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Virtual Prototype

Autodesk, Autodesk Inventor

The Autodesk® Inventor® product line provides a comprehensive and flexible set of software for 3D mechanical design, product simulation, tooling creation, and design communication that help you cost-effectively take advantage of a Digital Prototyping workflow to design and build better products in less time. http://usa.autodesk.com/adsk/servlet/index?siteID=123112&id=4246282

CGV, Generative Modelling Language (GML)

The term ‘generative modeling’ describes a paradigm change in shape description, the generalization from objects to operations: A shape is described by a sequence of processing steps, rather than just the end result of applying operations. Shape design becomes rule design. http://www.generative-modeling.org/

Fraunhofer, Functional DMU

In order to put mechatronic products on a solid base in an early stage of the development process, an extension of DMU with functional aspects to a Functional DMU (FDMU) and the support of cooperation between the domains of mechanics, electronics and software development is imperative. http://www.igd.fhg.de/igd-a2/fdmu/

Visenso, COVISE CAD

The COVISE CAD extension module provides direct interfaces to all current CAD formats. Thus these data can be imported into the COVISE environment as easily as calculation results. Thereby it is guaranteed that also the structural information of the CAD data stays obtained and can be used directly in the VR environment in particular, in order to focus the visualisation on the essential parts. http://www.visenso.de/index.php?id=309&L=1

VPD (Prof. Shea),  CogMaSh

As an initial implementation, autonomous production of a customized toy maze was achieved including computationally generating alternative part geometry and all required CNC code. Further, a shape grammar representation has been developed for fabrication planning of the toy maze example, evaluated and found viable to robustly represent capabilities of machine tools. Illustrating applicability of semantic web services for a factory scenario is underway by developing a software prototype that represents the demonstration hardware as an aggregation of collaborative semantic web services. Potential product applications include customized consumer products and engineered-to-order robotic parts, modules and robots for both consumer and industrial markets. http://www.pe.mw.tum.de/forschung/projekte/cogmash

Virtual  Enterprise

ORTLOS, A.N.D.I. – A New Digital Instrument for networked creative collaboration

ORTLOS Space Engineering – Ivan Redi, Andrea Redi,  & transdisciplinary team.The first functional prototype of “A New Digital Instrument for creative networked collaboration” has been finished 2005 and already went through some big-scale Use Cases between 2006-2008. The implemented methods consider new approaches to creative trans-disciplinary networked design collaboration in complex, knowledge-based environments – most notably in architectural practice, and A.N.D.I. can be perceived as novel software instruments that make such approaches possible. The Design aspect of A.N.D.I. includes interoperability, platform independence, reusability, concurrency and abstraction. The Technology aspect of A.N.D.I. correlates to the following aspects: the discovery of knowledge from large data collections; providing cooperative support to users in complex query formulation and refinement; access, retrieval, storage and management of large collections of (multimedia) data and knowledge; information integration from multiple heterogeneous data and knowledge sources; behavior and information unity in virtual systems, and reasoning about information under uncertain conditions. OpenSourceCode unter: http://www.ortlos.at

Altair, HiQube

HiQube combines three data management methodologies – Hierarchical, Relational and Multidimensional – within a single, unified database architecture. With the ability to take advantage of the strengths of each methodology within a single database management system, HiQube delivers a high-performance business intelligence (BI) solution that is fast and able to handle heterogeneous datasets that are virtually unlimited in size. Altair will be connectors to run against A.N.D.I. API and toward concurrent engineering platform.  http://www.hiqube.com/Default.aspx?AspxAutoDetectCookieSupport=1

ECS, Engineering Integration Base (EIB)

The Engineering Integration Base, for which new connectors and processors are continuously being developed, places emphasis on data exchange processors and the high level of release elasticity associated with CAD system revisions. http://www.ecs.steyr.com/Engineering_Integration_Base_EIB.174.0.html?&L=1

