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10 pages, 1337 KiB

Open AccessArticle

Modeling the Chemical Composition of Ferritic Stainless Steels with the Use of Artificial Neural Networks

by Rafał Honysz

Metals 2021, 11(5), 724; https://doi.org/10.3390/met11050724 - 28 Apr 2021

Cited by 20 | Viewed by 2067

Abstract

The aim of this paper is an attempt to answer the question of whether, on the basis of the values of the mechanical properties of ferritic stainless steels, it is possible to predict the chemical concentration of carbon and nine of the other [...] Read more.

The aim of this paper is an attempt to answer the question of whether, on the basis of the values of the mechanical properties of ferritic stainless steels, it is possible to predict the chemical concentration of carbon and nine of the other most common alloying elements in these steels. The author believes that the relationships between the properties are more complicated and depend on a greater number of factors, such as heat and mechanical treatment conditions, but in this paper, they were not taken into account due to the uniform treatment of the tested steels. The modeling results proved to be very promising and indicate that for some elements, this is possible with high accuracy. Artificial neural networks with radial basis functions (RBF), multilayer perceptron with one and two hidden layers (MLP) and generalized regression neural networks (GRNN) were used for modeling. In order to minimize the manufacturing cost of products, developed artificial neural networks can be used in industry. They may also simplify the selection of materials if the engineer has to correctly select chemical components and appropriate plastic and/or heat treatments of stainless steel with the necessary mechanical properties. Full article

(This article belongs to the Special Issue Application of Artificial Neural Networks in Studies of Steels and Alloys)

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14 pages, 11811 KiB

Open AccessArticle

Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance

by Sunčana Smokvina Hanza, Tea Marohnić, Dario Iljkić and Robert Basan

Metals 2021, 11(5), 714; https://doi.org/10.3390/met11050714 - 27 Apr 2021

Cited by 16 | Viewed by 2450

Abstract

Successful prediction of the relevant mechanical properties of steels is of great importance to materials engineering. The aim of this research is to investigate the possibility of reducing the complexity of artificial neural networks-based prediction of total hardness of hypoeutectoid, low-alloy steels based [...] Read more.

Successful prediction of the relevant mechanical properties of steels is of great importance to materials engineering. The aim of this research is to investigate the possibility of reducing the complexity of artificial neural networks-based prediction of total hardness of hypoeutectoid, low-alloy steels based on chemical composition, by introducing the specific Jominy distance as a new input variable. For prediction of total hardness after continuous cooling of steel (output variable), ANNs were developed for different combinations of inputs. Input variables for the first configuration of ANNs were the main alloying elements (C, Si, Mn, Cr, Mo, Ni), the austenitizing temperature, the austenitizing time, and the cooling time to 500 °C, while in the second configuration alloying elements were substituted by the specific Jominy distance. Comparing the results of total hardness prediction, it can be seen that the ANN using the specific Jominy distance as input variable (r_unseen = 0.873, RMSE_unseen = 67, MAPE = 14.8%) is almost as successful as ANN using main alloying elements (r_unseen = 0.940, RMSE_unseen = 46, MAPE = 10.7%). The research results indicate that the prediction of total hardness of steel can be successfully performed only based on four input variables: the austenitizing temperature, the austenitizing time, the cooling time to 500 °C, and the specific Jominy distance. Full article

(This article belongs to the Special Issue Application of Artificial Neural Networks in Studies of Steels and Alloys)

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14 pages, 4077 KiB

Open AccessArticle

Optimal Design of Hot-Dip Galvanized DP Steels via Artificial Neural Networks and Multi-Objective Genetic Optimization

by Edgar O. Reséndiz-Flores, Gerardo Altamirano-Guerrero, Patricia S. Costa, Antonio E. Salas-Reyes, Armando Salinas-Rodríguez and Frank Goodwin

Metals 2021, 11(4), 578; https://doi.org/10.3390/met11040578 - 1 Apr 2021

Cited by 6 | Viewed by 2243

Abstract

This modeling and optimization study applies a non-linear back-propagation artificial neural network, commonly denoted as BPNN, to model the most important mechanical properties such as yield strength (YS), ultimate tensile strength (UTS) and elongation at fracture (EL) [...] Read more.

