Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
3.5
Journals
Processes
Special Issues
Digital Research and Development of Materials and Processes
share announcement

Digital Research and Development of Materials and Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Materials Processes".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 8326

Special Issue Editors

Dr. Wei Lv

E-Mail Website
Guest Editor
Institute of Energy Power Innovation, North China Electric Power University, Beijing 102206, China
Interests: energy storage; aqueous battery; hydrogen energy; hydrogen storage material
Prof. Dr. Wanqiang Liu

E-Mail Website
Guest Editor
School of Materials Science and Engineering, Changchun University of Science and Technology, Changchun 130022, China
Interests: Li-ion battery; energy storage; aqueous battery; hydrogen energy
Dr. Li Yang

E-Mail Website
Guest Editor
Material Digital R&D Center, China Iron and Steel Research Institute Group, Beijing 100081, China
Interests: material and process design; CALPHAD; phase field method; FEM; ICME; process simulation; steels for hydrogen storage
Dr. Weisen Zheng

E-Mail Website
Guest Editor
School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China
Interests: computational thermodynamics; computational kinetics, integrated computation materials engineering (ICME); high-throughput calculations; high strength steels; materials design
Dr. Feng Wang

E-Mail Website
Guest Editor
Department of Materials, Guilin University of Electronic Technology, Guilin, China
Interests: cathode materials for lithium-ion batteries; interface modification engineering of non-metal/metal coatings; large-scale rapid supply technology of hydrogen and its isotopes

Special Issue Information

Dear Colleagues,

Digital research and development technology has become an important tool for the design of materials and processes, and it can significantly reduce the time and costs of research and discovery compared with the traditional trial and error method. It combines the advanced idea of integrated computational materials engineering (ICME) and material genome initiative (MGI), and applies those digital technologies into material and process research, e.g., ICME platform, multiscale modelling, material database, machine learning, artificial intelligence (AI), 3D printing, high-throughput experiment, etc. These digital research and development approaches could enable an improved understanding and prediction of the microstructure, mechanical properties and service behavior of materials.

This Special Issue, entitled “Digital Research and Development of Materials and Processes”, aims to collect the most recent research on digital technologies applied in the design of materials and processes, and it welcomes original research papers, reviews, and case studies on topics including, but not limited to, the following:

  • Integrated computational materials engineering (ICME);
  • Material genome initiative (MGI);
  • Multiscale modeling of materials and processes;
  • Material database;
  • Machine learning;
  • 3D printing;
  • High-throughput experiment;
  • Artificial intelligence;
  • Digital twin.

We welcome contributions from researchers in various fields, such as materials science, mechanical engineering, physics, informatic, and applied mathematics, among others. We hope this Special Issue will provide a valuable platform for researchers to share their findings and insights, especially regarding frontier digital technologies of materials and processes, and promote interdisciplinary collaborations to solve more engineering problems.

Dr. Wei Lv
Prof. Dr. Wanqiang Liu
Dr. Li Yang
Dr. Weisen Zheng
Dr. Feng Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • integrated computational materials engineering (ICME)
  • material genome initiative (MGI)
  • multiscale modeling of materials and processes
  • material database
  • machine learning
  • 3D printing
  • high-throughput experiment
  • artificial intelligence
  • digital twin

