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Journal articles on the topic 'Process of Simulation'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Matsumura, Takashi. "Special Issue on Process Simulation." International Journal of Automation Technology 7, no. 1 (2013): 5. http://dx.doi.org/10.20965/ijat.2013.p0005.

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High production rates and low costs in manufacturing process should be considered in the manufacturing design divisions. Process simulation, therefore, plays an important role in implementing high performance manufacturing. Simulation is expected to improve the manufacturing processes and the human activities without production faults and downtime of the manufacturing facilities. The production simulation has become diversified with requirements for the manufacturing processes. Then, the effective use of the simulation is also an important issue for the simulation users considering investment

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2

Cohal, Viorel. "A Simulation of Spot Welding Process." Applied Mechanics and Materials 657 (October 2014): 226–30. http://dx.doi.org/10.4028/www.scientific.net/amm.657.226.

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The paper presents the optimization of spot welding parameters using offline simulation. The procedure of making simulation with SORPAS® is similar to the procedure of doing practical welding process, which can be divided into the following three steps:Data preparation - the materials and geometries of the workpieces and electrodes are defined, the type of welding machine is selected and the process parameters are specified.Running simulation of welding - the parts are welded in the selected welding machine with the specified process parameter settings. The simulations can be carried out in fo

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3

Galić, Mario, Ralf Thronicke, Benjamin Michael Schreck, Immo Feine, and Hans-Joachim Bargstädt. "PROCESS MODELING AND SCENARIO SIMULATION IN CONSTRUCTION USING ENTERPRISE DYNAMICS SIMULATION SOFTWARE." Elektronički časopis građevinskog fakulteta Osijek 6, no. 10 (2015): 22–29. http://dx.doi.org/10.13167/2015.10.3.

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4

Pedersen, J. "Controlling Activated Sludge Process Using EFOR." Water Science and Technology 26, no. 3-4 (1992): 783–90. http://dx.doi.org/10.2166/wst.1992.0459.

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A newly developed simulation program, based on the Activated Sludge Model No. 1, has been investigated for its controlling abilities. The program is capable of simulating most of the control types which have been applied to wastewater treatment plants. The program was tested on a nitrifying and a denitrifying treatment plant. The results showed that the model makes good simulations of the applied controls.

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5

E, Kanniga, and Akhil Varma. "History for Batch Process Simulation CM System." International Journal of Psychosocial Rehabilitation 23, no. 4 (2019): 354–60. http://dx.doi.org/10.37200/ijpr/v23i4/pr190194.

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6

Elliman, Tony, Tally Hatzakis, and Alan Serrano. "Business Process Simulation." International Journal of Enterprise Information Systems 2, no. 3 (2006): 43–58. http://dx.doi.org/10.4018/jeis.2006070104.

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7

Onga, Shinji. "VLSI Process Simulation." IEEJ Transactions on Electronics, Information and Systems 107, no. 6 (1987): 507–12. http://dx.doi.org/10.1541/ieejeiss1987.107.6_507.

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8

Tseng, Ren-Ho, Chien-Hung Wen, Chen-Hsiang Chang, Yu-Hao Chen, Chieh-Hsun Tsai, and Sheng-Jye Hwang. "Nozzle Pressure- and Screw Position-Based CAE Scientific Process Parameter Setup for Injection Molding Process." Polymers 17, no. 2 (2025): 198. https://doi.org/10.3390/polym17020198.

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This study developed a scientific process parameter setup based on nozzle pressure and screw position, with the process parameter search sequence being injection speed, V/P switchover position, packing pressure, and packing time. Unlike previous studies, this study focuses on the scientific process parameter setup of experiments and simulations, as well as on the implementation of calibration. Experiments and simulations had the same trend of results in the scientific process parameter setup. Although the experiments and simulations had the same trend, the machine response caused parameter err

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9

Linnhoff, B., and C. G. Akinradewo. "Linking process simulation and process integration." Computers & Chemical Engineering 23 (June 1999): S945—S953. http://dx.doi.org/10.1016/s0098-1354(99)80229-4.

