This front matter contains the Contents, Foreword for the Series, Foreword for Volume 02, Foreword by CIE and Acknowledgments are available for free viewing by clicking on the PDF below. eBook keywords: computers and information in engineering, advanced modeling and simulation, computer-aided product and process development, computer-aided design and geometric modeling, design automation, systems engineering, information and knowledge management, finite element analysis, simulation, design optimization
This book series aims to capture advances in computers and information in engineering research, especially by researchers and members of ASME's Computers & Information in Engineering (CIE) Division. The books will be published in both traditional and eBook formats. The series is focusing on advances in computational methods, algorithms, tools, and processes on the cutting edge of research and development as they have evolved and/or have been reported during the last three to five annual CIE conferences. The series will provide a resource for enhancing engineering practice by enabling the understanding and the application of evolving and emerging technologies that impact critical engineering issues related to the topics and themes under CIE’s technical committees areas of interest, but not limited to: Advanced Modeling and Simulation; Computer-Aided Product and Process Development; Systems Engineering, Information and Knowledge Management; Virtual Environments and Systems. This front matter contains the contents, foreword for the series, foreword for volume 01, foreword by CIE and acknowledgments. Nanotechnology is the understanding and control of matter at dimensions between 1 and 100 nanometers. Materials at these scales usually exhibit unique characteristics and can provide significant technical and economic advancement with novel applications. Although promising, most nanotechnology research only focuses on dozens of or a few hundred particles or molecules. To realize large-scale devices and commercializable products, massive assembly techniques with high-volume high-rate output are required. This poses a great challenge to the nanomanufacturing research community. That is, how to fabricate nanomaterials and devices in a repeatable and scalable way such that nanotechnology becomes commercially viable. Scaling up nanotechnology from laboratory setup to industry-level production is critical to enable mass-scale impacts of nanotechnology on our daily lives. Topology optimization  has rapidly evolved from an academic exercise into an exciting discipline with numerous industrial applications. Such applications include optimization of aircraft components , , spacecraft modules , automobile components , cast components , compliant mechanisms , , , , etc. In structural analysis, topology optimization may be posed as (see illustration in Fig. 2.1). In other words, one must find the optimal topology that minimizes a specific objective function and meets certain constraints, within the given design space. Typical objective functions include mass, compliance, etc. Constraints include stress constraints, manufacturing constraints, buckling constraints, etc. This chapter discusses a multi-physics simulation engine, called Chrono , that relies heavily on parallel computing. Chrono aims at simulating the dynamics of systems containing rigid bodies, flexible (compliant) bodies, and fluid-rigid body interaction. To this end, it provides five modules: equation formulation (modeling), equation solution (simulation), collision detection support, domain decomposition for parallel computing, and post-processing analysis with emphasis on high quality rendering/visualization. For each component we point out how parallel central processing unit (CPU) and/or graphics processing unit (GPU) computing have been leveraged to allow for the physical simulation of problems with millions of degrees of freedom such as (1) rigid multi-body dynamics, (2) flexible body dynamics with friction and contact, and (3) fluid-structure interaction problems. Simulation and visualization of these physical phenomena becomes even more difficult at large scales, such as in granular dynamics. The research areas of mutiaxial robotic testing and design optimization have been recently utilized for the purpose of data-driven constitutive characterization of anisotropic material systems. This effort has been enabled by both the progress in the areas of computers and information in engineering as well as the progress in computational automation. Although our efforts have begun three decades ago, in this chapter we are presenting our progress on this synergistic combination of technologies developed in the last five years. Specifically, in this chapter we are reporting on the first successful implementation of our evolving methodology that recently was completed with the first industrial rate campaign of experiments for this purpose. This methodology is motivated by the data-driven requirements of employing design optimization principles for determining the constitutive behavior of composite materials as described in our recent work [1, 2]. Traditionally, the determination of the constitutive characterization of composite materials has been achieved through conventional uniaxial tests, mainly aiming for the estimation of the elastic properties. Typically, extraction of these properties, involve uniaxial tests conducted with specimens mounted on uniaxial testing machines, where the major orthotropic axis of any given specimen is angled relative to the loading direction. In addition, specimens are designed such that a homogeneous state of strain is developed over a well-defined area, which is required for the purpose of measuring stresses and strains through the measurement of the respective reaction forces and displacement [3, 4]. Consequently, the use of uniaxial testing machines imposes requirements of using multiple specimens, griping fixtures, and multiple experiments without the option of studying mutltiaxial effects. The requirement of a homogeneous state of