IICM, Virtual University of Technology

http://www.iicm.tugraz.at/

Virtual Environment

ORTLOS, iVAN – Intelligent Vibrant Ambience Network

ORTLOS Space Engineering – Ivan Redi, Andrea Redi, Georg Flachbart & transdisciplinary team. iVAN – Intelligent Vibrant Ambience Network – is a large-scale instrument of the beyond-the-desktop-era 3D ambient computing for creative networked collaboration. This mixed-reality based space module is highly flexible due to its innovative user-responsive IT components, intelligent control mechanisms, and novel workflow. Enriched by mobile and virtual elements, a broad spectrum of so-called on-demand spatial settings could be possible inside iVAN, thus enabling different kinds of SMEs to substantially improve their innovative performance in prototyping and developing new products or product ideas and, eo ipso, their overall competitiveness in the context of a globalized economy. http://www.ortlos.org

Definitely Affordable Virtual Environment

Dr. Sven Havemann. DAVE stands for Definitely Affordable Virtual Environment. Affordable means that by mostly using standard hardware components we can greatly reduce costs compared to other commercial systems. In addition, by exchanging just the graphics cards every couple of years and less frequently the PCs the latest graphics hardware can be used for an economic price. Optimally, DAVE uses a normal existing room without constructional changes. In 2005 the CGV institute built a new version of the DAVE in Graz, Austria. This type of immersive display proved itself as the best way to explore virtual worlds. http://informatik.tugraz.at/cs/de/aboutus/institutes/cgv/index.html

CogVis, The Cosamed Project

The goal of this project is a reliable and automated computer vision system to enable an independent lifestyle for the elderly and disabled. In contrast to other projects, the system relies solely on computer vision techniques. http://www.cogvis.at/cosamed/sota.html

IMS, iFUN

iFUN projects interactive content onto floor spaces. An integrated camera detects motion and allows for direct interaction with the projected elements. Fully branded, interactive games and spectacular effects are created. http://www.im-solutions.com/

MAT, AlloSphere

Researchers in UCSB’s Media Arts and Technology program, led by Professor JoAnn Kuchera-Morin, have developed an infrastructure that will provide powerful methods for detailed analysis, synthesis, and manipulation of data by integrating multimodal representations of large-scale data with human-scale visualization and interaction techniques in a novel immersive environment. The facility serves as computing research infrastructure in two primary ways: (1) as a platform for driving computing research needed to create the future versions of the AlloSphere with enhanced video, audio, and interaction capabilities, and addressing challenging problems in storage, networking, rendering, software virtualization, real-time simulation, human-computer interaction, and other areas of computing; and (2) as an immersive visualization environment for computing research in areas such as scientific and information visualization, visual analytics, collaborative human-computer interaction, complex design, large-scale performance debugging, cloud computing, quantum computing, and data mining. http://www.mat.ucsb.edu/allosphere/

VRVis, ViFeLoe

VRVis. Virtual Training in Hand Fire Extinguisher Use: Fighting small fires with a hand extinguisher is not as easy as it looks. It is a skill to be trained; no one is a born fire-fighter! Grease fires, burning insulation, or burning file cabinets are just some of the possible situations that have to be treated correctly from the beginning, if a larger fire has to be avoided. Therefore the use of hand extinguishers shall be taught. In many cases such a training is even required by law.  http://www.vrvis.at/projects/running-projects/ViFeLoe

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For the design of highly complex spatial configurations, as aimed at in LINE, we need a completely novel generation of user interfaces – where space itself becomes the medium, rather than the familiar desktop. In fact, this implies a paradigm change: Instead of having to choose operations explicitly, e.g., from a menu, the software should actively offer the operations that are most likely needed to the user. To some degree, the software derives the user’s intent, which requires certain context awareness, but also flexible re-configuration of input devices and interaction modes. An important principle of this form of human-computer-interaction is cognitive transparency: it must always be completely clear to the user what his options and choices are. The system must behave predictably so that user frustration is reduced to an absolute minimum.

This goal can only be achieved by sophisticated graphical means to highlight views, selections, and operations appropriately showing which input channels are mapped to which degrees of freedom of the design that is currently under development. The challenge, however, is to visually show not only data and relations, but also the operations t the system offers or proposes to the user. It must be possible to start a spatial flow simulation literally by only waving one’s hand, the result of which leads to model changes that can be carried out very precisely using tangible interaction devices.