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10

Nagursky, Oleg, and Yaroslav Gumnitsky. "Release of Capsulated Mineral Fertilizers Components. Process Simulation." Chemistry & Chemical Technology 6, no. 3 (2012): 321–25. http://dx.doi.org/10.23939/chcht06.03.321.

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11

Kr Mahobiya, Hemant. "Simulation of Wax Injection Process in Investment Casting." International Journal of Science and Research (IJSR) 8, no. 1 (2019): 2290–93. https://doi.org/10.21275/sr190110123042.

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12

Choudhary, Reema, and Nauman Riaz. "A business process re-engineering approach to transform business process simulation to BPMN model." PLOS ONE 18, no. 3 (2023): e0277217. http://dx.doi.org/10.1371/journal.pone.0277217.

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Software reverse engineering and reengineering are becoming common in the field of games and website development. Simulation and modeling play an important role in understanding the flow of the overall system. Business process modeling notation (BPMN) is used to show the overall architecture of the business process. Simulated business process re-engineering is essential for implementing change or creating new processes. The simulation model explains whether a change will be successful or not prior to adopting any new business processes or other changes. Some available tools help convert the BP

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13

Shi, Peng, Fei Liu, and Ming Yang. "Critical Problems in Validation Process of Simulation Models." Advanced Materials Research 187 (February 2011): 422–27. http://dx.doi.org/10.4028/www.scientific.net/amr.187.422.

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Simulation models are increasingly being used to solve more and more complex problems and to aid in decision-making. To provide a realistic and confident simulation environment for users, simulation models have become key components in military simulations. This paper discusses the modeling nature of simulation models, and then the modified validation criteria for measuring the agreements between Subject Matter Experts and simulation models are presented. Furthermore, validation methods such as graphical comparison, feature analysis, face validation, confidence interval and hypothesis tests of

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14

Bowles, David E., and Lorraine R. Gardiner. "Supporting process improvements with process mapping and system dynamics." International Journal of Productivity and Performance Management 67, no. 8 (2018): 1255–70. http://dx.doi.org/10.1108/ijppm-03-2017-0067.

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Purpose The purpose of this paper is to study the effectiveness of combining process mapping and system dynamics (SD) in an organization’s ongoing business process improvement projects. Design/methodology/approach Norfield Industries, designer and manufacturer of prehung door machinery, used process mapping and SD in a project targeting the improvement of its design document control process. The project team first used process mapping to document its current process and identify potential improvements. The team then developed an SD model to investigate the potential impacts of proposed process

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15

PEI, Q. X., C. LU, F. Z. FANG, and H. WU. "MOLECULAR DYNAMICS SIMULATION OF NANOMETRIC CUTTING PROCESS." International Journal of Nanoscience 05, no. 04n05 (2006): 633–38. http://dx.doi.org/10.1142/s0219581x06004905.

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Nanoscale machining involves changes in only a few atomic layers at the surface. Molecular dynamics (MD) simulation can play a significant role in addressing a number of machining problems at the atomic scale. In this paper, we employed MD simulations to study the nanometric cutting process of single crystal copper. Instead of the widely used Morse potential, we used the Embedded Atom Method (EAM) potential for this study. The simulations were carried out for various tool geometries at different cutting speeds. Attention was paid to the cutting chip formation, the cutting surface morphology an

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16

Oktavian, Muhammad Rizki. "Applying Gaussian Process Regression for Machine Learning-Assisted Reactor Simulations." Journal of Physics: Conference Series 2828, no. 1 (2024): 012007. http://dx.doi.org/10.1088/1742-6596/2828/1/012007.

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Abstract This study explores the integration of machine learning, specifically Gaussian Process Regression (GPR), into traditional reactor core simulations. Building upon previous work on Boiling Water Reactors (BWR), GPR is implemented to predict and correct errors in lower-fidelity simulation outcomes. The findings demonstrate significant improvements in prediction accuracy when GPR is coupled with the diffusion-based core simulator, exhibiting remarkable reductions in both keff and nodal power errors. The comparison reveals that the GPR-enhanced core simulation model significantly outperfor

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17

Viswanathan, N. N., M. N. Srinivasan, and A. K. Lahiri. "Process Simulation of Cupola." ISIJ International 38, no. 10 (1998): 1062–68. http://dx.doi.org/10.2355/isijinternational.38.1062.

