Discrete Event Simulation (DES) project data management is a complex and important engineering activity which impacts on an organization’s efficiency. This efficiency could be decreased by the lack of provenance information or the unreliability of existing information regarding previous simulation projects, all of which complicates the reusability of the existing data. This study presents an analysis of the management of simulation projects and their provenance data, according to the different types of scenarios usually found at a manufacturing plant. A survey based on simulation projects at an automotive manufacturing plant was conducted, in order to categorize the information regarding the studied projects, map the available provenance data and standardize its management. This study also introduces an approach that demonstrates how a structured framework based on the specific data involved in the different types of scenarios could allow an improvement of the management of DES projects.
Discrete Event Simulation is a comprehensive tool for the analysis and design of manufacturing systems. Over the years, considerable efforts to improve simulation processes have been made. One step in these efforts is the standardisation of the output data through the development of an appropriate system which presents the results in a standardised way. This paper presents the results of a survey based on simulation projects undertaken in the automotive industry. In addition, it presents the implementation of an automated output data-handling system which aims to simplify the project’s documentation task for the simulation engineers and make the results more accessible for other stakeholders.
Previous research has highlighted the role of virtual engineering tools in the development of manufacturing machinery systems. Simulation models created for this purpose can potentially be used to provide support for other tasks, such as operational planning and service and maintenance. This requires that the simulation models can be fed with historic data as well as with snapshot data. Furthermore, the models must be able to communicate with other business software. The paper describes how simulation models can be used for operational production planning and for service and maintenance support. Benefits include a better possibility to verify production plans and the possibility to monitor and service manufacturing machinery from remote locations. Furthermore, the expanded and continuously updated models provide a good tool to study the effect of, for instance, planned new product introduction in existing manufacturing systems. The paper also presents directions for future research. One ambition is to add AI tools to the system so as to develop a semi-autonomous system for decision support
The paper describes manufacturing simulation with a focus on discrete event simulation and computer aided robotics. Some generic good practices, problems, and pitfalls in the use of simulation are described. Some advanced applications of manufacturing simulation are described and elucidated on the hand of a system for simulation-based service & maintenance. Simulation-based decision support and information fusion are closely related, and plans for novel synergistic research in these area are presented
Lean and simulation analysis are driven by the same objective, how to better design and improve processes making the companies more competitive. The adoption of lean has been widely spread in companies from public to private sectors and simulation is nowadays becoming more and more popular. Several authors have pointed out the benefits of combining simulation and lean, however, they are still rarely used together in practice. Optimization as an additional technique to this combination is even a more powerful approach especially when designing and improving complex processes with multiple conflicting objectives. This paper presents the mutual benefits that are gained when combining lean, simulation and optimization and how they overcome each other´s limitations. A framework including the three concepts, some of the barriers for its implementation and a real-world industrial example are also described.
Reuse of virtual engineering models and simulations improves engineering efficiency. Reuse requires preserving the information provenance. This paper suggests a framework based on the 7W data provenance model to be part of simulation data management implemented in product lifecycle management systems. The resulting provenance framework is based on a case study in which a product was re-engineered using finite element analysis.
Ett examensarbete har utförts på ett företag där trädetaljer tillverkas. Arbetet gick ut på att förbättra processen vid tillverkning av glaspartier. Företaget är ledande i Skandinavien beträffande sina träprodukter. Tillverkningen sker till stor grad av helautomatiserad produktion men har fortfarande några manuella stationer kvar. Företaget omsätter omkring 450 miljoner kronor per år och har cirka 70 anställda.
Syftet med examensarbetet var att förbättra processen vid produktion av glaspartier för att reducera den totala operationstiden och minska interna kvalitetsförluster. Huvudmålen var att reducera interna kvalitetsförluster med 10 % samt att reducera operationstiderna med 15 %. För att uppnå målen valdes verktygen go to gemba, frekvensstudie, tidsstudie samt spagettidiagram för att hitta olika slöserier i produktionen och kartlägga operationstiderna. Metod valdes genom att läsa på om de olika verktygen och jämföra dem mot andra verktyg i den teoretiska referensramen.
Det visade sig att det fanns en del slöserier i processen. Framför allt en hel del onödiga rörelser för både material och för operatörer. Det fanns också många olika lager som varierade i väntetid för det bearbetade materialet under flödet samt en hel del manuellt arbete som företaget helst ville bygga bort. Vid undersökning av nuläget sågs det också att företaget saknade ett standardiserat arbete samt ett 5S arbete i flödet av glaspartier.
