolled strips. The Pearson correlation coefficient was applied to reduce the data dimension. The safe semi-supervised regression was implemented to add the pseudo labels to the unlabeled data for training dataset expansion. The deep neural network was trained with Bayesian optimization to determine the optimal hyper-parameters of the network. The results show that the SAFER-BODNN model achieves good accuracy for mechanical property prediction of hot-rolled strips with R of 0.9610 for yield strength, 0.9682 for tensile strength, and 0.8619 for elongation, respectively. Compared with the deep neural network trained on the labeled dataset, the SAFER-BODNN model obtains stable smaller predicted errors. Among all the variables, C content and Mn content have large influence on the yield strength and tensile strength, coiling temperature has the largest influence on the elongation. The investigation makes full use of unlabeled data to elevate the accuracy of the deep neural network, and also provides a way for deep learning modeling when the data are insufficient.

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“…In high-alloyed hot working steels like SAE H13, the alloying elements provide enhanced wear and corrosion resistance. The microstructure of this steel group shows a secondary hardness maximum by increasing the tempering temperature, where secondary precipitates are formed [ 12 , 13 ]. The steels cited are all Cr- or Cr-Mo-alloyed.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of the Tempering Behavior of Different Martensitic Steels by Means of In-Situ Diffractometry and Dilatometry

et al. 2020

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Martensitic steels are tempered to increase the toughness of the metastable martensite, which is brittle in the as-quenched state, and to achieve a more stable microstructure. During the tempering of steels, several particular overlapping effects can arise. Classical dilatometric investigations can only detect effects by monitoring the integral length change of the sample. Additional in-situ diffractometry allowed a differentiation of the individual effects such as transformation of retained austenite and formation of cementite during tempering. Additionally, the lattice parameters of martensite and therefrom the tetragonality was analyzed. Two low-alloy steels with carbon contents of 0.4 and 1.0 wt.% and a high-alloy 5Cr-1Mo-steel with 0.4 wt.% carbon were investigated by dilatometry and in-situ diffractometry. In this paper, microstructural effects during tempering of the investigated steels are discussed by a comparative study of dilatometric and diffractometric experiments. The influence of the chemical composition on the tempering behavior is illustrated by comparing the determined effects of the three steels. The kinetics of tempering is similar for the low-alloy steels and shifted to much higher temperatures for the high-alloy steel. During tempering, the tetragonality of martensite in the steel with 1.0 wt% carbon shifts towards a low carbon behavior, as in the steels with 0.4 wt.% carbon.

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Multiscale modeling of tempering of AISI H13 hot-work tool steel – Part 1: Prediction of microstructure evolution and coupling with mechanical properties

Cited by 40 publications

References 26 publications

Properties of Tool Steels and Their Importance When Used in a Coated System

Properties of Tool Steels and Their Importance When Used in a Coated System

Elevating Prediction Performance for Mechanical Properties of Hot-Rolled Strips by Using Semi-Supervised Regression and Deep Learning

Comparative Study of the Tempering Behavior of Different Martensitic Steels by Means of In-Situ Diffractometry and Dilatometry

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