Pub Date : 2023-03-27DOI: 10.1109/SmartIndustryCon57312.2023.10110792
M. Gavrikov, Anna Y. Mezentseva, R. Sinetsky
Three interrelated heuristic techniques for setting the parameters of hidden Markov models for implementation in pattern recognition algorithms of stochastic processes recorded in the form of sequences of observations are proposed. The techniques make it possible to obtain working models for a small number of training implementations. The first two techniques include the stage of preliminary adjustment of the initial parameters of the model using a priori data and the training stage using the Baum-Welch algorithm. At both stages, an additional procedure for adjusting the model parameters is used, which makes it possible to eliminate numerical problems when they are implemented in recognition algorithms. The third technique implements the procedure of weighted averaging of the parameters of hidden Markov models obtained by the first two techniques. The results of experimental testing of the techniques are presented, illustrating the quality of the resulting hidden Markov models used in the algorithm for pattern recognition of stochastic processes.
{"title":"Heuristic Techniques for Constructing Hidden Markov Models of Stochastic Processes","authors":"M. Gavrikov, Anna Y. Mezentseva, R. Sinetsky","doi":"10.1109/SmartIndustryCon57312.2023.10110792","DOIUrl":"https://doi.org/10.1109/SmartIndustryCon57312.2023.10110792","url":null,"abstract":"Three interrelated heuristic techniques for setting the parameters of hidden Markov models for implementation in pattern recognition algorithms of stochastic processes recorded in the form of sequences of observations are proposed. The techniques make it possible to obtain working models for a small number of training implementations. The first two techniques include the stage of preliminary adjustment of the initial parameters of the model using a priori data and the training stage using the Baum-Welch algorithm. At both stages, an additional procedure for adjusting the model parameters is used, which makes it possible to eliminate numerical problems when they are implemented in recognition algorithms. The third technique implements the procedure of weighted averaging of the parameters of hidden Markov models obtained by the first two techniques. The results of experimental testing of the techniques are presented, illustrating the quality of the resulting hidden Markov models used in the algorithm for pattern recognition of stochastic processes.","PeriodicalId":157877,"journal":{"name":"2023 International Russian Smart Industry Conference (SmartIndustryCon)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116124276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1109/SmartIndustryCon57312.2023.10110779
A. Ragozin, A. D. Pletenkova
In order to improve the quality of forecasting and detect anomalies in signals recorded from the outputs of sensors of automated process control systems (APCS), it is proposed to use an artificial neural network - a predictive auto-encoder with a preliminary digital signal processing (DSP) unit. It is shown that the preliminary DSP of the input predicted signal, consisting of a parallel set (comb) of digital low-pass filters with finite impulse responses (FIR-LPF), leads to non-equilibrium accounting for the correlations of time samples of the input signal and increases the accuracy of the prediction result. It is also shown that the predictive autoencoder (PAE) considered in the paper, in addition to restoring the PAE output of the input signal, additionally generates predicted samples of the input signal at the output, which also increases the accuracy of the prediction result. If anomalies occur in the signals (for example, as a result of the impact of cyberattacks), during the operation of the APCS, structural changes will occur in the error signal of the generated forecast, as a result of the analysis of these structural changes in the forecast error, anomalies are detected in the observed APCS processes.