Published Papers (10 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 829 KiB  
Article
Texture and Twinning Evolution of Cold-Rolled Industrial Pure Zirconium
by Yuan Liu, Yiming Li, Weimin Mao, Huiyi Bai, Qi Fang, Yunping Ji and Huiping Ren
Processes 2024, 12(5), 948; https://doi.org/10.3390/pr12050948 - 7 May 2024
Viewed by 174
Abstract
Industrial pure zirconium plays an essential role as a structural material in the nuclear energy sector. Understanding the deformation mechanisms is crucial for effectively managing the plasticity and texture evolution of industrial pure zirconium. In the present study, the texture and microstructure evolution [...] Read more.
Industrial pure zirconium plays an essential role as a structural material in the nuclear energy sector. Understanding the deformation mechanisms is crucial for effectively managing the plasticity and texture evolution of industrial pure zirconium. In the present study, the texture and microstructure evolution of industrial pure zirconium during the cold-rolling process have been characterized by XRD, EBSD, and TEM. The influences of various twins on texture evolution have also been simulated by the reaction stress model. The effects of slip and twinning on the deformation behavior and texture evolution have been discussed based on crystallographic and experimental considerations. Cold rolling yields a typical bimodal texture, resulting in the preferential <2110>//RD orientation. The activation of the deformation mechanisms during cold rolling follows the sequential trend of slip, twinning, local slip. Experimental characterization and reaction stress simulation illustrate that T1 twins dominate in the early stage, whereas C2 twins develop at the later stage of the cold-rolling process. Twinning, especially the T1 twin, contributes to the formation of the {0001}<1010> orientation. Full article
(This article belongs to the Special Issue Digital Research and Development of Materials and Processes)
16 pages, 22589 KiB  
Article
Mathematical Model of Graphene Yield in Ultrasonic Preparation
by Jinquan Yi, Baoshan Gu, Chengling Kan, Xudong Lv, Zhifeng Wang, Peiyan Yang and Haoqi Zhao
Processes 2024, 12(4), 674; https://doi.org/10.3390/pr12040674 - 27 Mar 2024
Viewed by 587
Abstract
Based on the Box–Behnken design (BBD) methodology, an experimental study of the preparation of graphene using ultrasonication was conducted. The yield of graphene served as the response variable, with ultrasonication process time, ultrasonic power, the graphite initial weight, and their interactive effects acting [...] Read more.
Based on the Box–Behnken design (BBD) methodology, an experimental study of the preparation of graphene using ultrasonication was conducted. The yield of graphene served as the response variable, with ultrasonication process time, ultrasonic power, the graphite initial weight, and their interactive effects acting as the independent variables influencing the yield. A multivariate nonlinear regression model was established to describe the ultrasonic production of graphene. Verification of the experiments suggests that the developed multivariate nonlinear regression model is highly significant and provides a good fit, enabling an effective prediction of the graphene yield. The yield of graphene was found to increase with higher ultrasonic power but decrease with longer ultrasonication times and the initial weight of the graphite. The optimal process parameters according to the regression model were determined to be 30 min of ultrasonication time, an ultrasonic power of 1500 W, and a graphite initial weight of 0.5 g. Under these conditions, the yield of graphene reached 31.6%, with a prediction error of 2.8% relative to the actual value. Furthermore, the results were corroborated with the aid of scanning electron microscopy (SEM), Raman spectroscopy, and transmission electron microscopy (TEM). It was observed that under constant ultrasonic power and graphite initial weight, a reduction in the ultrasonication processing time led to an increase in the thickness of the graphene. Continuing to increase the ultrasonication time beyond 30 min did not decrease the thickness of the graphene but rather reduced its lateral size. Decreasing the ultrasonic power resulted in thicker graphene, and even with an extended ultrasonication time, the quality of the graphene was inferior compared to that produced under the optimal processing parameters. Full article
(This article belongs to the Special Issue Digital Research and Development of Materials and Processes)
►▼ Show Figures

Figure 1

20 pages, 9931 KiB  
Article
Numerical Simulation of the Hydrogen-Based Directly Reduced Iron Melting Process
by Xiaoping Lin, Bing Ni and Fangqin Shangguan
Processes 2024, 12(3), 537; https://doi.org/10.3390/pr12030537 - 8 Mar 2024
Viewed by 629
Abstract
In the context of carbon reduction and emission reduction, the new process of electric arc furnace (EAF) steelmaking based on direct hydrogen reduction is an important potential method for the green and sustainable development of the steel industry. Within an electric furnace for [...] Read more.
In the context of carbon reduction and emission reduction, the new process of electric arc furnace (EAF) steelmaking based on direct hydrogen reduction is an important potential method for the green and sustainable development of the steel industry. Within an electric furnace for the hydrogen-based direct reduction of iron, after hydrogen-based directly reduced iron (HDRI) is produced through a shaft furnace, HDRI is melted or smelted in an EAF to form final products such as high-purity iron or high-end special steel. As smelting proceeds in the electric furnace, it is easy for pieces of HDRI to bond to each other and become larger pieces; they may even form an “iceberg”, and this phenomenon may then worsen the smelting working conditions. Therefore, the melting of HDRI is the key to affecting the smelting cycle and energy consumption of EAFs. In this study, based on the basic characteristics of HDRI, we established an HDRI melting model using COMSOL Multiphysics 6.0 and studied the HDRI melting process, utilizing pellets with a radius of 8 mm. The results of our simulation show that the HDRI melting process can be divided into three different stages: generating a solidified steel layer, melting the solidified steel layer, and melting HDRI bodies. Moreover, multiple HDRI processes are prone to bonding in the melting process. Increasing the spacing between pieces of HDRI and increasing the preheating temperature used on the HDRI can effectively reduce the aforementioned bonding phenomenon. When the melting pool temperature is 1873 K, increasing the spacing of HDRI to 10 mm and increasing the initial HDRI temperature to 973 K was shown to effectively reduce or eliminate the bonding phenomenon among pieces of HDRI. In addition, with the increase in the melting pool temperature, the time required for melting within the three stages of the HDRI melting process shortened, and the melting speed was accelerated. With the increase in the temperature used to preheat the HDRI, the duration of the solidified steel layer’s existence was also shortened, but this had no significant impact on the time required for the complete melting of HDRI. This study provides a theoretical basis for the optimization of the HDRI process within EAFs. Full article
(This article belongs to the Special Issue Digital Research and Development of Materials and Processes)
►▼ Show Figures