strain frequently imposes restrictions on the sizes and shapes of specimens to be tested. These requirements result in increased cost and time, and to inefficient characterization processes.To address these issues and to extend characterization to multiaxial state of strain in both the linear and non-linear regimes, multi-degree of freedom automated mechatronic testing machines, which are capable of loading specimens multiaxially, in conjunction with energy-based inverse characterization methodologies, were introduced at the Naval Research Laboratory (NRL) [5–7]. This development was the first of its kind and has continued through the present [8–10]. Over the last decade, full field measurement methods have become a very useful tool for experimental mechanics and other technical disciplines. The rapid evolution of digital imaging, that has materialized toward higher quality, more inexpensive digital cameras, and the increase in the available computational power, have made these methods accessible to a broader range of scientific laboratories. Our interest in the continuing development of meshless methods for full field measurements originates from the need to improve, generalize and integrate them into the data-driven composite material characterization methodology via multi-axial mechatronic systems that were developed by the Naval Research Laboratory (NRL). This methodology requires the measurement of both in-plane and out-of-plane strain fields, and therefore such methods are particularly attractive for this purpose. In addition, there is a large variety of applications in the area of experimental mechanics and other technical disciplines where the whole field displacement and strain measurement is very important, and consequently provide a unique opportunity for the usage of the meshless approximation schemes. In engineering applications traditional approximation schemes involve the use of discrete elements corresponding to typical meshes for representing field variable distributions. More specifically, the underlying geometry is subdivided into a number of sub-regions of simple geometry, usually in the shape of a triangle or quadrilateral. The values of the field quantities of interest within these subregions or elements can be calculated based on assumed shape functions of the respective elements. In meshless approaches such sub-regions are not defined and the values of the field variable are calculated based on shape functions operating on nodal values only. All the traditional full field measurement techniques are based on shape functions associated with element approaches. This chapter is motivated by the need to report on the advances related to the use of meshless approximations in full field displacement and strain measurements. These approximations were first introduced in  and were shown to be a very efficient way to increase the accuracy of these methods by taking advantage of the filtering characteristics of the representation. The method wasmore recently updated with a direct strain approximation scheme that increases the accuracy even further . The goal of this chapter is to present the older meshless random grid method (MRG) and the newer direct strain imaging (DSI) methods, while comparing their performance and reporting on implementation details, like their 3-D extension. In most implementations of the Finite Element Method (FEM) [1–3], interpolation functions or shape functions are predefined. On the other hand, in meshless or meshfree methods these functions have to be determined by solving local equations. Therefore, the computational efficiency of the FEM is higher than the computational efficiency of meshless methods. In addition, very complex problem domains can be well represented by finite element meshes that are generated using adaptive algorithms [4, 5], which is very challenging to achieve in current meshless methods. Nevertheless, it is well known that the performance of the FEM is significantly affected by the quality of finite element meshes, which is measured by element shapes in the meshes, or more accurately, by the relative locations of nodes in the elements. The existence of a few distorted or invalid elements in a mesh may ruin the whole finite element solutions, or at the best, compromise the accuracy. As mentioned in , with readily available commercial software used for geometrical modeling and mesh generation, generation of finite element meshes is not a difficult task any more. However, tuning the quality of a finite element mesh to make all elements in the mesh have ideal shapes is very time consuming, especially if the problem domain has a complex geometric shape, as an optimization algorithm is usually required to obtain the optimal mesh. Element distortion during finite element simulation has been a major issue that makes the FEM inconvenient or inefficient in solving engineering problems involving large deformation, propagation of discontinuities, evolving interface of material phases, etc. Local mesh modification and remeshing of the evolved problem domain at every time or load step during simulation have been the two commonly used techniques to correct or remove distorted and invalid elements. Both of them are very time consuming and thus not practical for solving large-scale problems [4, 5]. Successful open-source software projects, crowdsourcing efforts, and open encyclopedias have shown that the innovation capabilities of loosely-connected masses of people can, under certain circumstances, transcend the capabilities of traditional hierarchical organizations. Examples such as Linux, Apache, Innocentive and Wikipedia epitomize the emergence of the paradigm of community-based product innovation. In this new paradigm, products are developed in a bottom-up manner by self-organized virtual communities, as opposed to traditional hierarchical organizations. While self-organized communities have resulted in successful information-based products such as software and encyclopedias, there is limited knowledge to answer the following question: How can complex systems