Journal articles on the topic 'Simulation tools'

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1

Grafton, Carl, and Anne Permaloff. "Microcomputer Simulations and Simulation Writing Tools." PS: Political Science and Politics 22, no. 2 (June 1989): 247. http://dx.doi.org/10.2307/419603.

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Grafton, Carl, and Anne Permaloff. "Microcomputer Simulations and Simulation Writing Tools." PS: Political Science & Politics 22, no. 02 (June 1989): 247–57. http://dx.doi.org/10.1017/s1049096500030560.

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3

Ritchie, Nicholas W. M. "Spectrum Simulation in DTSA-II." Microscopy and Microanalysis 15, no. 5 (September 16, 2009): 454–68. http://dx.doi.org/10.1017/s1431927609990407.

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AbstractSpectrum simulation is a useful practical and pedagogical tool. Particularly with complex samples or trace constituents, a simulation can help to understand the limits of the technique and the instrument parameters for the optimal measurement. DTSA-II, software for electron probe microanalysis, provides both easy to use and flexible tools for simulating common and less common sample geometries and materials. Analytical models based on ϕ(ρz) curves provide quick simulations of simple samples. Monte Carlo models based on electron and X-ray transport provide more sophisticated models of arbitrarily complex samples. DTSA-II provides a broad range of simulation tools in a framework with many different interchangeable physical models. In addition, DTSA-II provides tools for visualizing, comparing, manipulating, and quantifying simulated and measured spectra.
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Dean, T. D., and M. J. Haney. "FASTBUS simulation tools." IEEE Transactions on Nuclear Science 39, no. 4 (1992): 910–14. http://dx.doi.org/10.1109/23.159731.

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5

Mukherjee, S. S., S. V. Adve, T. Austin, J. Emer, and P. S. Magnusson. "Performance simulation tools." Computer 35, no. 2 (2002): 38–39. http://dx.doi.org/10.1109/2.982914.

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Reinhart, William F., and Charles J. C. Lloyd. "A Human Factors Simulation Tool for Stereoscopic Displays." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, no. 19 (October 1994): 1290–94. http://dx.doi.org/10.1177/154193129403801909.

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The increasing complexity of advanced display systems places increased importance on simulation tools. Display simulation tools allow the development of displays with the desired balance of image quality and end cost while reducing the time and cost otherwise associated with iterative testing of physical prototypes. In addition, simulation offers the advantage of permitting demonstration of display systems to targeted users and decision makers early in the display design cycle, allowing for more feedback in the design process. A new display simulation tool is described in this paper with the capability of simulating advanced electronic stereoscopic displays. To illustrate the use of this tool, an evaluation of a simulated stereoscopic head-mounted display is reported. Surgeons viewed stereoscopic laparoscopic imagery and rated the acceptability of display gray scale, resolution, and field of view. Clear preferences were seen for levels of each of these parameters. Simulations such as the one described in this report play an invaluable role in defining acceptable design parameters prior to commitment to display production.
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Moshagen, Thilo. "Convergence of explicitly coupled simulation tools (co-simulations)." Journal of Numerical Mathematics 27, no. 1 (March 26, 2019): 23–36. http://dx.doi.org/10.1515/jnma-2017-0073.

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Abstract In engineering, it is a common desire to couple existing simulation tools together into one big system by passing information from subsystems as parameters into the subsystems under influence. As executed at fixed time points, this data exchange gives the global method a strong explicit component. Globally, such an explicit co-simulation schemes exchange time step can be seen as a step of an one-step method which is explicit in some solution components. Exploiting this structure, we give a convergence proof for such schemes. As flows of conserved quantities are passed across subsystem boundaries, it is not ensured that system-wide balances are fulfilled: the system is not solved as one single equation system. These balance errors can accumulate and make simulation results inaccurate. Use of higher-order extrapolation in exchanged data can reduce this problem but cannot solve it. The remaining balance error has been handled in past work by recontributing it to the input signal in next coupling time step, a technique labeled balance correction methods. Convergence for that method is proven. Further, the lack of stability for co-simulation schemes with and without balance correction is stated.
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Kyncl, Jiří. "Digital Factory Simulation Tools." Manufacturing Technology 16, no. 2 (April 1, 2016): 371–75. http://dx.doi.org/10.21062/ujep/x.2016/a/1213-2489/mt/16/2/371.