Figure 1

17 pages, 4196 KiB  
Article
The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms
by Tao Pan, Chengmin Song, Zhiyu Gao, Tian Xia and Tianqi Wang
Processes 2024, 12(3), 441; https://doi.org/10.3390/pr12030441 - 22 Feb 2024
Viewed by 735
Abstract
The constitutive model refers to the mapping relationship between the stress and deformation conditions (such as strain, strain rate, and temperature) after being loaded. In this work, the hot deformation behavior of a Ni-Cr-Mo steel was investigated by conducting isothermal compression tests using [...] Read more.
The constitutive model refers to the mapping relationship between the stress and deformation conditions (such as strain, strain rate, and temperature) after being loaded. In this work, the hot deformation behavior of a Ni-Cr-Mo steel was investigated by conducting isothermal compression tests using a Gleeble-3800 thermal simulator with deformation temperatures ranging from 800 °C to 1200 °C, strain rates ranging from 0.01 s−1 to 10 s−1, and deformations of 55%. To analyze the constitutive relation of the Ni-Cr-Mo steel at high temperatures, five machine learning algorithms were employed to predict the flow stress, namely, back-propagation artificial neural network (BP-ANN), Random Committee, Bagging, k-nearest neighbor (k-NN), and a library for support vector machines (libSVM). A comparative study between the experimental and the predicted results was performed. The results show that correlation coefficient (R), root mean square error (RMSE), mean absolute value error (MAE), mean square error (MSE), and average absolute relative error (AARE) obtained from the Random Committee on the testing set are 0.98897, 8.00808 MPa, 5.54244 MPa, 64.12927 MPa2 and 5.67135%, respectively, whereas the metrics obtained via other algorithms are all inferior to the Random Committee. It suggests that the Random Committee can predict the flow stress of the steel more effectively. Full article
(This article belongs to the Special Issue Digital Research and Development of Materials and Processes)
►▼ Show Figures

Figure 1

13 pages, 7657 KiB  
Article
Training Tricks for Steel Microstructure Segmentation with Deep Learning
by Xudong Ma and Yunhe Yu
Processes 2023, 11(12), 3298; https://doi.org/10.3390/pr11123298 - 26 Nov 2023
Cited by 2 | Viewed by 882
Abstract
Data augmentation and other training techniques have improved the performance of deep learning segmentation methods for steel materials. However, these methods often depend on the dataset and do not provide general principles for segmenting different microstructural morphologies. In this work, we collected 64 [...] Read more.
Data augmentation and other training techniques have improved the performance of deep learning segmentation methods for steel materials. However, these methods often depend on the dataset and do not provide general principles for segmenting different microstructural morphologies. In this work, we collected 64 granular carbide images (2048 × 1536 pixels) and 26 blocky ferrite images (2560 × 1756 pixels). We used five carbide images and two ferrite images and derived from them the test set to investigate the influence of frequently used training techniques on model segmentation accuracy. We propose a novel method for quickly building models that achieve the highest segmentation accuracy for a given dataset through combining multiple training techniques that enhance the segmentation quality. This method leads to a 1–2.5% increase in mIoU values. We applied the optimal models to the quantization of carbides. The results show that the optimal models achieve the smallest errors of 5.39 nm for the mean radius and 29 for the total number of carbides on the test set. The segmentation results are also more reasonable than those of traditional segmentation methods. Full article
(This article belongs to the Special Issue Digital Research and Development of Materials and Processes)
►▼ Show Figures