{"title":"Artificial Neural Network Predictive Autoencoder with Pre-Digital Signal Processing Unit","authors":"A. Ragozin, A. D. Pletenkova","doi":"10.1109/SmartIndustryCon57312.2023.10110779","DOIUrl":"https://doi.org/10.1109/SmartIndustryCon57312.2023.10110779","url":null,"abstract":"In order to improve the quality of forecasting and detect anomalies in signals recorded from the outputs of sensors of automated process control systems (APCS), it is proposed to use an artificial neural network - a predictive auto-encoder with a preliminary digital signal processing (DSP) unit. It is shown that the preliminary DSP of the input predicted signal, consisting of a parallel set (comb) of digital low-pass filters with finite impulse responses (FIR-LPF), leads to non-equilibrium accounting for the correlations of time samples of the input signal and increases the accuracy of the prediction result. It is also shown that the predictive autoencoder (PAE) considered in the paper, in addition to restoring the PAE output of the input signal, additionally generates predicted samples of the input signal at the output, which also increases the accuracy of the prediction result. If anomalies occur in the signals (for example, as a result of the impact of cyberattacks), during the operation of the APCS, structural changes will occur in the error signal of the generated forecast, as a result of the analysis of these structural changes in the forecast error, anomalies are detected in the observed APCS processes.","PeriodicalId":157877,"journal":{"name":"2023 International Russian Smart Industry Conference (SmartIndustryCon)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126703073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1109/SmartIndustryCon57312.2023.10110739
O. Ivashchuk, V. Fedorov, V. A. Berezhnoy
In the article, results of the development of methods, models, and hardware/software solutions for creating and actualization of digital clones of crops with the complex structure in the form of a complex of 3D models are presented that ensure the possibility to perform virtual biological experiments consisting in the cultivation of agricultural plants in the context of in vitro conditions (in a test glass) with evaluation and forecasting of parameters that have an effect for the further field setting and adaptation of plants in conditions of the outdoor bed, and prevailing natural environment and climatic factors. For the segmentation of the plant using methods of machine learning, a segmenting neuron net with the U2 –Net architecture was used. Good results of learning were obtained. A prototype of an automated installation has been developed that makes it possible to perform the complete cycle of the digital phenotyping and the analysis of obtained results based on digital clones of plants and implementation of the virtual process of in vitro cultivation. The obtained complex makes it possible to perform studies in that the microclimate inside of the test glass will not be disrupted; the data registration process is accelerated essentially; the human factor and the subjectivity are excluded during measurements. The knowledge base has been created that includes 792 units of 3D models for six crop species.
{"title":"Digital Clones at the Adaptable Control in the Agricultural Biotechnology","authors":"O. Ivashchuk, V. Fedorov, V. A. Berezhnoy","doi":"10.1109/SmartIndustryCon57312.2023.10110739","DOIUrl":"https://doi.org/10.1109/SmartIndustryCon57312.2023.10110739","url":null,"abstract":"In the article, results of the development of methods, models, and hardware/software solutions for creating and actualization of digital clones of crops with the complex structure in the form of a complex of 3D models are presented that ensure the possibility to perform virtual biological experiments consisting in the cultivation of agricultural plants in the context of in vitro conditions (in a test glass) with evaluation and forecasting of parameters that have an effect for the further field setting and adaptation of plants in conditions of the outdoor bed, and prevailing natural environment and climatic factors. For the segmentation of the plant using methods of machine learning, a segmenting neuron net with the U2 –Net architecture was used. Good results of learning were obtained. A prototype of an automated installation has been developed that makes it possible to perform the complete cycle of the digital phenotyping and the analysis of obtained results based on digital clones of plants and implementation of the virtual process of in vitro cultivation. The obtained complex makes it possible to perform studies in that the microclimate inside of the test glass will not be disrupted; the data registration process is accelerated essentially; the human factor and the subjectivity are excluded during measurements. The knowledge base has been created that includes 792 units of 3D models for six crop species.","PeriodicalId":157877,"journal":{"name":"2023 International Russian Smart Industry Conference (SmartIndustryCon)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123515867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1109/SmartIndustryCon57312.2023.10110791
A. Iskhakov, M. Mamchenko, S. P. Khripunov
Detection of anomalies in user behavior to improve authentication procedures (including on the web platforms) is still a relevant task in information security. These anomalies may be presented as data outliers in the standard logs with records with users’ actions on the web resources. To solve this problem, an algorithm for detecting anomalies in the behavior of users of web platforms based on machine learning is proposed. Standard audit logs and user browser fingerprints were used as a set of features to identify a user and/or his device. The algorithm detects anomalies (data outliers) in user behavior based on three classifiers: OneClassSVM, IsolationForest, and EllipticEnvelope. If anomalies are detected, one or more authentication factors are used for additional verification of the user. The proposed algorithm is aimed at increasing the security of the target web system based on the risk assessment of the threat of users’ abnormal behavior in near real time. The experiment showed that it is generally possible to use both IsolationForest and EllipticEnvelope as the main classifier. In particular, EllipticEnvelope has a higher average accuracy on large datasets of user activity (up to 1600 records per user). However, the use of IsolationForest gives the best value of maximum average accuracy, especially for small logs (up to 100 records per user).