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

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Rauh, Jochen, and Alexander Eichberger. "Coupling of Simulation Tools." IFAC Proceedings Volumes 30, no. 8 (June 1997): 221–26. http://dx.doi.org/10.1016/s1474-6670(17)43827-4.

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McClintock, Edwin, and Zhonghong Jiang. "Technology Tips: Spreadsheets: Powerful Tools for Probability Simulations." Mathematics Teacher 90, no. 7 (October 1997): 572–79. http://dx.doi.org/10.5951/mt.90.7.0572.

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The Curriculum and Evaluation Standards for School Mathematics (NCTM 1989) recommends that high school students extend their K-8 experiences with simulations and experimental probability to continue to improve their intuition and build more formal concepts of theoretical probability based on these experiences. In keeping with this idea, we use spreadsheets frequently in studying and investigating probabilistic situations with both high school students and preservice mathematics teachers. Our approach includes simulations but goes beyond the simulation process as a way of learning important concepts and principles of probability. We have found that spreadsheets are a very powerful tool for simulating probabilistic situations, not only for simple problems, such as simulating coin Ripping and die tossing, but also for rather complex problem situations. In this article, we illustrate the use of spreadsheets as a simulation tool for solving a collection of probability problems. The spreadsheet program we use is Microsoft Excel (1995), but other spreadsheet programs, such as Quattro Pro (Borland 1994), can be used with the same degree of effectiveness.
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Van Tendeloo, Yentl, and Hans Vangheluwe. "An evaluation of DEVS simulation tools." SIMULATION 93, no. 2 (November 28, 2016): 103–21. http://dx.doi.org/10.1177/0037549716678330.

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DEVS is a popular formalism for modeling complex dynamic systems using a discrete-event abstraction. Owing to its popularity, and the simplicity of the simulation kernel, a number of tools have been constructed by academia and industry. However, each of these tools has distinct design goals and a specific programming language implementation. Consequently, each supports a specific set of formalisms, combined with a specific set of features. Performance differs significantly between different tools. We provide an overview of the current state of eight different DEVS simulation tools: ADEVS, CD++, DEVS-Suite, MS4 Me, PowerDEVS, PythonPDEVS, VLE, and X-S-Y. We compare supported formalisms, compliance, features, and performance. This paper aims to help modelers in deciding which tools to use to solve their specific problems. It further aims to help tool builders, by showing the aspects of their tools that could be extended in future tool versions.
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Wang, Hao, Ai Bing Yu, Liang Dong, and Lei Wu. "Wear Simulation for Edge Preparation Cutting Tools." Applied Mechanics and Materials 101-102 (September 2011): 1039–42. http://dx.doi.org/10.4028/www.scientific.net/amm.101-102.1039.

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Edge preparation is a process to modify edge geometry and surface integrity of cutting tools. Edge preparation experiments of tungsten carbide cutting tools were carried out through an abrasive nylon brushing method. Tools wear and cutting temperatures with different edge radius were simulated with FEM software. The experimental results show that cutting edge defects were eliminated through brushing edge preparation. The edge radius has influences on cutting tool performance. When the edge radius is 20μm, the least wear can be obtained. Then tool wear value increases with the edge radius. A suitable edge radius will have a reasonable cutting temperature distribution. The tool temperature distribution shows the same results as tool wear. A suitable edge radius range is needed for edge preparation and a reasonable edge radius is needed to improve tool life.
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Vesa, Dumitru Claudiu, and Ilie Gligorea. "Creating Modeling and Simulation Scenarios Using Dedicated Tools." International conference KNOWLEDGE-BASED ORGANIZATION 26, no. 1 (June 1, 2020): 172–75. http://dx.doi.org/10.2478/kbo-2020-0027.

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AbstractSimulating military actions has been an important element since ancient times, the fighting ability being intricately connected to training and exercises simulating real combat. The paper describes the steps followed to create a scenario used for online teaching activities during the pandemic, using the facilities of the Centre for Modelling and Simulation of Military Actions. The scenarios were created by using two modelling and simulation software - JCATS (Joint Conflict and Tactical Simulation) for constructive simulation and the VBS (Virtual Battlespace) for virtual simulation.
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Binder, M., F. Klocke, and D. Lung. "Tool wear simulation of complex shaped coated cutting tools." Wear 330-331 (May 2015): 600–607. http://dx.doi.org/10.1016/j.wear.2015.01.015.