be effectively engineered by self-organized communities of self-directed individuals? The fundamental challenge from the standpoint of realizing complex engineered systems is the sociotechnical nature of activities and contributions of independent individuals on interdependent aspects of a complex product. In this chapter, the authors present some of the research challenges, and their research efforts towards addressing these challenges. Product design is a complex multitask process that requires a wide range of expertise, knowledge, and designers creativity. It includes several interrelated activities such as studying feasibility of product concepts, developing design concepts, generating alternative product or system architectures, defining product or system interfaces, selecting materials and production processes, refining design concepts, defining parts geometry, specifying tolerances, building and testing experimental prototypes, evaluating usability and acceptance of design concepts, and analyzing aesthetics, ergonomics, reliability, performance, and cost—see, e.g., , ,  and . Effective methods and tools are needed to enable designers and engineers to perform these activities, to support creativity, and to ensure quality and success of products. One of the main challenges is that the needs on ground and the requirements for design support tools have been changing over the years. This is partly attributed to the ever-changing nature and increasing complexity of products. Overall, there is a demand for new appropriate methods and tools that meet today’s designers’ and engineers’ needs in the processes of development of present-day products, and which match up with today’s technology advancements in the areas of computing, computer graphics, and communications. Our research group has been involved in multiple research activities over the past five years, most of them focusing on creation of novel theoretical solutions as well as on developing methods and computational algorithms to support designers and engineers in the execution of activities in the design interval. The strategy in developing new tools has been to explore the relevance and appropriateness of the emerging technological solutions and to take advantage of the technological advancements in various areas of computing, computer graphics, and communications. This has been done with a view to providing contextualized solutions to various problems in product design and to enable designers and engineers to quickly and reliably externalize their innovative ideas when designing. The new theoretical and technological solutions presented in this chapter contribute to the efforts that are continuously being invested by many researchers around the globe to develop effective and efficient methods and tools to support designers and engineers in performing product development activities and to improve quality of products. While there are several factors that can contribute to reducing quality of products as well as designers or engineers performance and productivity, the limitation of the capabilities of the applied tools and methods is often the key factor that aggravates these problems. It is expected that accommodation of new technological solutions in the design process is likely to impact how the designers and engineers go about their daily professional work routines. Industrial Design Engineering (IDE) and Engineering Design (ED) are technical domains that have their own specific and intrinsic meanings, processes, procedures, and methods. However, cross-over relations and similarities are also found in approach, structure, behavior and interaction in, for example, a product creation process (PCP) and product engineering process (PEP). In our research framework we focus on the multi-disciplinary, collaborative, and mixed reality representation activities and user interactions in conjunction with hybrid computational design tools. Furthermore, we recognize and adhere to the idiosyncrasies, tacit knowledge, expertise and intuitive skill sets of the individual within the singular and/or collective context. We investigate and test these phenomena through exploration and experimentation in higher education and industry domains. We choose a best-of-both-worlds approach in which we combine the real and virtual realms to assist and support designers and engineers in their representation and presentation processes as shown in Figure 20.1  . The word cloud shows the envisioned Rawshaping paradigm, the words represent and show possible connections for exploration and research. The larger the word or group of words the more importance, notion or meaning within the hypothetical paradigm. In the evolving scenario of the global market, companies need to improve their competitiveness by innovating their traditional products and processes. In order to better understand the product development process typically implemented by industry, and the possible room for improvement, it is also important to take into consideration the evolution of products and of their users. Today’s products for the consumer market are more complex, and their aesthetic and usability aspects deserve more attention than in the past. Therefore, achieving a good balance between form and function is crucial . Although the shape is strictly related with the aesthetic impact, it can also affect the functional requirements of the product, which must be satisfied. Therefore, quite often, the original shape has to be defined, and redefined, so as to best satisfy both the user’s aesthetic preferences and the functional requirements. The full satisfaction of consumers is increasingly becoming a key element of successful business strategies. Actually, customers’ preferences and attitudes have changed: more and more customers want to buy customized and personalized products . This is the case for several kinds of products, including cars, domestic and electronic appliances, shoes, garments, and many others. So the personalization of products to fulfill specific requests from customers is becoming a key