This overall goal can be roughly divided into two parts, a backed context management and a fronted technology the user is in direct contact with, i.e., a diversity of heterogeneous input and output devices that can be reconfigured promptly.

The concepts of a Virtual Environment are based on and related to several approaches developed at institutes and by partners within the LINE network, the main directions of research are listed below:

DAVE (Dr. Havemann, CGV TU Graz)

DAVE stands for Definitely Affordable Virtual Environment. Affordable means that by mostly using standard hardware components we can greatly reduce costs compared to other commercial systems. In addition, by exchanging just the graphics cards every couple of years and less frequently the PCs the latest graphics hardware can be used for an economic price. Optimally, DAVE uses a normal existing room without constructional changes. In 2005 the CGV institute built a new version of the DAVE in Graz, Austria. This type of immersive display proved itself as the best way to explore virtual worlds.

ALLOSPHERE (Prof. Turk, MAT)[1]

Researchers in UCSB’s Media Arts and Technology program, led by Professor JoAnn Kuchera-Morin, have developed an infrastructure that will provide powerful methods for detailed analysis, synthesis, and manipulation of data by integrating multimodal representations of large-scale data with human-scale visualization and interaction techniques in a novel immersive environment. The facility serves as computing research infrastructure in two primary ways: (1) as a platform for driving computing research needed to create the future versions of the AlloSphere with enhanced video, audio, and interaction capabilities, and addressing challenging problems in storage, networking, rendering, software virtualization, real-time simulation, human-computer interaction, and other areas of computing; and (2) as an immersive visualization environment for computing research in areas such as scientific and information visualization, visual analytics, collaborative human-computer interaction, complex design, large-scale performance debugging, cloud computing, quantum computing, and data mining.

iVAN (ARGE LCIG.ORTLOS)[2]

iVAN – Intelligent Vibrant Ambience Network – is a large-scale instrument of the beyond-the-desktop-era 3D ambient computing for creative networked collaboration. This mixed-reality based space module is highly flexible due to its innovative user-responsive IT components, intelligent control mechanisms, and novel workflow. Enriched by mobile and virtual elements, a broad spectrum of so-called on-demand spatial settings could be possible inside iVAN, thus enabling different kinds of SMEs to substantially improve their innovative performance in prototyping and developing new products or product ideas and, eo ipso, their overall competitiveness in the context of a globalized economy.

Other examples of already developed and implemented solutions would be “C3 Classroom” by Visenso, “Virtual Training in Hand Fire Extinguisher Use” by VRVis, “iFUN – Interactive Projections” by IM Solutions, etc.

Connections to Area 1 & Area 2

Issues in Area 3 can be understood as hardware for software applications from Area 2 and content components from Area 1. Based on ambient intelligence by integration of context-awareness with user feedback, an environment “sensitive” to user interaction within a immersive spatial setting, can be prolonged as a new instrument for the knowledge worker of the future. In a brother view all stake holders involved into the real-time design and engineering processes can benefit from the interaction by visual computing in-person or through a collaborative network during their decision making practice. Only by strong interoperability between all three areas, a lifecycle intelligence of networked environments can be established.

[2] More information about iVAN City Lab – http://www.ortlos.org

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In the recent past, the essence of change in design procedures (Change Management) dealing with a huge amount of data and various perspectives typical for knowledge-based environments, has been manifested in social phenomena such as the Open Source movement, the bottom-up approach, the transparency of multiple-authorship, user-driven innovations and the “form follows feedback” principle. This development has called for novel software instruments enabling designers to harness the vast complexity of collaborative networked settings without losing their own point of view.

The focus of this area is on strategies and concepts for creative networked collaboration, as the necessity to manage emerging relational networks between the involved actors and their productive interaction within collaborative design projects more efficiently. The online expert communities from various domains (“connected intelligence”) will be included. The findings may be of valuable experience for future endeavors in related research fields, too.

The approach pursued in this Area is based on the assumption that creativity can be enhanced and enabled by exploiting collaboration, and, in addition, time to market of innovative products, a critical parameter for the success or failure of a business, can be reduced.