{"title":"Enhanced User Authentication Algorithm Based on Behavioral Analytics in Web-Based Cyberphysical Systems","authors":"A. Iskhakov, M. Mamchenko, S. P. Khripunov","doi":"10.1109/SmartIndustryCon57312.2023.10110791","DOIUrl":"https://doi.org/10.1109/SmartIndustryCon57312.2023.10110791","url":null,"abstract":"Detection of anomalies in user behavior to improve authentication procedures (including on the web platforms) is still a relevant task in information security. These anomalies may be presented as data outliers in the standard logs with records with users’ actions on the web resources. To solve this problem, an algorithm for detecting anomalies in the behavior of users of web platforms based on machine learning is proposed. Standard audit logs and user browser fingerprints were used as a set of features to identify a user and/or his device. The algorithm detects anomalies (data outliers) in user behavior based on three classifiers: OneClassSVM, IsolationForest, and EllipticEnvelope. If anomalies are detected, one or more authentication factors are used for additional verification of the user. The proposed algorithm is aimed at increasing the security of the target web system based on the risk assessment of the threat of users’ abnormal behavior in near real time. The experiment showed that it is generally possible to use both IsolationForest and EllipticEnvelope as the main classifier. In particular, EllipticEnvelope has a higher average accuracy on large datasets of user activity (up to 1600 records per user). However, the use of IsolationForest gives the best value of maximum average accuracy, especially for small logs (up to 100 records per user).","PeriodicalId":157877,"journal":{"name":"2023 International Russian Smart Industry Conference (SmartIndustryCon)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123601774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1109/SmartIndustryCon57312.2023.10110754
A. N. Krasnov, M. Prakhova, Y. Kalashnik
The digitalization of the drilling process and the emergence of the intelligent drilling concept allow leading companies to transit from a single-well control system to the integrated operation control systems for several drilling sites at once by means of drilling control centers. These systems are distributed systems, the individual components of which are interconnected by communication channels, both wired and wireless. At most fields production drilling for hydrocarbons is conducted by the cluster method in which the mouths of directional wells are grouped closely at a common limited site where the drilling rig itself and a large number of additional facilities are located. Both the objects of one drilling site and several sites controlled from one center shall have a reliable communication between them. Recently, self-organizing wireless sensor networks (WSN) have become widespread. The efficiency of data transmission in such networks is determined by their topology and communication algorithms between individual nodes. The article considers a WSN model for several drilling sites with a single control center; it is used to estimate the impact of such parameters as the number of nodes, the density of their distribution, the node operating range and the area of the covered territory on the probability of network connectivity. Applying the proposed model helps select the optimal values of these parameters and improve the efficiency of drilling control from a single situational center.
{"title":"Organization of Wireless Sensor Network at Drilling Sites","authors":"A. N. Krasnov, M. Prakhova, Y. Kalashnik","doi":"10.1109/SmartIndustryCon57312.2023.10110754","DOIUrl":"https://doi.org/10.1109/SmartIndustryCon57312.2023.10110754","url":null,"abstract":"The digitalization of the drilling process and the emergence of the intelligent drilling concept allow leading companies to transit from a single-well control system to the integrated operation control systems for several drilling sites at once by means of drilling control centers. These systems are distributed systems, the individual components of which are interconnected by communication channels, both wired and wireless. At most fields production drilling for hydrocarbons is conducted by the cluster method in which the mouths of directional wells are grouped closely at a common limited site where the drilling rig itself and a large number of additional facilities are located. Both the objects of one drilling site and several sites controlled from one center shall have a reliable communication between them. Recently, self-organizing wireless sensor networks (WSN) have become widespread. The efficiency of data transmission in such networks is determined by their topology and communication algorithms between individual nodes. The article considers a WSN model for several drilling sites with a single control center; it is used to estimate the impact of such parameters as the number of nodes, the density of their distribution, the node operating range and the area of the covered territory on the probability of network connectivity. Applying the proposed model helps select the optimal values of these parameters and improve the efficiency of drilling control from a single situational center.","PeriodicalId":157877,"journal":{"name":"2023 International Russian Smart Industry Conference (SmartIndustryCon)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130449812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1109/SmartIndustryCon57312.2023.10110734
Konstantin Kulagin, Mansur Salikhov, R. Burnashev
This paper presents the implementation of an educational intelligent system with natural language processing based on fuzzy logic and spatial data visualization using geographic information technology. With our development we contribute to the development of modern information technologies in the educational process, namely intelligent linguistic resources with subsequent visualization and processing of spatial data. For the development of the web interface of the shell and the server part we used the Django framework of the Python programming language. Pandas and Folium libraries were used for data processing and visualization. To implement the fuzzy logic module the Levenshtein distance algorithm was used.