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Pekarčíková, Miriam, Peter Trebuňa, Marek Kliment, Radko Popovič, and Jaromír Markovič. "Basics of Model Creation in Simulation Program Extend." Applied Mechanics and Materials 816 (November 2015): 369–74. http://dx.doi.org/10.4028/www.scientific.net/amm.816.369.

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Article deals with the possibilities of modelling and simulating in program Extend. It is a simple tool that allows you to model and carry out simulation of specified process to create a good precondition for decision-making. The development of simulation tools and their introduction into industrial practice is the now common practice, particularly in large enterprises. In these turbulent times it is only a matter of time before these virtual tools are commonly used in small businesses.
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17

Matsumura, Takashi. "Special Issue on Process Simulation." International Journal of Automation Technology 7, no. 1 (January 5, 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 returns. Recently advanced materials have been applied to products with developments in material science. The machining systems have also become complicated with progress in the machine tools. Therefore, the process simulations should be developed in terms of materials and machine tools. This special issue includes 9 papers for providing innovative approaches to advanced modeling and simulations in manufacturing technologies and machine tool systems. The special issue also includes discussions in the simulation with the advanced materials for future manufacturing processes. I thank the authors for their generous cooperation and the editing staff for its many contributions.
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18

Alosaimi, Shatha, Armand Bandiang, Noelle van Biljon, Denis Awany, Prisca K. Thami, Milaine S. S. Tchamga, Anmol Kiran, et al. "A broad survey of DNA sequence data simulation tools." Briefings in Functional Genomics 19, no. 1 (December 23, 2019): 49–59. http://dx.doi.org/10.1093/bfgp/elz033.

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Abstract In silico DNA sequence generation is a powerful technology to evaluate and validate bioinformatics tools, and accordingly more than 35 DNA sequence simulation tools have been developed. With such a diverse array of tools to choose from, an important question is: Which tool should be used for a desired outcome? This question is largely unanswered as documentation for many of these DNA simulation tools is sparse. To address this, we performed a review of DNA sequence simulation tools developed to date and evaluated 20 state-of-art DNA sequence simulation tools on their ability to produce accurate reads based on their implemented sequence error model. We provide a succinct description of each tool and suggest which tool is most appropriate for the given different scenarios. Given the multitude of similar yet non-identical tools, researchers can use this review as a guide to inform their choice of DNA sequence simulation tool. This paves the way towards assessing existing tools in a unified framework, as well as enabling different simulation scenario analysis within the same framework.
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19

Jones, J. W. "MODEL INTEGRATION AND SIMULATION TOOLS." Acta Horticulturae, no. 456 (March 1998): 411–18. http://dx.doi.org/10.17660/actahortic.1998.456.50.

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20

Neureuther, A. R. "Process modeling and simulation tools." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 4, no. 3 (May 1986): 912–15. http://dx.doi.org/10.1116/1.574006.

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21

Pfeiffer, A., and M. G. Pia. "Tools for simulation and analysis." Nuclear Physics B - Proceedings Supplements 117 (April 2003): 888–90. http://dx.doi.org/10.1016/s0920-5632(03)90699-2.

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22

Thode, Henry C. "Statistical Tools for Simulation Practitioners." Technometrics 30, no. 4 (November 1988): 464. http://dx.doi.org/10.1080/00401706.1988.10488459.

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23

Yamamoto, H., M. Barton, B. Bhawal, M. Evans, and S. Yoshida. "Simulation tools for future interferometers." Journal of Physics: Conference Series 32 (March 2, 2006): 398–403. http://dx.doi.org/10.1088/1742-6596/32/1/061.

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24

Stanton, Scott. "Electric vehicle Development simulation tools." ATZelektronik worldwide 8, no. 6 (December 2013): 50–53. http://dx.doi.org/10.1365/s38314-013-0213-7.

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Rahman, Muhammad Azizur, Algirdas Pakštas, and Frank Zhigang Wang. "Network modelling and simulation tools." Simulation Modelling Practice and Theory 17, no. 6 (July 2009): 1011–31. http://dx.doi.org/10.1016/j.simpat.2009.02.005.

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26

Tizzard, Keith, and J. P. C. Kleijnen. "Statistical Tools for Simulation Practitioners." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 2 (1988): 374. http://dx.doi.org/10.2307/2982781.

