ISSN 1991-2927
 

АПУ № 3 (65) 2021

Author: "Iuliia Aleksandrovna Radionova"

Andrei Alekseevich Pertsev, Candidate of Sciences in Engineering; graduated from the Faculty of Mechanics and Mathematics of Ulyanovsk State University; Head of an department at FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of the automated enterprise management system implementation. e-mail: mars@mv.ruA. A. Pertsev

Aleksandr Nikolaevich Podobrii, Candidate of Sciences in Engineering; graduated from the Faculty of Mechanics and Mathematics of Ulyanovsk State University; Deputy Chief of a department at FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of the automated enterprise management system implementation. e-mail: mars@mv.ruA.N. Podobrii

Iuliia Aleksandrovna Radionova, Candidate of Sciences in Engineering; graduated from the Faculty of Mechanics and Mathematics of Ulyanovsk State University; Lead Programming Engineer at FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of automated workflow systems, intelligent technical documentation storage bases and systems for statistical analysis of supplier appraisal; research interests are in the field of electronic document management, archival depositories, statistical data analysis, decision support systems. e-mail: julia-owl@mail.ruI.A. Radionova

Material scheduling to provide manufacturing in machine industry63_5.pdf

The article describes an approach to the material scheduling to provide manufacturing by machinery designers. The approach is based on the statistics of material consumption over the last periods and on manufacturing sequence. The article considers information technology alternatives of production reserve planning. The authors propose the model of material support based on a time series analysis. They describe a study scheme and a database structure for calculating by the model developed. The algorithm steps of data analysis, modeling of time series and resulted VS test values comparison are described in details. The article defines an experimental calculation to test the model validity and diagrams to compare the time series of the main and auxiliary material sample pieces. The programmed calculations are given as an additional module for interactive web resource and are implemented through T-SQL.

Manufacturing plan formation, time series, critical path, scheduling network, material, resources, project manufacturing, mechanical engineering, capacity, statistics of operations fulfilled, shiftwork scheduling.

2021_'А

Рубрика: Information systems

Тематика:



Andrei Alekseevich Pertsev, Candidate of Sciences in Engineering; graduated from the Faculty of Mechanics and Mathematics of Ulyanovsk State University; Head of a department of FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of the automated enterprise management system implementation. e-mail: mars@mv.ruA.A. Pertsev,

Aleksandr Nikolaevich Podobrii, Candidate of Sciences in Engineering; graduated from the Faculty of Mechanics and Mathematics of Ulyanovsk State University; Deputy Chief of a department of FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of the automated enterprise management system implementation. e-mail: mars@mv.ruA.N. Podobrii,

Iuliia Aleksandrovna Radionova, Candidate of Sciences in Engineering; graduated from the Faculty of Mechanics and Mathematics of Ulyanovsk State University; Leading Software Engineer at FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of automated workflow systems, intelligent technical documentation storage bases and systems for statistical analysis of supplier appraisal; research interests are in the field of electronic document management, archival depositories, statistical data analysis, decision support systems. e-mail: julia-owl@mail.ruI.A. Radionova

An approach to plan the production resources of a machine-building enterprise using neural networks64_7.pdf

Generally, this is the experience of production workers, economists, designers, etc. that used to get a preliminary assessment of feasibility of hardware manufacturing at an enterprise. For a preliminary assessment, it is enough to understand the complexity of the product and available analogues. At the same time, it is not always possible to calculate an accurate production time for the product due to the lack of a complete set of design and technological documentation.
The article presents an approach to calculating the production time of hardware using neural networks based on existing data for previous periods and types of hardware using. This approach allows estimating the production time without using accurate data on the design and manufacturing technology. The article also describes the structure of neural networks and defines the training sample. Some experiments were conducted based on the sample data, which allowed determining the initial weight coefficients of the neural network. The software implementation is made in the form of an additional module for an interactive web resource and uses T-SQL.

Production plan formation, neural network, small-scale manufacturing, project manufacturing, mechanical engineering, production capacity, operation performance statistics.