This modeling and optimization study applies a non-linear back-propagation artificial neural network, commonly denoted as BPNN, to model the most important mechanical properties such as yield strength (YS), ultimate tensile strength (UTS) and elongation at fracture (EL) during the experimental processing of hot-dip galvanized dual-phase (GDP) steels. Once the non-linear BPNN is properly trained, the most important variables of the continuous galvanizing process, including initial/first cooling rate (CR1), holding time at the galvanizing temperature of 460 °C (tg) and the final/second cooling rate (CR2), are obtained in an optimal way using an evolutionary approach. The experimental development of GDP steels in continuous processing lines with outstanding mechanical properties (550 < YS < 750 MPa, 1100 MPa < UTS and 10% < EL) is possible by using a combined hybrid approach based in BPNN and multi-objective genetic algorithm (GA). The proposed computational method is applied to the specific design of an actual manufacturing process for the first time. Full article

(This article belongs to the Special Issue Application of Artificial Neural Networks in Studies of Steels and Alloys)

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12 pages, 2823 KiB

Open AccessFeature PaperArticle

Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks

by Roman Hartl, Andreas Bachmann, Jan Bernd Habedank, Thomas Semm and Michael F. Zaeh

Metals 2021, 11(4), 535; https://doi.org/10.3390/met11040535 - 25 Mar 2021

Cited by 19 | Viewed by 3158

Abstract

Preliminary studies have shown the superiority of convolutional neural networks (CNNs) compared to other network architectures for determining the surface quality of friction stir welds. In this paper, CNNs were employed to detect cavities inside friction stir welds by evaluating inline measured process [...] Read more.

Preliminary studies have shown the superiority of convolutional neural networks (CNNs) compared to other network architectures for determining the surface quality of friction stir welds. In this paper, CNNs were employed to detect cavities inside friction stir welds by evaluating inline measured process data. The aim was to determine whether CNNs are suitable for identifying surface defects exclusively, or if the approach is transferable to internal weld defects. For this purpose, 120 welds were produced and examined by ultrasonic testing, which was the basis for labeling the data as “good” or “defective.” Different types of artificial neural network were tested for predicting the placement of the welds into the defined classes. It was found that the way of labeling the data is significant for the accuracy achievable. When the complete welds were uniformly labeled as “good” or “defective,” an accuracy of 98.5% was achieved by a CNN, which was a significant improvement compared to the state of the art. When the welds were labeled segment-wise, an accuracy of 79.2% was obtained by using a CNN, showing that a segment-wise prediction of the cavities is also possible. The results confirm that CNNs are well suited for process monitoring in friction stir welding and their application enables the identification of various defect types. Full article

(This article belongs to the Special Issue Application of Artificial Neural Networks in Studies of Steels and Alloys)

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23 pages, 8945 KiB

Open AccessArticle

Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks

by Shuai Wang, Xiaojun Xia, Lanqing Ye and Binbin Yang

Metals 2021, 11(3), 388; https://doi.org/10.3390/met11030388 - 26 Feb 2021

Cited by 89 | Viewed by 9575

Abstract

Automatic detection of steel surface defects is very important for product quality control in the steel industry. However, the traditional method cannot be well applied in the production line, because of its low accuracy and slow running speed. The current, popular algorithm (based [...] Read more.

Automatic detection of steel surface defects is very important for product quality control in the steel industry. However, the traditional method cannot be well applied in the production line, because of its low accuracy and slow running speed. The current, popular algorithm (based on deep learning) also has the problem of low accuracy, and there is still a lot of room for improvement. This paper proposes a method combining improved ResNet50 and enhanced faster region convolutional neural networks (faster R-CNN) to reduce the average running time and improve the accuracy. Firstly, the image input into the improved ResNet50 model, which add the deformable revolution network (DCN) and improved cutout to classify the sample with defects and without defects. If the probability of having a defect is less than 0.3, the algorithm directly outputs the sample without defects. Otherwise, the samples are further input into the improved faster R-CNN, which adds spatial pyramid pooling (SPP), enhanced feature pyramid networks (FPN), and matrix NMS. The final output is the location and classification of the defect in the sample or without defect in the sample. By analyzing the data set obtained in the real factory environment, the accuracy of this method can reach 98.2%. At the same time, the average running time is faster than other models. Full article