{"title":"Designing an Educational Intelligent System with Natural Language Processing Based on Fuzzy Logic","authors":"Konstantin Kulagin, Mansur Salikhov, R. Burnashev","doi":"10.1109/SmartIndustryCon57312.2023.10110734","DOIUrl":"https://doi.org/10.1109/SmartIndustryCon57312.2023.10110734","url":null,"abstract":"This paper presents the implementation of an educational intelligent system with natural language processing based on fuzzy logic and spatial data visualization using geographic information technology. With our development we contribute to the development of modern information technologies in the educational process, namely intelligent linguistic resources with subsequent visualization and processing of spatial data. For the development of the web interface of the shell and the server part we used the Django framework of the Python programming language. Pandas and Folium libraries were used for data processing and visualization. To implement the fuzzy logic module the Levenshtein distance algorithm was used.","PeriodicalId":157877,"journal":{"name":"2023 International Russian Smart Industry Conference (SmartIndustryCon)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124649485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1109/SmartIndustryCon57312.2023.10110717
A. Astafiev
The paper proposes a method of contactless matrix labeling of products made of ferrous metals, which combines the simplicity of applying shock-point labeling and the low cost of ink-jet labeling. The main goal of the enclosed solution is to reduce the cost of labeling. Various options for applying labeling at metalworking enterprises are considered. An approach to the recognition of the applied labeling on the surfaces of ferrous metals is proposed. A convolutional neural network was used to develop the recognition system. For training, we synthesized our own dataset of 12,865 images. The trainings of the neural network were carried out, the training results are given. Experimental studies on random images are presented, which showed a high percentage of labeling recognition.
{"title":"Development of a Methodology for the Identification of Ferrous Metal Products by Their Contactless Point Labeling Using Convolutional Neural Networks","authors":"A. Astafiev","doi":"10.1109/SmartIndustryCon57312.2023.10110717","DOIUrl":"https://doi.org/10.1109/SmartIndustryCon57312.2023.10110717","url":null,"abstract":"The paper proposes a method of contactless matrix labeling of products made of ferrous metals, which combines the simplicity of applying shock-point labeling and the low cost of ink-jet labeling. The main goal of the enclosed solution is to reduce the cost of labeling. Various options for applying labeling at metalworking enterprises are considered. An approach to the recognition of the applied labeling on the surfaces of ferrous metals is proposed. A convolutional neural network was used to develop the recognition system. For training, we synthesized our own dataset of 12,865 images. The trainings of the neural network were carried out, the training results are given. Experimental studies on random images are presented, which showed a high percentage of labeling recognition.","PeriodicalId":157877,"journal":{"name":"2023 International Russian Smart Industry Conference (SmartIndustryCon)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129733378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1109/SmartIndustryCon57312.2023.10110818
V. Bogatyrev, Anh Tu Le, E. A. Abramova
The article explores the possibility of improving the reliability of a network with multipath routing. A feature of the proposed study is the analysis of the influence of the placement of switching nodes that switch path segments on the probability of network connectivity with servers of the same type and heterogeneous in functionality. The purpose of the article is to increase the reliability of the network, taking into account the influence of the placement of communication nodes that implement the switching of route segments during reconfiguration, on the connectivity of the network with servers that are homogeneous and heterogeneous in functionality. The research involves the construction of a structural reliability model that takes into account the possibility of failures of both communication nodes and the links between them. The reliability model is built taking into account the availability of request sources with full and non-full access connections to the set of network paths.