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18

TANAKA, Toru, Seiya HAGIHARA, and Takanobu MORI. "Simulation of Shearing Process." Journal of the Japan Society for Technology of Plasticity 55, no. 638 (2014): 195–99. http://dx.doi.org/10.9773/sosei.55.195.

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19

Ivanov, Viktor, Sergey Ivanov, and Vladimir Ivanov. "Hydroabrasive cutting process simulation." Актуальные направления научных исследований XXI века: теория и практика 3, no. 5 (2015): 270–74. http://dx.doi.org/10.12737/16023.

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20

Saydam, Tuncay. "Process-oriented simulation languages." ACM SIGSIM Simulation Digest 16, no. 2 (1985): 8–13. http://dx.doi.org/10.1145/1102958.1102959.

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21

Banks, Jerry, and John S. Carson. "Process-interaction simulation languages." SIMULATION 44, no. 5 (1985): 225–34. http://dx.doi.org/10.1177/003754978504400503.

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22

Greasley, A. "Simulation in process design." Manufacturing Engineer 78, no. 4 (1999): 173–77. http://dx.doi.org/10.1049/me:19990406.

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23

Rafferty, C. S. "Front-end process simulation." Solid-State Electronics 44, no. 5 (2000): 863–68. http://dx.doi.org/10.1016/s0038-1101(99)00283-x.

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24

Lorenz, J., B. Baccus, and W. Henke. "Three-dimensional process simulation." Microelectronic Engineering 34, no. 1 (1996): 85–100. http://dx.doi.org/10.1016/s0167-9317(96)00011-1.

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25

Fariselli, L., M. Fumagalli, M. Mapelli, et al. "The virtual simulation process." European Journal of Cancer 37 (April 2001): S215. http://dx.doi.org/10.1016/s0959-8049(01)81281-8.

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26

Raffo, David, and Paul Wernick. "Software Process Simulation Modelling." Journal of Systems and Software 59, no. 3 (2001): 223–25. http://dx.doi.org/10.1016/s0164-1212(01)00063-2.

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27

Cale, T. S., M. O. Bloomfield, D. F. Richards, K. E. Jansen, and M. K. Gobbert. "Integrated multiscale process simulation." Computational Materials Science 23, no. 1-4 (2002): 3–14. http://dx.doi.org/10.1016/s0927-0256(01)00216-6.

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28

Volesky, Bohumil. "Biosorption process simulation tools." Hydrometallurgy 71, no. 1-2 (2003): 179–90. http://dx.doi.org/10.1016/s0304-386x(03)00155-5.

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29

Ryan, John, and Cathal Heavey. "Process modeling for simulation." Computers in Industry 57, no. 5 (2006): 437–50. http://dx.doi.org/10.1016/j.compind.2006.02.002.

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30

Jifeng, He. "Process simulation and refinement." Formal Aspects of Computing 1, no. 1 (1989): 229–41. http://dx.doi.org/10.1007/bf01887207.

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31

Taniguchi, Kenji, and Chihiro Hamaguchi. "Semiconductor Process/Device Simulation." Bulletin of the Japan Institute of Metals 31, no. 7 (1992): 608–13. http://dx.doi.org/10.2320/materia1962.31.608.

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32

Peng, J., and R. Victora. "Micromagnetic recording process simulation." IEEE Transactions on Magnetics 23, no. 5 (1987): 2865–67. http://dx.doi.org/10.1109/tmag.1987.1065428.

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33

Ehrhardt, K., J. Borchardt, F. Grund, and D. Horn. "Distributed Dynamic Process Simulation." ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 81, S3 (2001): 715–16. http://dx.doi.org/10.1002/zamm.200108115133.

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34

Schöneberger, J. C., A. Fricke, and A. Wolna. "Process Simulation Cup 2018." Chemie Ingenieur Technik 90, no. 9 (2018): 1245. http://dx.doi.org/10.1002/cite.201855250.

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35

Lebl, Aleksandar, Dragan Mitić, and Žarko Markov. "A role of Excel program in telecommunication processes simulation." Scientific Technical Review 73, no. 1 (2023): 13–17. http://dx.doi.org/10.5937/str2301013l.