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Pike, A. W., and M. A. Johnson. "Simulation Tools for the 90s." Measurement and Control 27, no. 6 (July 1994): 185–94. http://dx.doi.org/10.1177/002029409402700602.

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Delzendeh, E., R. Alaaeddine, M. Al-Adhami, and S. Wu. "Beyond Building Energy Simulation Tools." IOP Conference Series: Earth and Environmental Science 588 (November 21, 2020): 022044. http://dx.doi.org/10.1088/1755-1315/588/2/022044.

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Razzano, Massimiliano. "Pulsar simulation tools for GLAST." Astrophysics and Space Science 309, no. 1-4 (April 17, 2007): 235–39. http://dx.doi.org/10.1007/s10509-007-9451-6.

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Oron, Gideon. "Statistical tools for simulation practitioners." European Journal of Operational Research 34, no. 2 (March 1988): 240–41. http://dx.doi.org/10.1016/0377-2217(88)90361-x.

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Wang, Wentao, Wenying Yang, and Venkata Dinavahi. "Co-Simulation Interfacing Capabilities in Device-Level Power Electronic Circuit Simulation Tools: An Overview." International Journal of Power Electronics and Drive Systems (IJPEDS) 6, no. 4 (December 1, 2015): 665. http://dx.doi.org/10.11591/ijpeds.v6.i4.pp665-682.

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Power electronic circuit simulation today has become increasingly more demanding in both the speed and accuracy. Whilst almost every simulator has its own advantages and disadvantages, co-simulations are becoming more prevalent. This paper provides an overview of the co-simulation capabilities of device-level circuit simulators. More specifically, a listing of device-level simulators with their salient features are compared and contrasted. The co-simulation interfaces between several simulation tools are discussed. A case study is presented to demonstrate the co-simulation between a device-level simulator (PSIM) interfacing a system-level simulator (Simulink), and a finite element simulation tool (FLUX). Results demonstrate the necessity and convenience as well as the drawbacks of such a comprehensive simulation.
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Alberto Canesin, Carlos, Flávio Alessandro Serrão Gonçalves, and Leonardo Poltronieri Sampaio. "Simulation Tools For Power Electronics Courses, Based In Java Technologies." Eletrônica de Potência 13, no. 2 (May 1, 2008): 91–98. http://dx.doi.org/10.18618/rep.2008.2.091098.

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Noureldin, Kareem, Tobias Hirsch, Bijan Nouri, Zeyad Yasser, and Robert Pitz-Paal. "Evaluating the Potential Benefit of Using Nowcasting Systems to Improve the Yield of Parabolic Trough Power Plants with Single-Phase HTF." Energies 14, no. 3 (February 2, 2021): 773. http://dx.doi.org/10.3390/en14030773.

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Solar field developers include innovative solutions to optimize the energy production of their plants. Simulation tools play a significant role in the design and testing phases as they provide estimations of this yield in different conditions. Transient processes, like passing clouds and solar field start-up, are specifically challenging to optimize and estimate using such simulation tools. Solar fields are subject to high degree of both temporal and spatial variability in the energy input and a detailed estimation can be achieved by simulating subsystems within acceptable time and computational power. Hence, such simulation tools cannot be utilized for tests under realistic operation conditions. The Virtual Solar Field is a computationally efficient simulation tool that allows a detailed transient simulation of parabolic trough solar fields based on single-phase fluids. Using this tool, developers could reproduce a transient test case with exactly the same disturbances to provide fair comparisons between different configurations. In this paper, an evaluation process based on numerical simulations using the Virtual Solar Field is presented. The economic benefit of novel innovative control concepts can be assessed according to the presented scheme. This is demonstrated by evaluating the potential benefit of availability of spatial DNI nowcasts on the control of parabolic trough solar fields. Results show that nowcasting can increase the economic revenue of commercial power plants by up to 2.5% per day. This proves the feasibility of installing such systems.
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JØRGENSEN, KATARINA M., and PAULINE C. HADDOW. "VISUALIZATION IN SIMULATION TOOLS: REQUIREMENTS AND A TOOL SPECIFICATION TO SUPPORT THE TEACHING OF DYNAMIC BIOLOGICAL PROCESSES." Journal of Bioinformatics and Computational Biology 09, no. 04 (August 2011): 579–95. http://dx.doi.org/10.1142/s0219720011005410.