factor for success. Human factors are involved in several steps of product life and the capability to account for them effectively is a key point for a winning product on the market. However, the way a human will function in relation to a product or system is difficult to predict, yet ergonomic considerations traditionally have been addressed by intuition or rough calculations. Physical tests can be performed long after the product or system can be changed easily or without a huge loss of time and money. Too often, this leads to product redesigns or massive cost overruns to correct deficiencies neglected earlier in the process. Designers are not able, without proper support, to take into account the complex interaction that people of different size and strength may have with the system. Digital human models (DHM) can represent a valid tool to support the design team during the product development process from conception to disposal, by decreasing the development time, reducing the need for physical prototypes, lowering costs, and improving the quality and safety of the products. They are popular in some industrial domains and their use is becoming mandatory for any company in which manual operations are either directly performed or requested from any participant in the product life cycle. In fact, designers have to take into account that the product can be used by human beings, acting either as workers along the production process or as final user, interacting with the product in different ways according to their habits, goals, or needs. This requires (a) the development of products centered on human beings and suitable for the widest range of population characterized by different sizes, genders, ages, culture, preferences, and abilities , and (b) the introduction of virtual ergonomics, i.e., an organized set of strategies and tools that the simulation and evaluation of ergonomic aspects since the conceptual design stage. Therefore, DHM tools can enable the designers to address and solve ergonomics and human factors along the whole product life cycle. The chapter refers to this context and considers the use of digital human modeling techniques and virtual ergonomics in the first phases of product development. We first introduce the scientific background related to digital human modeling, including motion capture techniques. Then, we present research activities related to the application of DHM within the product development process, as well as practical examples developed in various industrial contexts performed by the authors. Final considerations about potential and current trends conclude the chapter. Today, the current competitive industrial context requires more advanced models, methods, and tools in order to deliver well-balanced products which fulfill all life-cycle constraints in a consistent and harmonious manner. In such a context, product design is generally seen as a crucial stage since design decisions have an important impact on downstream processes (i.e., manufacturing, assembly, maintenance, etc.). Among the numerous existing issues to be addressed in this phase [1–4], the aim of our research work concerns assembly process generation/integration in early product design so as to promote life-cycle awareness for designers and compact product life cycles in a concurrent manner, especially at the beginning-of-life (BOL) phase. In this research area, many attempts have been made in the past, in order to integrate assembly process knowledge and constraints in product design. These efforts have provided some interesting results such as well-known design for assembly (DFA) analysis and knowledge-based engineering applications, to name a few [5–8]. Actually these approaches have enabled some improvements and verifications on detailed product geometry but are still limited with a part-oriented vision and reactive procedures, therefore leading to redesign [9, 10]. Here the proposed research work introduces another way to tackle this challenging issue. The whole concept is based on an effort to generate as early as possible admissible assembly sequences in the first steps of the product design in order to define an assembly-oriented design (AOD) context for designers. This stake therefore requires additional efforts to gather a comprehensive model, some approaches with reasoning procedures for data structuring, geometric computation and product process data management, and tools . Creativity is the generation of new ideas, by proposing new ways of looking at existing problems, seeing new opportunities, or by exploiting emerging technologies or changes in markets. Innovation is the successful exploitation of new ideas. Design is what links creativity and innovation. Creative industries are based on these three processes. Combined with information and communication technologies (ICT), they are an emerging lead market in the world knowledge economy and a dynamic motor for economic and social innovation, outlining a common vision of future ICT-driven creative industries, where the fusion of creativity and technology is considered a crucial factor of success. We are observing a huge impact of ICT on several sectors of these industries: music compositionand production; film, television, and video; animation and computer games; writing, publishing, and print media; advertising and marketing; architecture and visual arts; product design and manufacturing. Activities in all these sectors require a high level of technical knowledge to create and manipulate content. The computer is now the workbench for making digital content, and creators need to feel at home with digital technology. Computer-aided engineering (CAE) analysis often necessitates the merger of information from multiple data sources, some of which may be the result of physical experiments or computer simulations, and others that are the result of empirical relationships. Through surrogate (or approximation) modeling techniques this disparate data can be combined into a single unified representation, sometimes termed a “metamodel” in the design