Figure 1

13 pages, 5883 KiB  
Article
A Deep Learning Labeling Method for Material Microstructure Image Segmentation
by Xuandong Wang, Hang Su, Nan Li, Ying Chen, Yilin Yang and Huimin Meng
Processes 2023, 11(12), 3272; https://doi.org/10.3390/pr11123272 - 22 Nov 2023
Viewed by 924
Abstract
In the existing deep learning modeling process for material microstructure image segmentation, the manual pixel labeling process is time-consuming and laborious. In order to achieve fast and high-accuracy modeling, this work proposes a convenient deep learning labeling method and a workflow for generating [...] Read more.
In the existing deep learning modeling process for material microstructure image segmentation, the manual pixel labeling process is time-consuming and laborious. In order to achieve fast and high-accuracy modeling, this work proposes a convenient deep learning labeling method and a workflow for generating a synthetic image data set. Firstly, a series of label templates was prepared by referring to the distribution of the material microstructure. Then, the typical textures of different microstructures were box-selected in the images to be segmented to form texture templates. The manual pixel labeling was simplified to the box-selection of the typical microstructure texture. Finally, a synthetic data set can be generated using the label and texture templates for further deep learning model training. Two image cases containing multiple types of microstructures were used to verify the labeling method and workflow. The results show that the pixel segmentation accuracy of the deep learning model for the test images reaches 95.92% and 95.40%, respectively. The modeling workflow can be completed within 20 min, and the labeling time that requires manual participation is within 10 min, significantly reducing the modeling time compared to traditional methods where the labeling process may take several hours. Full article
(This article belongs to the Special Issue Digital Research and Development of Materials and Processes)
►▼ Show Figures

Figure 1

11 pages, 3008 KiB  
Article
Impact of Alloy Elements on the Adsorption and Dissociation of Gaseous Hydrogen on Surfaces of Ni–Cr–Mo Steel
by Zhishan Mi, Xiuru Fan, Tong Li, Li Yang, Hang Su, Weidong Cai, Shuangquan Li and Guoxin Zhang
Processes 2023, 11(11), 3241; https://doi.org/10.3390/pr11113241 - 17 Nov 2023
Cited by 1 | Viewed by 756
Abstract
In this study, the effect of alloying elements on the adsorption and dissociation behaviors of hydrogen molecules on the bcc-Fe (001) surface has been investigated using first-principles calculations. H2 molecules can easily dissociate on the hollow site, and the dissociated hydrogen atoms [...] Read more.
In this study, the effect of alloying elements on the adsorption and dissociation behaviors of hydrogen molecules on the bcc-Fe (001) surface has been investigated using first-principles calculations. H2 molecules can easily dissociate on the hollow site, and the dissociated hydrogen atoms bond with the surrounding metal atoms. Doping Cr and Mo atoms on the surface would reduce the H2 molecule adsorption energy, which promotes the H2 molecule adsorption and dissociation. When only one or two Ni atoms doping on the surface, it improves the adsorption energies, which in turn can hinder the H2 molecule adsorption and dissociation. However, three or four Ni atoms doping on the surface is beneficial to the H2 molecule adsorption and dissociation. Thus, the nickel content in Ni–Cr–Mo steel should be reasonably controlled to improve the hydrogen embrittlement resistance of the steel. Full article
(This article belongs to the Special Issue Digital Research and Development of Materials and Processes)
►▼ Show Figures

Figure 1

18 pages, 4932 KiB  
Article
Thermodynamic Assessment of the Fe–Mn–Ni System and Diffusion Mobility of Its Face-Centered Cubic Phase
by Min Wang, Guodong Fan, Chengyang Ma, Yu Mei, Tao Luo, Weisen Zheng and Jiang Wang
Processes 2023, 11(11), 3216; https://doi.org/10.3390/pr11113216 - 13 Nov 2023
Viewed by 762
Abstract
Through extrapolation of updated binary descriptions, the Fe–Mn–Ni system was thermodynamically elucidated in a self-consistent way. The obtained thermodynamic description was confirmed to be reliable by measuring phase equilibria at relatively low temperatures. Our current thermodynamic evaluation can describe the phase stabilities over [...] Read more.
Through extrapolation of updated binary descriptions, the Fe–Mn–Ni system was thermodynamically elucidated in a self-consistent way. The obtained thermodynamic description was confirmed to be reliable by measuring phase equilibria at relatively low temperatures. Our current thermodynamic evaluation can describe the phase stabilities over a wide temperature range and provide a reliable thermodynamic factor for the diffusion mobility optimization. For the face-centered cubic (FCC) phase in the investigated alloy system, optimization of diffusion mobilities was accomplished with the CALPHAD method. Interdiffusivities were extracted based on the composition-distance profiles of diffusion couples investigated herein. Through comprehensive diffusion behavior comparisons, our proposed diffusion mobilities were confirmed. Full article
(This article belongs to the Special Issue Digital Research and Development of Materials and Processes)
►▼ Show Figures

