Guodong Shao

A test implementation of the core manufacturing simulation data specification

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.

Input data management methodology for discrete event simulation

Input Data Management (IDM) is a time consuming and costly process for Discrete Event Simulation (DES) projects. In this paper, a methodology for IDM in DES projects is described. The approach is to use a methodology to identify and collect data, then use an IDM software to extract and process the data. The IDM software will structure and present the data in Core Manufacturing Simulation Data (CMSD) format, which is aimed to be a standard data format for any DES software. The IDM methodology was previously developed and tested by Chalmers University of Technology in a case study in the automotive industry. This paper presents a second test implementation in a project at the National Institute of Standards and Technology (NIST) in collaboration with an aerospace industry partner.

A Virtual Machining Model for Sustainability Analysis

Sustainability has become a very significant research topic since it impacts many different manufacturing industries. The adoption of sustainable manufacturing practices and technologies offers industry a cost effective route to improve economic, environmental, and social performance. As a major manufacturing process, the machining system plays an important role for sustainable manufacturing on the factory floor. Therefore, technologies for monitoring, analyzing, evaluating, and optimizing the sustainability impact of machining systems are critical for decision makers. Modeling and Simulation (M&S) can be an effective tool for success of sustainable manufacturing through its ability to predict the effect of implementing a new facility, process without interrupting real production. This paper introduces a methodology that provides a traditional virtual Numerical Control (NC) machining model with a new capability — to quantitatively analyze the environmental impact of machining system based on Life Cycle Assessment (LCA). The objective of the methodology is to analyze the sustainability impacts of machining process and determine a better plan for improving the sustainable performance of machining system in a virtual environment before work orders are released to the shop floor. Testing different scenarios with simulation models ensures the best setting option available can be chosen. The virtual NC model provides the necessary data for this assessment. In this paper, a list of environmental impact indicators and their metrics has been identified, and modeling elements for sustainable machining have been discussed. Inputs and outputs of the virtual machining model for sustainable machining are described. A case study to experiment the proposed methodology is discussed.

Data analytics using simulation for smart manufacturing

Manufacturing organizations are able to accumulate large amounts of plant floor production and environmental data due to advances in data collection, communications technology, and use of standards. The challenge has shifted from collecting a sufficient amount of data to analyzing and making decisions based on the huge amount of data available. Data analytics (DA) can help understand and gain insights from the big data and in turn help advance towards the vision of smart manufacturing. Modeling and simulation have been used by manufacturers to analyze their operations and support decision making. This paper proposes multiple methods in which simulation can serve as a DA application or support other DA applications in manufacturing environment to address big data issues. An example case is discussed to demonstrate one use of simulation. In the presented case, a virtual representation of machining operations is used to generate the data required to evaluate manufacturing data analytics applications.

Process analytics formalism for decision guidance in sustainable manufacturing

This paper introduces National Institute of Standards and Technology (NIST)’s Sustainable Process Analytics Formalism (SPAF) to facilitate the use of simulation and optimization technologies for decision support in sustainable manufacturing. SPAF allows formal modeling of modular, extensible, and reusable process components and enables sustainability performance prediction, what-if analysis, and decision optimization based on mathematical programming. SPAF models describe (1) process structure and resource flow, (2) process data, (3) control variables, and (4) computation of sustainability metrics, constraints, and objectives. This paper presents the SPAF syntax and formal semantics, provides a sound and complete algorithm to translate SPAF models into formal mathematical programming models, and illustrates the use of SPAF through a manufacturing process example.

Simulation of shipbuilding operations

This paper discusses the objectives and requirements for a shipbuilding simulation. It presents an overview of a generic simulation of shipbuilding operations. The shipbuilding simulation model can be used as a tool to analyze the schedule impact of new workload, evaluate production scenarios, and identify resource problems. The simulation helps identify resource constraints and conflicts between competing jobs. The simulation can be used to show expected results of inserting new technologies or equipment into the shipyard, particularly with respect to operating costs and schedule impact. The use of DOD High Level Architecture (HLA) and Run Time Infrastructure (RTI) as an integration mechanism for distributed simulation is also discussed briefly.

Energy efficiency analysis for a casting production system

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.

Interoperability for simulation of sustainable manufacturing

Sustainability has become a very significant research topic, it impacts many different manufacturing industries. To achieve sustainable manufacturing, designing manufacturing systems that have less negative impact on the environment is critical. Modeling and Simulation (M&S) is an essential ingredient for success of sustainable manufacturing through its ability to predict the effect of implementing certain facility, process, and product actions. M&S for sustainable manufacturing requires the exchange of information with a variety of manufacturing systems, applications, and databases. M&S also provides appropriate models and methods to tailor algorithms for performing accurate transformations of the data. This paper introduces a methodology to support interoperability among simulation tools and other manufacturing systems that support sustainability. The methodology describes how to collect and process the identified data in order to make it reusable for M&S studies to achieve sustainable manufacturing. Three potential case studies are identified and discussed as a scope for future work.

Virtual factory revisited for manufacturing data analytics

Development of an effective data analytics application for manufacturing requires testing with large sets of data. It is usually difficult for application developers to find access to real manufacturing data streams for testing new data analytics applications. Virtual factories can be developed to generate the data for selected measures in formats matching those of real factories. The vision of a virtual factory has been around for more than a couple decades. Advances in technologies for computation, communication, and integration and in associated standards have made the vision of a virtual factory within reach now. This paper discusses requirements for a virtual factory to meet the needs of manufacturing data analytics applications. A framework for the virtual factory is proposed that leverages current technology and standards to help identify the developments needed for the realization of virtual factories.

Benchmarking production system, process energy, and facility energy performance using a systems approach

Evaluating overall energy performance of a manufacturing system requires accurate information on how, when, and where energy is being used. Collecting and tracking energy data is necessary for determining performance benchmarks and reducing energy consumption. Optimizing energy efficiency in manufacturing systems is difficult to achieve since energy management is typically performed separately from the production monitoring and control systems. Further, low-level equipment energy data collection is costly to do, and, if done, is often not well-linked to production data. The smarter integration of production system, process energy, and facility energy data is a significant opportunity to improve manufacturing sustainability. This paper will examine the issues related to the linking of these three types of data as well as develop a methodology for jointly modeling and evaluating production, process energy, and facility energy performance. A case study of a sand casting production line will be discussed to better understand the integration issues, validate the methodology, test performance benchmarks, and investigate sustainable manufacturing opportunities.