Pavlo Maruschak

Bio: Pavlo Maruschak is an academic researcher from National Technical University. The author has contributed to research in topics: Deformation (engineering) & Fracture toughness. The author has an hindex of 17, co-authored 213 publications receiving 1227 citations. Previous affiliations of Pavlo Maruschak include Ivano-Frankivsk National Technical University of Oil and Gas & Ternopil Ivan Pul'uj National Technical University.

Steel Surface Defect Classification Using Deep Residual Neural Network

Abstract: An automated method for detecting and classifying three classes of surface defects in rolled metal has been developed, which allows for conducting defectoscopy with specified parameters of efficiency and speed. The possibility of using the residual neural networks for classifying defects has been investigated. The classifier based on the ResNet50 neural network is accepted as a basis. The model allows classifying images of flat surfaces with damage of three classes with the general accuracy of 96.91% based on the test data. The use of ResNet50 is shown to provide excellent recognition, high speed, and accuracy, which makes it an effective tool for detecting defects on metal surfaces.

Algorithm for the assessment of heavyweight and oversize cargo transportation routes

Abstract: The improvement of transportation systems and technologic equipment leads to changing technical capabilities of this equipment. With the development of technologies, industrial development is also inevitable, resulting in correspondingly increasing need of transportation of HeavyWeight and OverSize (HW/OS) cargo. The application of a systematic approach in HW/OS cargo transportation processes allows reducing costs of delivery of such a cargo several times, which leads to a dramatic change of economic development and investment attraction conditions. Thus creating a system of criteria for the selection and assessment of HW/OS routes, which would allow selecting the most appropriate route of transportation in terms of cost and time, is expedient for this reason. The algorithm for the assessment of HW/OS cargo transportation routes will be drawn up in this article. This algorithm enables an objective evaluation of HW/OS transportation processes comparing different modes of transport, route segments, cargo transportation and cargo handling technology, and it can be practically applied to any territory.

Degradation of the main gas pipeline material and mechanisms of its fracture

Abstract: The effect of the in-service scattered damage in the pipe wall metal is evaluated, and the effect of hydrogen absorbed by metal on the variation of the structure and mechanical properties of the main gas pipeline after a long-term operation is found. The main regularities in the graded nature of the static deformation process and the effect of hydrogenation on the scattered damage and fracture accumulation are found.

Створення порталу інформаційно-довідкових ресурсів міністерства освіти і науки україни

Abstract: Premises of creation of Internet portal designed to provide access to participants of educational and scientific process for the joint creation, consolidation, concentration and rapid spreading of educational and scientific information resources in its own depository are considered. CMS-based portal content management systems’ potentiality is investigated. Architecture for Internet portal of MES of Ukraine’s information resources is offered.

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Abstract: Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE).

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

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 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.

A search for technologies implementing a high fighting efficiency of the multilayered elements of military equipment

Abstract: Technologies for the production of multilayered spherical bottoms and cylindrical shells of quasi-layered material and compaction of the multilayered wall of military equipment elements are developed. The need for these studies is associated with increasing survivability and eliminating fragmentation effects of multilayered structures on manpower and equipment that use pressure vessels (submarines, flamethrower vessels, elements of armored vehicles).Operating conditions for the most efficient use of structures made of layered structural materials are presented. Their advantages in operation and production are described. The highest efficiency is achieved with firm adhesion of structural layers or with the use of quasi-layered materials. The solution to this problem is possible using the energy of high explosives. It is experimentally found that explosive loading leads to an increase in toughness and ballistic resistance of treated materials. Analysis of various methods of multilayered wall compaction is carried out and the most effective technologies are proposed.As a result of the research, process parameters, requirements for process equipment are established.The importance of the presented studies is associated with increasing the fighting efficiency, survivability of military equipment and personnel