Gleizes et al cite three main challenges to engineering systems so they gain the appropriate emergent behaviors for desired functionalities:

• Controlling system behavior at the macro level by focusing on the design of agents at the micro level.
• Providing designers and engineers with the tools, models and guides to develop such systems.
• Validating these systems.

Correspondingly, the methods and techniques we will develop within this Area and which are used in emergent systems design and concurrent engineering may correspond to one or more of the following categories:

• Design methods and methodology that allow designers and engineers to specify a system that exploits emergent behaviors
• Techniques for controlling interactions to facilitate the appearance of desired emergent properties and behaviors, and to prevent occurrence of undesirable behaviors
• Validation techniques

To enable effective knowledge management (KM), knowledge sharing, and concurrent engineering within and between organizations and creative networked enterprises, the following factors and technologies should be explored and adapted to design:

• Knowledge and practice pattern recognition: Mechanisms and tools to identify, for example, usage patterns in the execution of a particular task using some application. These patterns may then be used by application wizards to help users in the execution of such tasks (e.g. provision of the first ten steps) and not having to navigate through complex menus.
• Context- and knowledge-aware applications: Applications that are context-sensitive and can recognize what the user is aiming at. They should hence be able to provide appropriate guidance and make the relevant information available.
• Comprehensive and intelligent product libraries: Intelligent digital catalogues of useful products. They should contain substantial product information (much more than simple geometry) in parametric form. For example, they could contain built-in support for engineering analysis and product configuration, and guidelines for the work implementation of the product.
• Knowledge portals (KM environments): KM environments at an industry level are needed to enable individuals to retrieve shared best practices and experiences. These should ideally be transparent to the users and be accessible for different applications, search services and expertise. Furthermore, they should provide relevant groupware functionality at an industry (e.g. network of experts) level.
• High-level semantic representations through ontologies: These will help with the identification of key concepts and their interrelationships. Ontologies should not be too generic or too large. Life cycle phases, or specific topics should rather be developed in detail. A meta-ontology should be built on top of these to allow interoperability and mapping between these ontologies whenever and wherever it is needed.
• Knowledge-related services: These services should facilitate inter-enterprise KM through simple services such as searching, and sophisticated services. These services may be subscribed to on an as-need basis.

Moreover, knowledge has to be exploited and embodied in autonomous software for learning purposes followed by an improved performance. Agent Technology (AT) has proven to be a promising paradigm that is suitable for modelling and implementing the unification of data mining tasks, as well as for providing autonomous entity models that dynamically incorporate and use existing knowledge.

The concepts of a concurrent environment is based on and related to several approaches developed at institutes and by partners within the LINE network. The main directions of research are listed below:

A.N.D.I. (ARGE LCIG.ORTLOS)[1]

The first functional prototype of “A New Digital Instrument for creative networked collaboration” has been finished 2005 and already went through some big-scale Use Cases between 2006-2008. The implemented methods consider new approaches to creative trans-disciplinary networked design collaboration in complex, knowledge-based environments – most notably in architectural practice, and A.N.D.I. can be perceived as novel software instruments that make such approaches possible. The Design aspect of A.N.D.I. includes interoperability, platform independence, reusability, concurrency and abstraction. The Technology aspect of A.N.D.I. correlates to the following aspects: the discovery of knowledge from large data collections; providing cooperative support to users in complex query formulation and refinement; access, retrieval, storage and management of large collections of (multimedia) data and knowledge; information integration from multiple heterogeneous data and knowledge sources; behavior and information unity in virtual systems, and reasoning about information under uncertain conditions. Methods, strategies and software architecture developed in A.N.D.I. will be departure the point for the project “Concurrent Engineering Platform” within Area 2.

HiQube (Altair)[2]

HiQube combines three data management methodologies – Hierarchical, Relational and Multidimensional – within a single, unified database architecture. With the ability to take advantage of the strengths of each methodology within a single database management system, HiQube delivers a high-performance business intelligence (BI) solution that is fast and able to handle heterogeneous datasets that are virtually unlimited in size. Altair will be connectors to run against A.N.D.I. API and toward concurrent engineering platform.