(This article belongs to the Special Issue Application of Artificial Neural Networks in Studies of Steels and Alloys)

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20 pages, 5401 KiB

Open AccessArticle

On the Zener–Hollomon Parameter, Multi-Layer Perceptron and Multivariate Polynomials in the Struggle for the Peak and Steady-State Description

by Petr Opěla, Petr Kawulok, Ivo Schindler, Rostislav Kawulok, Stanislav Rusz and Horymír Navrátil

Metals 2020, 10(11), 1413; https://doi.org/10.3390/met10111413 - 23 Oct 2020

Cited by 7 | Viewed by 2171

Abstract

Description of flow stress evolution, specifically an approximation of a set of flow curves acquired under a wide range of thermomechanical conditions, of various materials is often solved via so-called flow stress models. Some of these models are associated with a description of [...] Read more.

Description of flow stress evolution, specifically an approximation of a set of flow curves acquired under a wide range of thermomechanical conditions, of various materials is often solved via so-called flow stress models. Some of these models are associated with a description of significant flow-curve coordinates. It is clear, the more accurate the coordinates description, the more accurate the assembled model. In the presented research, Zener–Hollomon-based relations, multi-layer perceptron networks and multivariate polynomials are employed to describe the peak and steady-state coordinates of an Invar 36 flow curve dataset. Comparison of the utilized methods in the case of the studied alloy has showed that the suitable description is given by the multivariate polynomials although the Zener–Hollomon and perceptron networks also offer valuable results. Full article

(This article belongs to the Special Issue Application of Artificial Neural Networks in Studies of Steels and Alloys)

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Review

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16 pages, 1548 KiB

Open AccessFeature PaperReview

Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

by Wojciech Sitek and Jacek Trzaska

Metals 2021, 11(11), 1832; https://doi.org/10.3390/met11111832 - 15 Nov 2021

Cited by 11 | Viewed by 2219

Abstract

Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper [...] Read more.

Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented. Full article

(This article belongs to the Special Issue Application of Artificial Neural Networks in Studies of Steels and Alloys)

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21 pages, 1937 KiB

Open AccessReview

Preparation Methods for Graphene Metal and Polymer Based Composites for EMI Shielding Materials: State of the Art Review of the Conventional and Machine Learning Methods

by Saba Ayub, Beh Hoe Guan, Faiz Ahmad, Muhammad Faisal Javed, Amir Mosavi and Imre Felde

Metals 2021, 11(8), 1164; https://doi.org/10.3390/met11081164 - 22 Jul 2021

Cited by 26 | Viewed by 4082

Abstract

Advancement of novel electromagnetic inference (EMI) materials is essential in various industries. The purpose of this study is to present a state-of-the-art review on the methods used in the formation of graphene-, metal- and polymer-based composite EMI materials. The study indicates that in [...] Read more.

Advancement of novel electromagnetic inference (EMI) materials is essential in various industries. The purpose of this study is to present a state-of-the-art review on the methods used in the formation of graphene-, metal- and polymer-based composite EMI materials. The study indicates that in graphene- and metal-based composites, the utilization of alternating deposition method provides the highest shielding effectiveness. However, in polymer-based composite, the utilization of chemical vapor deposition method showed the highest shielding effectiveness. Furthermore, this review reveals that there is a gap in the literature in terms of the application of artificial intelligence and machine learning methods. The results further reveal that within the past half-decade machine learning methods, including artificial neural networks, have brought significant improvement for modelling EMI materials. We identified a research trend in the direction of using advanced forms of machine learning for comparative analysis, research and development employing hybrid and ensemble machine learning methods to deliver higher performance. Full article

(This article belongs to the Special Issue Application of Artificial Neural Networks in Studies of Steels and Alloys)

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