Figure 1

17 pages, 4991 KiB  
Article
InterMat: A Blockchain-Based Materials Data Discovery and Sharing Infrastructure
by Changchang Wang, Hang Su, Linna Duan and Hao Li
Processes 2023, 11(11), 3168; https://doi.org/10.3390/pr11113168 - 7 Nov 2023
Viewed by 849
Abstract
Material research and development driven by data analysis necessitates a substantial volume of data. However, conventional material data sharing platforms encounter challenges in sharing and integrating data across multiple platforms. This article proposes a blockchain-based materials data discovery and sharing infrastructure—InterMat, which is [...] Read more.
Material research and development driven by data analysis necessitates a substantial volume of data. However, conventional material data sharing platforms encounter challenges in sharing and integrating data across multiple platforms. This article proposes a blockchain-based materials data discovery and sharing infrastructure—InterMat, which is a material big data management and sharing framework model integrating cloud platforms and blockchain. It could support the full lifecycle of materials data sharing, including data generation, management, discovery, sharing, traceability, and valuation. The architecture of the InterMat, its unique method of constructing a consortium chain, and the protocol for data discovery are presented in this paper. Additionally, the method for materials data identifier and blockchain certification is established, which allows for a unified identifier on the blockchain and cloud-based data addressing from various organizations. InterMat has data discovery algorithms for various materials to achieve the discovery of similar materials data from different nodes. Furthermore, we have designed some blockchain smart contracts for InterMat to encourage data sharing across nodes. These contracts include a proof smart contract that records data sharing activities, ensuring transparency and traceability in the materials data flow. The other contract is a value-estimating contract to encourage high-quality data sharing. Finally, this article introduces the application case of InterMat, using steel materials as an example to demonstrate its applications in data management, data discovery, data valuation, etc. This study successfully addresses various challenges associated with the cross-platform sharing of materials data, such as issues related to data discovery, data rights and control, and willingness to share. InterMat can assist material researchers in discovering and accessing more data, which would create a new ecology for sharing data. Full article
(This article belongs to the Special Issue Digital Research and Development of Materials and Processes)
►▼ Show Figures

Figure 1

18 pages, 4073 KiB  
Article
Machine Learning Aided Prediction of Glass-Forming Ability of Metallic Glass
by Chengcheng Liu, Xuandong Wang, Weidong Cai, Yazhou He and Hang Su
Processes 2023, 11(9), 2806; https://doi.org/10.3390/pr11092806 - 21 Sep 2023
Cited by 3 | Viewed by 1047
Abstract
The prediction of the glass-forming ability (GFA) of metallic glasses (MGs) can accelerate the efficiency of their development. In this paper, a dataset was constructed using experimental data collected from the literature and books, and a machine learning-based predictive model was established to [...] Read more.
The prediction of the glass-forming ability (GFA) of metallic glasses (MGs) can accelerate the efficiency of their development. In this paper, a dataset was constructed using experimental data collected from the literature and books, and a machine learning-based predictive model was established to predict the GFA. Firstly, a classification model based on the size of the critical diameter (Dmax) was established to determine whether an alloy system could form a glass state, with an accuracy rating of 0.98. Then, regression models were established to predict the crystallization temperature (Tx), glass transition temperature (Tg), and liquidus temperature (Tl) of MGs. The R2 of the prediction model obtained in the test set was greater than 0.89, which showed that the model had good prediction accuracy. The key features used by the regression models were analyzed using variance, correlation, embedding, recursive, and exhaustive methods to select the most important features. Furthermore, to improve the interpretability of the prediction model, feature importance, partial dependence plot (PDP), and individual conditional expectation (ICE) methods were used for visualization analysis, demonstrating how features affect the target variables. Finally, taking Zr-Cu-Ni-Al system MGs as an example, a prediction model was established using a genetic algorithm to optimize the alloy composition for high GFA in the compositional space, achieving the optimal design of alloy composition. Full article
(This article belongs to the Special Issue Digital Research and Development of Materials and Processes)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-10
Back to TopTop