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Simulation tools are playing an increasingly important role behind advances in the field of systems biology. However, the current generation of biological science students has either little or no experience with such tools. As such, this educational glitch is limiting both the potential use of such tools as well as the potential for tighter cooperation between the designers and users. Although some simulation tool producers encourage their use in teaching, little attempt has hitherto been made to analyze and discuss their suitability as an educational tool for noncomputing science students. In general, today's simulation tools assume that the user has a stronger mathematical and computing background than that which is found in most biological science curricula, thus making the introduction of such tools a considerable pedagogical challenge. This paper provides an evaluation of the pedagogical attributes of existing simulation tools for cell signal transduction based on Cognitive Load theory. Further, design recommendations for an improved educational simulation tool are provided. The study is based on simulation tools for cell signal transduction. However, the discussions are relevant to a broader biological simulation tool set.
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Ikeda, Makoto, Leonard Barolli, Giuseppe De Marco, Tao Yang, Arjan Durresi, and Fatos Xhafa. "Tools for Performance Assessment of OLSR Protocol." Mobile Information Systems 5, no. 2 (2009): 165–76. http://dx.doi.org/10.1155/2009/782684.

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In this paper, we evaluate the performance of Optimized Link State Routing (OLSR) protocol by experimental and simulation results. The experiments are carried out by using our implemented testbed and the simulations by using ns-2 simulator. We also designed and implemented a new interface for the ad-hoc network testbed in order to make more easier the experiments. The comparison between experimental and simulation results shows that for the same parameters set, in the simulation we did not notice any packet loss. On the other hand, in the experiments we experienced packet loss because of the environment effects and traffic interference.
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Pawlewski, Pawel, Paulina Golinska, and Paul-Eric Dossou. "Application potential of Agent Based Simulation and Discrete Event Simulation in Enterprise integration modelling concepts." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 1, no. 1 (July 1, 2013): 33–42. http://dx.doi.org/10.14201/adcaij2012113342.

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This paper aims to present the dilemma of simulation tool selection. Authors discuss the examples of methodologies of enterprises architectures (CIMOSA and GRAI) where agent approach is used to solve planning and managing problems. Actually simulation is widely used and practically only one tool which can enable verification of complex systems. Many companies face the problem, which simulation tool is appropriate to use for verification. Selected tools based on ABS and DES are presented. Some tools combining DES and ABS approaches are described. Authors give some recommendation on selection process.
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Liu, Xian Li, Hong Min Pen, Tao Chen, Fu Gang Yan, Yu Wang, and Jing Shu Hu. "Effect of Different Edge Preparation on High Speed Turning Hardened Steel Process." Materials Science Forum 532-533 (December 2006): 412–15. http://dx.doi.org/10.4028/www.scientific.net/msf.532-533.412.

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Through combining turning experiments and FE simulations, this paper studied the effects of force, temperature and residual stress of machined surface on high speed hard turning GCr15 bearing steel hardened to HRC60-62 with three kinds of ordinary edge preparation (sharp-edge, hone and chamfer). The experiment and simulation results indicated that the diathermanous proportion of chamfered edge preparation to tool and machined surface is less, and this distribution of cutting temperatures is useful for tool life and machined surface quality. The simulation results showed that cutting force had a descending tendency with increasing of cutting speed, which is in accordance with the change rules of machining general rigidity material, and it proved that FE simulations have good precision. The simulation results of residual stress of machined surface showed that residual tensile stress existed in machined surface using both honed and chamfered tools, and a highest compressive stress (about -200MPa) existed among 150-200μm of the depth into the workpiece surface. The difference was that the depth of superficial harden layer with honed tools is larger than that with chamfered tools.
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Das, Anjana P., and Sabu M. Thampi. "Simulation Tools for Underwater Sensor Networks: A Survey." Network Protocols and Algorithms 8, no. 4 (January 15, 2017): 41. http://dx.doi.org/10.5296/npa.v8i4.10471.