engineering community. The expense of data acquisition and traditional modeling methods such as finite element analysis (FEA) can be mitigated through efficient surrogate modeling approaches, such as a class of geometric surrogate models defined using a non-uniform rational B-spline (NURBs) basis. NURBs-based surrogate models exhibit many desirable and useful properties in an engineering context. Their underlying structure supports adaptive data collection methods that can rapidly lead to accurate representations of high-dimensional (>3-D) nonlinear data sets. Furthermore, NURBsbasedsurrogates present interesting properties in terms of analysis and optimization capabilities. Many design optimization problems of interest exhibit nonlinear behaviors; are composed of combinations of continuous and discrete variables; and the desired solutions are not uniquely defined by a single design, but by a robust set of designs that perform despite manufacturing variations. While advances in computer simulation and analysis have made it feasible to analyze ever more complex engineering designs at increasing levels of fidelity, design optimization often demands solutions from these simulations to thousands and thousands of perturbations in the search for a solution. Layered manufacturing plays an important role in industry. It fabricates an input 3-D model by adding material in the layer-by-layer pattern. Layered manufacturing is widely used in applications such as biomedical engineering, aerospace industry, and automotive industry. Most layered manufacturing processes require the input model to be represented in STereoLithography (STL) format, which defines the object as a raw unstructured triangulated surface by the unit normals and vertices (ordered by the right-hand rule) of the triangles using a 3-D Cartesian coordinate system. A set of parallel planes are used to intersect with the triangulated surface of the object as the slicing strategy, and the intersection contours are traced on each slice. Non-manifold features like self-intersection, degenerated triangles, or gaps will always lead to problematic contours. The existing commercial software packages implement heuristic rules to deal with such problematic contours. For example, cutting the singular point at which the contour has a self-intersection (as shown in Figure.15.1). However, these heuristic rules do not fundamentally solve the problem. Consequently, incorrect part region classification will lead to incorrectly fabricated layers, either with unwanted gaps (see Figure.15.2 from ) or membranes. The models shown in Figure.15.2 are fabricated by fused deposition modeling (FDM). In this chapter, we investigate robust and efficient approaches for layered manufacturing process planning directly applied on an implicit solid, which is reconstructed from point cloud or volumetric images in the reverse engineering context. The design of large engineering systems is complex, costly, and a highly technical operation. Design reuse is one approach used to decrease the cost and time to market, where design knowledge reuse can range from the use of standard logic gates in integrated circuit design to the reuse of a fixture design [1, 2]. Over the last 20 or so years the introduction of the personal computer in parallel with the Internet and the World Wide Web has totally transformed how we deal with information in the workplace. This chapter considers the current and future impact of this technology, within engineering, with particular reference to the design process. It has been estimated that 90% of engineering design activity is based on variant design , while during a redesign activity up to 70% of the information is taken from previous solutions . A cursory consideration of these figures identified two immediate challenges, how we capture knowledge during the design process, and how we retrieve it. This chapter considers various aspects of the knowledge management challenges found in engineering organizations, with particular referance to the design process, and the range of technologies that can provide a solution. The evolution of automated manufacturing systems from fixed automation to flexible and programmable automation is driven by the need for achieving higher degrees of flexibility and adaptability while meeting the productivity requirements. Although flexible manufacturing systems (FMS) and, more recently, reconfigurable manufacturing systems (RMS)   successfully enabled flexible automation at the mechanical and mechatronics levels, they still suffer from rigidity at the informatics level  . Heavy reliance on humans for off-line programming hampers the abilities of the advanced production systems to respond autonomously to changes in work orders and reconfigure appropriately based on the available manufacturing resources. Ideally, automated manufacturing systems should obtain the necessary cognitive capabilities such as learning, reasoning, and adapting to changes in order to minimize their dependencies, to the extent possible, on human agents for hardwiring the program of instructions  . In particular, dynamic configuration of new manufacturing cells or retrofitting the existing ones in an autonomous fashion should be supported by the underlying computational models of next generation manufacturing systems. This chapter presents an approach to precise formal analysis of business processes with stochastic properties. The method presented here allows for both qualitative and quantitative properties to be individually analyzed at design time without requiring a full specification. This provides an effective means to explore possible designs for a business process and to debug any flaws. Product lifecycle management (PLM) provides abundant tools to define products, combines information related to the products, and exchanges such information between different actors in their life cycle. Among these tools, computer-aided engineering (CAE) gives to the designer the opportunity to create and edit a product in a digital format and may supply numerical simulation methods