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| 2. |
- Berglund, Jonatan, 1983, et al.
(author)
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Energy Efficiency Analysis for a Casting Production System
- 2011
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In: Proceedings of the 2011 Winter Simulation Conference. - 9781457721090 ; 2011, s. 1060-1071
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Conference paper (peer-reviewed)abstract
- A growing number of manufacturing industries are initiating efforts to address sustainability issues. A study by the National Association of Manufacturers indicated that the manufacturing sector currently accounts for over a third of all energy consumed in the United States. There are many areas and opportunities to reduce energy costs and pollution emissions within a manufacturing facility. One way to achieve an energy efficient manufacturing system is to measure and evaluate the combined impact of process energy from manufacturing operations, their resources (e.g., plant floor equipment), and facility energy from building services (e.g., ventilation, lighting). In this paper, issues associated with integrating production system, process energy, and facility energy to improve manufacturing sustainability are explored. A modeling and simulation case study of analyzing energy consumption in a precision casting operation is discussed.
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| 4. |
- Johansson, Björn, 1975, et al.
(author)
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Enabling Flexible Manufacturing Systems by Using Level of Automation as Design Parameter
- 2009
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In: Proceedings - Winter Simulation Conference. - 0891-7736. - 9781424457700 ; , s. 2176-2184
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Conference paper (peer-reviewed)abstract
- Handling flexibility in an ever changing manufacturing environment is one of the key challenges for a successful industry. By using tools for virtual manufacturing, industries can analyze and predict outcomes of changes before taking action to change the real manufacturing systems. This paper describes a simulation tool that can be used to study the effect of level of automation issues on the design of manufacturing systems, including their effect on the overall system performance, ergonomics, environment, and economic measures. Determining a suitable level of automation can provide a manufacturing system with the flexibility needed to respond to the unpredictable events that occur in factory systems such as machine failures, lack of quality, lack of materials, lack of resources, etc. In addition, this tool is designed to use emerging simulation standards, allowing it to provide a neutral interface for both upstream and downstream data sources.
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| 7. |
- Johansson, Marcus, 1992, et al.
(author)
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A TEST IMPLEMENTATION OF THE CORE MANUFACTURING SIMULATION DATA SPECIFICATION
- 2007
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In: Proceedings of the 2007 Winter Simulation Conference in Washington D.C. USA, December 9-12 2007. ; , s. 1673-1681
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Conference paper (peer-reviewed)abstract
- This paper describes an effort of testing the Core Manufacturing Simulation Data (CMSD) information model as a neutral data interface for a discrete event simulation model developed using Enterprise Dynamics. The implementation is based upon a model of a paint shop at a Volvo Car Corporation plant in Sweden. The model is built for a Swedish research project (FACTS), which focuses on the work procedure of developing new and modified production systems. FACTS has found standardized simulation data structures to be of high interest to achieve efficient data collection in conceptual stages of production development programs. For the CMSD-development team, implementations serve as an approach to validate the structures in CMSD and to gather requirements for future enhancements. CMSD was originally developed to support job shops, but the results of this implementation indicate a good possibility to extend CMSD to also support flow shops.
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| 9. |
- Leong, Swee, et al.
(author)
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A real world pilot implementation of the Core Manufacturing Simulation Data model
- 2008
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In: ; , s. 334-341
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Conference paper (peer-reviewed)abstract
- While software for discrete event simulation (DES) has emerged into sophisticated tools for decision support in a wide range of contexts, the need to integrate DES tools with other applications is increasing. In the industrial engineering context, simulation engineers strive to use real world data, e.g., logs of machine breakdown, to make behavior of DES models imitate reality. However, the format used for describing simulation data is often specialized to the current situation. The Core Manufacturing Simulation Data (CMSD) is a collaborative effort with academia and industry to standardize the format used for simulation data, to facilitate data exchange among simulation and manufacturing applications. This paper describes the results from a pilot implementation study at Volvo Trucks, where CMSD was utilized as the data exchange format between two data systems and two DES models. The DES tools used were commercial software packages Unigraphics Plant Simulation and InControl Enterprise Dynamics. Generic and reusable interfaces for CMSD-file communication were developed for each of these tools. The CMSD interfaces were successfully connected to a model in each simulation tool describing the same manufacturing process. A stand alone application was developed to collect and analyze raw data and to create the CMSD file being used as input data for both models. The result is a system including raw data analysis, data reformatting, CMSD interfacing, and model execution. Based on the result, a generic methodology for CMSD interface development in DES tools has evolved. The most important conclusion is that CMSD data can be interpretable by both Plant Simulation and Enterprise Dynamics, and that it saves engineering development time during the model building phase.
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| 10. |
- Lu, Roberto, et al.
(author)
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Implementation of Core Manufacturing Simulation Data in Aerospace Industry
- 2008
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In: Winter simulation conference.
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Conference paper (other academic/artistic)abstract
- This presentation will describe how Boeing implements the Core Manufacturing Simulation Data (CMSD) information model. The presentation will include visualization of manufacturing data for parts and tooling visibility in manufacturing industry. Simulation analyses are prepared by extracting and formatting data from real world production databases. This includes automatic data processing, automatic curve fitting for distribution functions, automatic CMSD file generation, and finally data is automatically fed to simulations.
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