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In underwater sensor network(UWSN) research, it is highly expensive to deploy a complete test bed involving complex network structure and data links to validate a network protocol or an algorithm. This practical challenge points to the need of a simulation environment which can reproduce the actual underwater scenario without the loss of generality. Since so many simulators are proposed for UWSN simulation, the selection of an appropriate tool based on the research requirement is very important in validation and interpretation of results. This paper provides an in-depth survey of different simulation tools available for UWSN simulation. We compared the features offered by each tool, pre-requirements, and provide the run time experiences of some of the open source tools. We conducted simulation of sample scenarios in some of the open source tools and compared the results. This survey helps a researcher to identify a simulation tool satisfying their specific research requirements.
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MIN, BYUNG-KWON, ZHENGDONG HUANG, ZBIGNIEW J. PASEK, DEREK YIP-HOI, FORBES HUSTED, and STEPHEN MARKER. "INTEGRATION OF REAL-TIME CONTROL SIMULATION TO A VIRTUAL MANUFACTURING ENVIRONMENT." Journal of Advanced Manufacturing Systems 01, no. 01 (June 2002): 67–87. http://dx.doi.org/10.1142/s0219686702000076.

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This paper presents a new integrated approach for simulation developed to improve the accuracy of virtual manufacturing environments. While machine tool simulation and virtual manufacturing for factory simulation have been frequently used in early stage plant development, each of these technique has been researched and implemented separately. This paper focuses on the utilization of real-time simulation of machine tools or active axes in manufacturing systems and integration of this simulation capability with virtual manufacturing environments. Machine-level simulation results are generated in real-time with a real machine tool controller and are fed to a virtual manufacturing environment. To integrate these two simulation techniques, system-level software is utilized as a communication platform. This system-level software was originally developed to control and configure whole manufacturing systems. The method has been successfully implemented within a testbed with full-scale machine tools. The results demonstrate that the proposed method advances the virtual manufacturing environments toward improved accuracy of factory level simulation, reduced effort for modeling and expanded functionality of machine-level simulations.
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Nie, Xue Jun. "Modeling and Simulation for Feed Servo System in CNC Machine Tool." Advanced Materials Research 510 (April 2012): 345–49. http://dx.doi.org/10.4028/www.scientific.net/amr.510.345.

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Feed servo system is an important part in CNC machine tool. In order to promote CNC machine tools precision, it is particularly important to enhance the control methods research on feed servo system. In this paper, different mathematical models of CNC machine tools feed servo system in different control modes are established, and their transfer functions are obtained, also the corresponding simulation models are constructed by using Simulink tools under MATLAB environment. Through simulating, the relationship between the systems parameters and the performance of servo system are obtained, some reliable theoretical basis can be achieved for improving and enhancing CNC machine tool servo system performance. Therefore some references for the performance optimization of feed servo system in CNC machine tool are provided.
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Reeve, Samuel Temple, David M. Guzman, Lorena Alzate-Vargas, Benjamin Haley, Peilin Liao, and Alejandro Strachan. "Online simulation powered learning modules for materials science." MRS Advances 4, no. 50 (2019): 2727–42. http://dx.doi.org/10.1557/adv.2019.287.

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Simulation tools are playing an increasingly important role in materials science and engineering and beyond their well established importance in research and development, these tools have a significant pedagogical potential. We describe a set of online simulation tools and learning modules designed to help students explore important concepts in materials science where hands-on activities with high-fidelity simulations can provide insight not easily acquired otherwise. The online tools, which involve density functional theory and molecular dynamics simulations, have been designed with non-expert end-users in mind and only a few clicks are required to perform most simulations, yet they are powered by research-grade codes and expert users can access advanced options. All tools and modules are available for online simulation in nanoHUB.org and access is open and free of charge. Importantly, instructors and students do not need to download or install any software. The learning modules cover a range of topics from electronic structure of crystals and doping, plastic deformation in metals, and physical properties of polymers. These modules have been used in several core undergraduate courses at Purdue’s School of Materials Engineering, they are self contained, and are easy to incorporate into existing classes.
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Khan, Muhammad A. A., and Anwar K. Sheikh. "Virtual Casting: State of the Art in Metal Casting Simulation Tools." Journal of Engineering Research [TJER] 15, no. 2 (November 7, 2018): 142. http://dx.doi.org/10.24200/tjer.vol15iss2pp142-154.