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This paper presents the simulation of mobile telephony systems realized in Excel program. Although primarily intended for other applications, Excel has several advantages over other specialized programs for simulation purposes. Excel application for simulation is illustrated by several examples from already published papers with the main goal to describe the most important part of realized simulations that allows determination of all important characteristics of telecommunication traffic process. Beside traffic process, the Excel application allows a reliable simulation of base station emissio

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36

Szilágyi, László. "Random Process Simulation Using Petri Nets." MACRo 2015 1, no. 1 (2015): 177–82. http://dx.doi.org/10.1515/macro-2015-0017.

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AbstractThis paper introduces a Petri net designed for the simulation of the ancient game called Rock-Paper-Scissors or Roshambo. The network enables us to simulate the behavior of machine players and allows us to design and evaluate strategies against weighted random machine opponents. The paper also presents a theoretical calculus on winning chances. Simulations carried out using the software package Pipe (version 4.3.0) fully confirm the theoretical considerations.

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37

Helquist, Joel H., Amit Deokar, Jordan J. Cox, and Alyssa Walker. "Analyzing process uncertainty through virtual process simulation." Business Process Management Journal 18, no. 1 (2012): 4–19. http://dx.doi.org/10.1108/14637151211214984.

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38

Li, Fanxing, Liang Zeng, and Liang-Shih Fan. "Biomass direct chemical looping process: Process simulation." Fuel 89, no. 12 (2010): 3773–84. http://dx.doi.org/10.1016/j.fuel.2010.07.018.

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39

Stephenson, G. R., and C. F. Shewchuk. "Reconciliation of process data with process simulation." AIChE Journal 32, no. 2 (1986): 247–54. http://dx.doi.org/10.1002/aic.690320211.

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40

Traversari, Roberto, Rien Goedhart, and Jan Maarten Schraagen. "Process Simulation during the Design Process Makes the Difference: Process Simulations Applied to a Traditional Design." HERD: Health Environments Research & Design Journal 6, no. 2 (2013): 58–76. http://dx.doi.org/10.1177/193758671300600206.

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41

Arachchige, Udara S. P. R., Dinesh Kawan, and Morten C. Melaaen. "Simulation of Carbon Dioxide Capture for Aluminium Production Process." International Journal of Modeling and Optimization 4, no. 1 (2014): 43–50. http://dx.doi.org/10.7763/ijmo.2014.v4.345.

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42

Hourng, Lih-Wu, and Yau Si Lin. "Numerical Simulation of Debinding Process in Metal Injection Molding." International Journal of Modeling and Optimization 4, no. 6 (2014): 421–25. http://dx.doi.org/10.7763/ijmo.2014.v4.411.

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43

Huliienko, Serhii, Yaroslav Kornienko, Svitlana Muzyka, and Kateryna Holubka. "Mathematical Simulation of Nanofiltration Process: State of Art Review." Chemistry & Chemical Technology 18, no. 2 (2024): 187–99. http://dx.doi.org/10.23939/chcht18.02.187.

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A review of publications devoted to the mathematical simulation of the nanofiltration process was carried out, the advantages, limitations, and areas of application of various modeling approaches were determined. It was found that the most effective approaches are based on the extended Nernst-Planck equation, Donnan equilibrium, as well as methods of computational fluid dynamics and molecular dynamics. The use of software for solving nanofiltration simulation problems was considered.

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44

Savenko, V. S., G. M. Verbinska, and L. A. Bulavin. "Computer Simulation of Evaporation Process of NaCl Aqueous Solution." Ukrainian Journal of Physics 61, no. 9 (2016): 812–18. http://dx.doi.org/10.15407/ujpe61.09.0812.

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45

Sakakura, M., S. Tsukamoto, T. Fujiwara, and I. Inasaki. "Visual simulation of the grinding process." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 222, no. 10 (2008): 1233–39. http://dx.doi.org/10.1243/09544054jem1032.