{"title":"Reliability of Multipath Networks with Optimization of the Location of Inter-Path Communication Nodes","authors":"V. Bogatyrev, Anh Tu Le, E. A. Abramova","doi":"10.1109/SmartIndustryCon57312.2023.10110818","DOIUrl":"https://doi.org/10.1109/SmartIndustryCon57312.2023.10110818","url":null,"abstract":"The article explores the possibility of improving the reliability of a network with multipath routing. A feature of the proposed study is the analysis of the influence of the placement of switching nodes that switch path segments on the probability of network connectivity with servers of the same type and heterogeneous in functionality. The purpose of the article is to increase the reliability of the network, taking into account the influence of the placement of communication nodes that implement the switching of route segments during reconfiguration, on the connectivity of the network with servers that are homogeneous and heterogeneous in functionality. The research involves the construction of a structural reliability model that takes into account the possibility of failures of both communication nodes and the links between them. The reliability model is built taking into account the availability of request sources with full and non-full access connections to the set of network paths.","PeriodicalId":157877,"journal":{"name":"2023 International Russian Smart Industry Conference (SmartIndustryCon)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129956332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1109/SmartIndustryCon57312.2023.10110760
O. Maryasin
The paper considers a solution for optimal energy consumption control in a multi-zone office building based on a hybrid digital twin of a building. The hybrid digital twin comprises the energy model of the building, digital energy consumption models of both individual zones and the entire building, and a computer model of the heating, ventilation, and air conditioning system of the building. Artificial neural networks were used to implement all digital models. The EnergyPlus energy simulation system generated the input data to train neural networks. A genetic algorithm was used to find an optimal solution to the problem. The optimal energy consumption control of the building was implemented in the DTTool software package, developed by the author. This approach allows implementing optimal energy consumption control for multi-zone buildings with the division of energy consumption into that consumed by the entire building and that consumed by certain zones of the building.
{"title":"Optimal Energy Consumption Control in a Multi-Zone Building Based on a Hybrid Digital Twin","authors":"O. Maryasin","doi":"10.1109/SmartIndustryCon57312.2023.10110760","DOIUrl":"https://doi.org/10.1109/SmartIndustryCon57312.2023.10110760","url":null,"abstract":"The paper considers a solution for optimal energy consumption control in a multi-zone office building based on a hybrid digital twin of a building. The hybrid digital twin comprises the energy model of the building, digital energy consumption models of both individual zones and the entire building, and a computer model of the heating, ventilation, and air conditioning system of the building. Artificial neural networks were used to implement all digital models. The EnergyPlus energy simulation system generated the input data to train neural networks. A genetic algorithm was used to find an optimal solution to the problem. The optimal energy consumption control of the building was implemented in the DTTool software package, developed by the author. This approach allows implementing optimal energy consumption control for multi-zone buildings with the division of energy consumption into that consumed by the entire building and that consumed by certain zones of the building.","PeriodicalId":157877,"journal":{"name":"2023 International Russian Smart Industry Conference (SmartIndustryCon)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133851122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1109/SmartIndustryCon57312.2023.10110810
G. Martinov, Natalia Martemianova
The paper considers the problems of manufacturing prototypes of printed circuit boards on bench-type milling machines that require the prompt production of a small batch and correction, if it’s necessary. We used modern tools from third-party manufacturers for the preparation of production and processing of single- and double-layer printed circuit boards on bench-type CNC machines. A technique is proposed that formalizes the process of preparing and verifying part programs and manufacturing printed circuit boards. The methodology was tested and an example of manufacturing a prototype of a printed circuit board for a voltage regulator on a machine with an "AxiOMA Control" CNC system was illustrated.
{"title":"An Approach to the Production of Prototype Printed Circuit Boards on Bench-Type Machine with the CNC System","authors":"G. Martinov, Natalia Martemianova","doi":"10.1109/SmartIndustryCon57312.2023.10110810","DOIUrl":"https://doi.org/10.1109/SmartIndustryCon57312.2023.10110810","url":null,"abstract":"The paper considers the problems of manufacturing prototypes of printed circuit boards on bench-type milling machines that require the prompt production of a small batch and correction, if it’s necessary. We used modern tools from third-party manufacturers for the preparation of production and processing of single- and double-layer printed circuit boards on bench-type CNC machines. A technique is proposed that formalizes the process of preparing and verifying part programs and manufacturing printed circuit boards. The methodology was tested and an example of manufacturing a prototype of a printed circuit board for a voltage regulator on a machine with an \"AxiOMA Control\" CNC system was illustrated.","PeriodicalId":157877,"journal":{"name":"2023 International Russian Smart Industry Conference (SmartIndustryCon)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130759712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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