Genom en summering av nuläget kunde ett förbättringsförslag läggas fram. En del av förbättringsförslaget var att bygga bort det manuella arbetet genom att införskaffa en maskin som byggs in i den automatiserade karmlinan. Det leder till mindre kvalitetsförluster, men också färre lager eftersom glaspartitillverkningen kommer att ske i karmlinan. En annan del av förbättringsförslaget var att bygga in slutmonteringen i den befintliga karmlinan där andra liknande produkter redan tillverkades. Som nuläget såg ut var slutmonteringen en separat process vid sidan av karmlinan. Det gjorde att produkterna lades på vagnar och ställdes på ett lager för att sedan ha en lång väntetid innan de monterades. Genom att bygga in slutmonteringen i karmlinan kommer företaget att montera glaspartierna i karmlinan utan onödiga lager. Ett sista förbättringsförslag var att implementera 5s och standardiserat arbetssätt över hela tillverkningen av glaspartier. Det troliga utfallet av förbättringsförslagen beräknades på reducerad operationstid med 59 % och reducerade interna kvalitetsförluster med 80 %
Over the past years, it has become more common in industry to use virtual manufacturing applications such as robotic simulations, FEM-analysis and factory flow simulation in various stages of the development of products and production facilities. As a result, today’s industry struggles with providing sufficient data for the growing virtual manufacturing activities, especially in situations where multiple subcontractors and project partners are involved. However, there have seldom been any specific requirements on the data shared by these engineering tools. This paper describes a project for maintaining context, semantic and structure on information acquired by VMS applications in an adequate way. The use of standards associated with this kind of work is strongly advocated. Therefore, standards covering different areas have to be considered and combined in order to form a solid foundation for the project.
Virtual engineering increases the rate of and diversity of models being created; hence requires maintenance in a product lifecycle management (PLM) system. This also induces the need to understand their creation contexts, known as historical or provenance information, to reuse the models in other engineering projects. PLM systems are specifically designed to manage product- and production-related data. However, they are less capable of handling the knowledge about the contexts of the models without an appropriate extension. Therefore, this research proposes an extension to PLM systems by designing a new information model to contain virtual models, their related data and knowledge generated from them through various engineering activities so that they can be effectively used to manage historical information related to all these virtual factory artifacts. Such an information model is designed to support a new Virtual Engineering ontology for capturing and representing virtual models and engineering activities, tightly integrated with an extended provenance model based on the W7 model. In addition, this paper presents how an application prototype, called Manage-Links, has been implemented with these extended PLM concepts and then used in several virtual manufacturing activities in an automotive company.
Discrete event simulation (DES) models imitates the behavior of a production system. Models can be developed to reflect different levels of the production system, e.g supply chain level or manufacturing line level. Product Lifecycle Management (PLM) systems have been developed in order to manage product and manufacturing related data. DES models is one kind of product lifecycle’s data which can be managed by a PLM system. This paper presents a method and its implementation for management of interacting multi-level models utilizing a PLM system.
Saving and managing virtual models’ provenance information (models’ history) can increase the level of reusability of those models. This paper describes a provenance management system (PMS) that has been developed based on an industrial case study.
The product lifecycle management (PLM) system, as a main data management system, is responsible for receiving virtual models and their related data from Computer-Aided technologies (CAx) and providing this information for the PMS. In this paper, the management of discrete event simulation data with the PLM system will be demonstrated as the first link of provenance data management chain (CAx-PLM-PMS).
Shortening of the product development process time is one of the main approaches for all enterprises to offer their products to the market. Virtual manufacturing tools can help companies to reduce their time to market, by reduction of the engineering lead time. Extensive use of virtual engineering models results in a need for verification of the model’s accuracy. This virtual engineering usability and assessment have been named virtual confidence. The two main factors of the achievement of this confidence are the accuracy of the virtual models and the virtual engineering results.
For controlling of both above factors, a complete virtual model and related virtual model knowledge are needed. These knowledges can be tacit or explicit. For exploring explicit knowledge, a data and information collection from different disciplines in the organization is needed.
In this paper, a data map with focus on the manufacturing engineering scope will be presented. This data map is generated from different data sources at a manufacturing plant, and gives an overview of different data that exist at different data sources, in the area of manufacturing. Combining real world data from different sources with virtual engineering model data supports, amongst others, establishment of virtual confidence.
Product lifecycle management (PLM) systems maintain amongst others the specifications and designs of product, process and resource artefacts and thus serve as the basis for realizing the concept of Virtual Manufacturing, and play a vital role in shortening the leadtimes for the engineering processes. Design of new products requires numerous experiments and test-runs of new facilities that delays the product release and causes high costs if performed in the real world. Virtualization promises to reduce these costs by simulating the reality. However, the results of the simulation must predict the real results to be useful. This is called virtual confidence. We propose a knowledge base approach to capture and maintain the virtual confidence in simulation results. To do so, the provenance of results of real, experimental and simulated processes are recorded and linked via confirmation objects.
This paper discusses the expected benefits of using linked data for the tasks of gathering, managing and understanding the data of smart factories. It has the further specific focus of using this data to maintaining a Digital Twin for the purposes of analysis and optimisation of such factories. The proposals are motivated by the use of an industrial example looking at the types of information required, the variation in data which is available and the requirements of an analysis platform to provide parameters for seamless, automated simulation and optimisation.