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Grinding is one of the machining methods for finishing that is performed by using a large number of abrasive grains with irregular shapes and random distribution. While this feature enables accurate and high-quality machining, it complicates analysis of the grinding process. To solve this problem, a great number of computer simulations have been carried out using the Monte Carlo method. Most of them, however, statically calculate geometric interaction between a grain and a workpiece, and have not provided enough achievements for practical applications. In this study, taking this background int

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46

Shen, Chen, and Min Ying Zong. "Simulation on 3D Computer Dynamic Cloth Simulation Process." Applied Mechanics and Materials 380-384 (August 2013): 1585–88. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1585.

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In the process of complex cloth modelling, the cloth tissues mobile disorderly, resulting in ineffective modeling and other problems. To address the problems mentioned above, a corresponding solution is put forward. Based on the HLA development platform and Vega development platform, a 3D cloth simulation method which improves the simulation algorithm of annealing is proposed. Some related processings are performed on the cloth image, and the moving cloth issues are simulated using the improved fast cloth moving particle model to complete 3D simulation of the cloth. Experimental results show t

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47

Kohn, Oliver, Julius Frenzel, Erkut Sarikaya, and Matthias Weigold. "Virtuelle Prozesssimulation einer CNC-Maschine/Virtual process simulation of a CNC machine – Digital twin for stress-based payment models." wt Werkstattstechnik online 113, no. 01-02 (2023): 24–28. http://dx.doi.org/10.37544/1436-4980-2023-01-02-28.

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Innovative Ansätze bei der Prozessplanung und -optimierung mittels digitaler Zwillinge erfordern leistungsfähige Simulationen für Werkzeugmaschinen. Die Entwicklung einer solchen Simulation für ein 3-Achs-Vertikal-Bearbeitungszentrum wird hier als hybride Multi-Domain-Simulation in „Modelica“ implementiert und experimentell validiert. Dabei ist das Ziel die berechneten Kenngrößen im Kontext aktueller Entwicklungen, wie zum Beispiel belastungsorientierter Bezahlmodelle bereitzustellen. Innovative approaches to process planning and optimization using digital twins require powerful simulations fo

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48

Bezerra, Felipi Luiz de Assunção, Lucas Bonfim Rocha, and Guilherme Henrique Gallo Lanza. "DYNAMIC SIMULATION INTEGRATION IN PROCESS CONTROL EDUCATION: A CASE STUDY ON METHANOL-WATER SEPARATION USING AVEVA PROCESS SIMULATION." ARACÊ 7, no. 1 (2025): 996–1012. https://doi.org/10.56238/arev7n1-061.

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This study explores implementing a novel teaching methodology for process control education using AVEVA Process Simulation software. The methodology integrates interactive simulations and practical problem-solving exercises, focusing on the dynamic simulation of an ethanol-water distillation process. In this scenario, students are tasked with evaluating the performance of controllers integrated into the simulation, allowing them to engage with dynamic process behaviors in a virtual environment. The results indicate a significant improvement in students' understanding and assimilation of proces

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49

BIAŁASZ, Sebastian, and Ramon PAMIES. "NUMERICAL SIMULATION OF THE DESIGN OF EXTRUSION PROCESS OF POLYMERIC MINI-TUBES." Applied Computer Science 14, no. 3 (2018): 81–95. http://dx.doi.org/10.35784/acs-2018-23.

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In this paper we represent a study reporting the numerical simulation of small-diameter pipes extrusion process. Polypropylene and low density polyethylene were chosen as plastics and a selected transverse head as a tool in the simulations. The aim of the study is to examine the distribution of temperature in the individual sections of the bagasse and tools, in order to optimize the parameters and process flow extrusion and validate the implementation tools, by simulating the flow of plastic by the head.

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50

Nahorniak, Svitlana, Natalia Stavnycha, and Ivan Chyshma. "Application of educational simulations in the process of adult education." Педевтологія 2, no. 1 (2024): 23–30. https://doi.org/10.31652/3041-1203-2024(1)-23-30.

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The article analyzes modern research on the application of simulation games in the process of adult education, in particular in such areas as medicine, aviation, jurisprudence and pedagogy. The advantages of such training methods are noted, in particular, the development of practical skills in a safe environment. The article describes in detail the different types of simulations, such as virtual reality, augmented reality, role-playing and computer simulations, and provides examples of their successful use. In particular, the use of the Body Interact platform for the training of medical profes

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