Connections to Area 1 and Area 3

Area 2 provides methods, strategies and software architecture for actual interoperability between virtual prototype development and visual interactive representation within Virtual environment. The deliverables of Area 2 should be seen as data handling extensions to the Area 1 project that can be used as collaborative pervasive building blocks for a content implementation of multi-physics simulations and digital mock-ups in “Sensitive Space Demonstrator” within Area 3. However, its importance goes beyond the facilitating connectors and interlinking applications. Based on the “human factor” within a dynamic knowledge-based environment, it can be seen as an enabling platform dealing with creative complex distributed collaboration and knowledge management within Virtual Enterprise.

[2] http://www.hiqube.com/

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The importance of virtual prototyping within modern product development is increasing not only in the fields of automotive and aerospace engineering. The approach of developing and validating products in simulation environments takes place in various product lifecycle phases. From the technical point of view the proposed Area 1 “Virtual Prototype” role is by defining a conceptual design tool and introducing procedural, rule-based design language within a real time capable model for development, processing and simulation from a mechatronic point of view.

Going one step ahead, Area 1 focuses on the support through creative transdisciplinary design processes. Here, the developer is disburdened by intuitive, intelligent tools and encouraged to creatively and playfully applying a new set of transdisciplinary methods. Furthermore, the design processes should be opened to be compatible to concurrent engineering and interaction scenarios defined in Area 2, and Area 3.

The research aims of Area 1 “Virtual Prototype” is primarily the conceptual design of a virtual prototype to be implemented into a Sensitive Space demonstrator, with the following technical capabilities:

• The further development of a procedural, rule-based design language to be deployed for the specific user (customer, developer) based on simple objects, where no programming knowledge is needed.
• The exemplary development of knowledge libraries for mechatronical product development in mechanical as well as architectural engineering within an open-source design framework
• The possibility of real-time manipulation and simulation within one information space, which is based on the approach of Generative Modelling Language[3]
• Integration of semantics within the design space
• Development of a design model to support transdisciplinary creativity in the design phases and transition into the process-oriented product lifecycle

The concept of a creative engineering environment is based on and related to several approaches developed at institutes within the partner network, the main directions of research are listed below:

Functional DMU: (Dr. Eggeling, Fraunhofer Austria)[4],

In order to put mechatronic products on a solid base at an early stage of the development process, an extension of DMU with functional aspects to a Functional DMU (FDMU) and the support of cooperation between the domains of mechanics, electronics and software development is imperative.

GML: (Dr. Havemann, CGV Graz)[5]

The generative modelling approach is a very simple programming language for the concise description of complex 3D shapes. It follows the “Generative Modelling” paradigm, where complex datasets are represented by “lists of operations” rather than by lists of objects, which is for instance the case in a relational database. Shape design becomes rule design. This approach is very general. It can be applied to any shape representation that provides a set of generating functions

Shape Grammars: (Prof. Shea, TU Munich)[6]

Shape grammars as framework for engineering expert systems can handle all the generation and analysis capabilities of traditional production systems, while being able to represent knowledge about both the functionality and the form of a product. Additionally, the parametric nature of shape grammars, their ability to deal with physical form rather than abstract elements, and their ability to recognize emergent shapes give them significant advantages over traditional production systems for geometry-based engineering design[7].

Connection to Area 2 & Area 3

The main goal of Lifecycle Intelligence of Networked Environments is centered on future knowledge workers and their ability to create products in a networked environment. All three areas are defined as specific views to the general tasks of the project. Highlighting the activities from the point of view of a Virtual Prototype, conceptual methods for a real-time engineering platform are provided, supplemented with networked collaborative models in Area 2 and integrated into an environment for user interaction in Area 3. To find a common language as well as allowing steady Information flow within the partner network a concurrent working platform will be established.

[2] Bill of Materials

[3] S. Havemann, Automatische Code Generierung mit der Generative Modelling Language, PhD Thesis, TU Braunschweig, 2005

[4] http://www.igd.fhg.de/igd-a2/fdmu/index.php

[5] http://www.generative-modelling.org

[6] http://www.pe.mw.tum.de

[7] M. Agarwal, J.Cagan, On the use of shape grammars as expert systems for geometry based engineering design, 2000

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