for analyzing functionalities of a product. The main drawback of such simulation lies in two aspects. On one hand, the implementation of a CAE simulation, e.g., finite element analysis (FEA), tends to be a time-consuming process. On the other hand, the interaction opportunities during these simulations remain relatively poor. For example, engineers cannot access intermediary simulation results in order to adjust simulation parameters in an interactive way. This drawback decreases the efficiency of the information flow and tends to influence the process of the PLM in a negative way. Currently, the development of information technologies boosts the emergence of new solutions based on advanced technical equipment that bring the user closer to the scientific data. Virtual Reality (VR) is such a promising domain in which an operator is immersed in a product space characterized by realistic renderings, as well as multi-sensory, and intuitive interactions. Thus, VR technology unseals a terrace with a large variety of potential applications, ranging from massive scientific data explorations, surgical trainings, to virtual prototyping. Within an industrial context, design evaluation of deformable mock-ups in a VR environment could benefit from the introduction of the user into the loop. Moreover, an interactive design validation of such mechanical parts plays an important role in a PLM, specifically during the Product Development Process (PDP), because an interactive deformation simulation in a VR environment enables engineers from different industrial sectors to immerse themselves and to manipulate these digital mock-ups for the purpose of identifying design problems prior to the real prototype phase. The time and costs required for sharing product information among different sectors would be largely reduced, and therefore the efficiency of the design information exchange in a PLM could be considerably increased. Since the industrial revolution, the automated acquisition and reuse of expert knowledge throughout the product life-cycle has been the holy grail of engineering. Yet recent findings [1, 2] determined in discussions with industry suggest that the uptake of such systems remains low, not least due to issues regarding implementation, costs, functionality, organizational implications, and ethics, amongst others [3, 4]. Indeed the requirements of different industrial sectors mean that there are still many challenges. For those that have embraced the technology, cost in terms of implementation and usability are key issues and many of the current tools and methods are labour-intensive, timeconsuming, and difficult to embed in engineering systems. Another key barrier is the effective access to and reuse of this knowledge in a timely and convenient manner . Knowledge capture and reuse are critical to the industrial competitiveness and are at the heart of any knowledge management process . In biopharmaceutical clinical trials, Grossman and Bates  indicated that considerable time and cost savings could be achieved through automated knowledge capture. The World Bank  reported that: “The Bank lacks the ability to efficiently retrieve and share the large volume of embedded knowledge generated during preparation and implementation of lending operations.” These articles show that advanced organizations still lack well-developed mechanisms to capture and share knowledge. Another threat is that legacy knowledge can be lost when experienced engineers leave or retire from companies and, as a consequence, that lessons learnt should be easily reused to inform and educate inexperienced personnel. Today virtual reality (VR) is an established technology in many disciplines. It is used in engineering for design review and development as well as process planning. It is increasingly used in the context of visualizing large and complex data from many sources. Recently, with ever falling hardware prices, VR technologies are also introduced in the market for home entertainment. However, in contrast to many other technology innovations, VR requires significant efforts to develop and customize content for specific application areas, which usually necessitates 3-D modeling as well as software development, even if previously available content could be reused. Historically, many VR development platforms come from a visual-presentation background and only provide customization through an application-programming interface (API). To create content for such a system, software development is inevitable complete with requirements analysis, building and testing of prototypes, and estimating a software life cycle. All of this requires experts in both software development as well as the targeted problem domain for which the software is intended. Unfortunately, this process excludes many experts from non-computer fields from exploring the capabilities of VR technologies because they would first have to become experts in developing VR applications (for a certain VR system) before actually being able to investigate new and better ways to employ VR technology in their original domain of expertise. These constraints are not very inviting for those non-VR experts and will render the technology inaccessible to them. Building VR scenarios and content should be much easier to achieve. Ideally, the person developing that content should be able to do just that within the actual VR environment itself. The challenge is to provide a set of tools and a scenario-development environment that could be used inside a VR system while capitalizing on existing tools and techniques to create these scenarios. To address this challenge, we have developed a software pipeline that enables content creation for virtual environments by bridging the experience in the immersive environment to design a scenario and behavior in that scenario with traditional desktop-based tools to fine-tune detailed aspects of a scenario. In this way we present users with a powerful setting to quickly create and experience new scenarios. This back matter contains the author index and keyword index.