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

Classification of steel surface defects in steel industry is essential for their detection and also fundamental for the analysis of causes that lead to damages. Timely detection of defects allows to reduce the frequency of their appearance in the final product. This paper considers the classifiers for the recognition of scratches, scrapes and abrasions on metal surfaces. Classifiers are based on the ResNet50 and ResNet152 deep residual neural network architecture. The proposed technique supports the recognition of defects in images and does this with high accuracy. The binary accuracy of the classification based on the test data is 97.14%. The influence of a number of training conditions on the accuracy metrics of the model have been studied. The augmentation conditions have been figured out to make the greatest contribution to improving the accuracy during training. The peculiarities of damages that cause difficulties in their recognition have been studied. The fields of neuron activation have been investigated in the convolutional layers of the model. Feature maps which developed in this case have been found to correspond to the location of the objects of interest. Erroneous cases of the classifier application have been considered. The peculiarities of damages that cause difficulties in their recognition have been studied.

Section: Grad-cam Class Activation Mapsmentioning

confidence: 99%

Recognition of Scratches and Abrasions on Metal Surfaces Using a Classifier Based on a Convolutional Neural Network

Konovalenko

Maruschak

Brevus³

et al. 2021

“…They compared the calculation results with the models developed by the logistic regression method and the SVM method. The results of modelling, aimed at detecting defects in the surface of products using deep neural networks, can be found, among others, in References [82][83][84][85][86][87].…”

Section: Deep Neural Networkmentioning

confidence: 99%

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

Sitek

Trzaska

2021

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.

“…For example, in [13] authors show how a hybrid strategy, which combines decision trees and ANNs, can be used for accurate and reliable prediction of ore crushing plate lifetimes. In [14], region convolutional neural networks are used for automatic detection of steel surface defects in product quality control. In [15], authors show the application of neural networks to a cyclic elastoplastic material as well as to a more complex thermo-viscoplastic steel solidification model.…”

Section: Introductionmentioning

confidence: 99%

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

Hanza

Marohnić

Iljkić

et al. 2021

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 (runseen = 0.873, RMSEunseen = 67, MAPE = 14.8%) is almost as successful as ANN using main alloying elements (runseen = 0.940, RMSEunseen = 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.