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The demands on the productivity and robustness of metal casting processes for high quality components are continuously increasing. Moreover, the financial considerations necessitate meticulous and reliable planning of the entire casting process before it is actually put into practice. A holistic approach to perform cradle to grave analysis of cast products is simulation-based metal casting. This method allows engineers to model, verify, and validate the process followed by its optimization and performance prediction in virtual reality. This paper provides insights on state of the art in simulation-based metal casting with reference to some case studies. Casting simulations software, mathematical models and solution methods, and casting process simulation together with the results obtained are clearly explained. The current practices revealed extensive utilization of simulation packages for defect minimization, yield maximization, and improved quality. The ongoing research on integration of casting simulations with mechanical performance simulations makes it possible to analyze the serviceability of cast parts. The reliability of cast part in service with dynamic loading of varying thermal and mechanical load cycles can be predicted through this integration. However, more rigorous work is needed in this area, particularly by developing the reliability prediction modules embedded in advanced simulation tools.
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43

Khan, Muhammad A. A., and Anwar K. Sheikh. "Virtual Casting: State of the Art in Metal Casting Simulation Tools." Journal of Engineering Research [TJER] 15, no. 2 (November 7, 2018): 42. http://dx.doi.org/10.24200/tjer.vol15iss2pp42-54.

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Abstract:
The demands on the productivity and robustness of metal casting processes for high quality components are continuously increasing. Moreover, the financial considerations necessitate meticulous and reliable planning of the entire casting process before it is actually put into practice. A holistic approach to perform cradle to grave analysis of cast products is simulation-based metal casting. This method allows engineers to model, verify, and validate the process followed by its optimization and performance prediction in virtual reality. This paper provides insights on state of the art in simulation-based metal casting with reference to some case studies. Casting simulations software, mathematical models and solution methods, and casting process simulation together with the results obtained are clearly explained. The current practices revealed extensive utilization of simulation packages for defect minimization, yield maximization, and improved quality. The ongoing research on integration of casting simulations with mechanical performance simulations makes it possible to analyze the serviceability of cast parts. The reliability of cast part in service with dynamic loading of varying thermal and mechanical load cycles can be predicted through this integration. However, more rigorous work is needed in this area, particularly by developing the reliability prediction modules embedded in advanced simulation tools.
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44

Ettikyala, Kalpana, and Y. Rama Devi. "A Study on Cloud Simulation Tools." International Journal of Computer Applications 115, no. 14 (April 22, 2015): 18–21. http://dx.doi.org/10.5120/20220-2500.

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45

Hric, Slavomir, Alena Vagaska, Svetlana Radchenko, Filip Murgas, and Milan Micko. "Product Quality Improvement Using Simulation Tools." Applied Mechanics and Materials 718 (December 2014): 77–82. http://dx.doi.org/10.4028/www.scientific.net/amm.718.77.

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The article deals with the basic technological aspects of injection moulding, pointing to the importance of correct product geometry design. Large differences in the wall thickness of the mould result in uneven cooling, material shrinkage, and the formation of internal stress. This ultimately may give rise to deformations, such as those in form sink marks, and may affect the required quality of the mould. The article describes the application of a specific example of one of the ways of correction of the mould wall thickness used in technological practice of the injection moulding technology. This is a case of employing the method of the so-called relief pins. After the introduction of the proposed modification of the form, the mould quality has improved, thereby increasing the production efficiency in the process of shape optimization of manufacturing the product called Installation Box Series S-BOX 106 made of polyamide PA 66. In addressing the issue, simulation of cooling plastic and injection pressure in Cadmold 3D-f software was employed.
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46

Worobel, Richard, Juraj Capek, Lucia Kovacova, Peter Bubenik, and Martin Krajcovic. "IMPROVING BUSINESS PROCESSES USING SIMULATION TOOLS." MM Science Journal 2018, no. 01 (March 7, 2018): 2244–51. http://dx.doi.org/10.17973/mmsj.2018_03_2017103.

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47

Morita, Youhei. "Validation of the MONARC simulation tools." Computer Physics Communications 140, no. 1-2 (October 2001): 153–61. http://dx.doi.org/10.1016/s0010-4655(01)00266-1.

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48

Bramley, A. N., and D. J. Mynors. "The use of forging simulation tools." Materials & Design 21, no. 4 (August 2000): 279–86. http://dx.doi.org/10.1016/s0261-3069(99)00090-4.

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49

Siregar, Ikhsan, Juri Bin Saedon, and Mohd Shariman Adenan. "Cutting tools simulation with different parameter." IOP Conference Series: Materials Science and Engineering 505 (July 4, 2019): 012042. http://dx.doi.org/10.1088/1757-899x/505/1/012042.

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50

Donn, Michael. "Tools for quality control in simulation." Building and Environment 36, no. 6 (July 2001): 673–80. http://dx.doi.org/10.1016/s0360-1323(00)00059-7.

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