2021_'А

Рубрика: Artificial intelligence

Тематика:



Andrei Alekseevich Pertsev, Candidate of Sciences in Engineering; graduated from the Faculty of Mechanics and Mathematics of Ulyanovsk State University; Head of a department of FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of the automated enterprise management system implementation. e-mail: mars@mv.ruA.A. Pertsev,

Aleksandr Nikolaevich Podobrii, Candidate of Sciences in Engineering; graduated from the Faculty of Mechanics and Mathematics of Ulyanovsk State University; Deputy Chief of a department of FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of the automated enterprise management system implementation. e-mail: mars@mv.ruA.N. Podobrii,

Iuliia Aleksandrovna Radionova, Candidate of Sciences in Engineering; graduated from the Faculty of Mechanics and Mathematics of Ulyanovsk State University; Lead Programming Engineer at FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of automated workflow systems, intelligent technical documentation storage bases and systems for statistical analysis of supplier appraisal; research interests are in the field of electronic document management, archival depositories, statistical data analysis, decision support systems. e-mail: julia-owl@mail.ruI.A. Radionova

The calculation model of hardware and software system production period designed by developing companies65_9.pdf

It is relevant for a developer of hardware and software systems to assess the duration of development in advance and determine the main factors influencing on its success. Bulk production or bulk production with little changes can be assessed based on the experience. However, when making preliminary assessment, some factors that had not a great effect before may not be considered. The article describes an approach to calculate the duration of hardware and software system development based on analogues, statistics and factors effecting the production. At the same time, it should be noted this method provides only a preliminary assessment and its usage requires additional s.

Duration of production plan development, neural net, small-scale manufacturing, design production, machinery, production capacity, operation flow statistics.

2021_'А

Рубрика: Computer-aided engineering

Тематика:



Aleksandr Alekseevich Emelianov, Federal Research-and-Production Center Joint Stock Company ‘Research-and-Production Association ‘Mars’, Candidate of Engineering; graduated from F.E. Dzerzhinsky Military Academy; Deputy Chief Engineer for Quality Assurance and Engineering Support - Head of the Management Department of Federal Research-and-Production Center Joint Stock Company ‘Research-and-Production Association ‘Mars’; an author of publications in the field of creation of the quality management and information security systems, statistical analysis of supplier appraisal. [e-mail: mars@mv.ru]A. Emelianov,

Iuliia Aleksandrovna Radionova, Federal Research-and-Production Center Joint Stock Company ‘Research-and-Production Association ‘Mars’, Candidate of Engineering; graduated from the Faculty of Mathematics and Mechanics of Ulyanovsk State University; finished her postgraduate study at Ulyanovsk State Technical University; Lead Software Engineer at FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of automated workflow systems, intelligent technical documentation storages, and statistical analysis of supplier appraisal. [e-mail: julia-owl@mail.ru]I. Radionova,

Aleksandr Leonidovich Savkin, Federal Research-and-Production Center Joint Stock Company ‘Research-and-Production Association ‘Mars’, Candidate of Military Sciences, Associate Professor; graduated from Ulyanovsk Higher Military Command School of Communications, Marshal Budjonny Military Academy of Signal Corps, completed postgraduate studies in the Military Academy of Communications; Head of Science and Engineering Support Department of FRPC JSC ‘RPA ‘Mars’; an author of scientific works, textbooks, and articles in the field of development and modelling of communication control systems and statistical analysis of supplier appraisal. [e-mail: mars@mv.ru]A. Savkin

Information and Analytical Model of Product Testing for Counterfeit 52_4.pdf

The article considers a problem of counterfeit products and the existing methods for solving this problem. An information and analytical method for the optimization of an expert product evaluation is proposed, which uses a parameter set and allows to automate the final decision-making process. The set of parameters takes into account not only indicators for suppliers, but also possible consequences of using counterfeit products for different groups of consumers. The method is based on the application of a neural network using a certain base of expert knowledge. Network training is provided during analyzing process with the possibility of correction by an expert. Modeling of the analysis process with different sets of input parameters and various parameters of the neural network was carried out. The results are stored in the database; the neural network performance is evaluated based on these results. The database structure, input parameters structure and the visualization of experimental results are presented.

Counterfeit, modeling, evaluation parameter, database, software, neural network.

2018_'А

Рубрика:

Тематика:


© ФНПЦ АО "НПО "Марс", 2009-2021 Работает на Joomla!