Fast and Flexible Visualization Using an Enhanced Scene Graph

Virtual Reality (VR) visualization of product data in engineering applications requires a largely manual process of translating various product data into a 3D representation. Modern game engines allow low-cost, high-end visualization using latest stereoscopic Head-Mounted Displays (HMDs) and input controllers. Thus, using them for VR tasks in the engineering industry is especially appealing. As standardized formats for 3D product representations do not currently meet the requirements that arise from engineering applications, the presented paper suggests an Enhanced Scene Graph (ESG) that carries arbitrary product data derived from various engineering tools. The ESG contains formal descriptions of geometric and non-geometric data that are functionally structured. A VR visualization may be derived from the formal description in the ESG immediately. The generic elements of the ESG offer flexibility in the choice of both engineering tools and renderers that create the virtual scene. Furthermore, the ESG allows storing user annotations, thereby sending feedback from the visualization directly to the engineers involved in the product development process. Individual user interfaces for VR controllers can be assigned and their controls mapped, guaranteeing intuitive scene interaction. The use of the ESG promises significant value to the visualization process as particular tasks are being automated and greatly simplified.

Polynomial Representation of the Gaussian Process

Gaussian Process (GP) regression is a well-established probabilistic meta-modeling and data analysis tool. The posterior distribution of the GP parameters can be estimated using, e.g., Markov Chain Monte Carlo (MCMC). The ability to make predictions is a key aspect of using such surrogate models. To make a GP prediction, the MCMC chain as well as the training data are required. For some applications, GP predictions can require too much computational time and/or memory, especially for many training data points. This motivates the present work to represent the GP in an equivalent polynomial (or other global functional) form called a portable GP. The portable GP inherits many benefits of the GP including feature ranking via Sobol indices, robust fitting to non-linear and high-dimensional data, accurate uncertainty estimates, etc. The framework expands the GP in a high-dimensional model representation (HDMR). After fitting each HDMR basis function with a polynomial, they are all added together to form the portable GP. A ranking of which basis functions to use in the fitting process is automatically provided via Sobol indices. The uncertainty from the fitting process can be propagated to the final GP polynomial estimate. In applications where speed and accuracy are paramount, spline fits to the basis functions give very good results. Finally, portable BHM provides an alternative set of assumptions with regards to extrapolation behavior which may be more appropriate than the assumptions inherent in GPs.

Kinesthetically Augmented Mid-Air Sketching of Multi-Planar 3D Curve-Soups

We present haptics-enabled mid-air interactions for sketching collections of three-dimensional planar curves — 3D curve-soups — as a means for 3D design conceptualization. Haptics-based mid-air interactions have been extensively studied for modeling of surfaces and solids. The same is not true for modeling curves; there is little work that explores spatiality, tangibility, and kinesthetics for curve modeling, as seen from the perspective of 3D sketching for conceptualization. We study pen-based mid air interactions for free-form curve input from the perspective of manual labor, controllability, and kinesthetic feedback. For this, we implemented a simple haptics-enabled workflow for users to draw and compose collections of planar curves on a force-enabled virtual canvas. We introduce a novel force-feedback metaphor for curve drawing, and investigate three novel rotation techniques within our workflow for both controlled and free-form sketching tasks.

Design of a Directional Olfactory Display to Study the Integration of Vision and Olfaction

This research aims to design and develop an innovative system, based on an olfactory display, to be used for investigating the directionality of the sense of olfaction. In particular, the design of an experimental setup to understand and determine to what extent the sense of olfaction is directional and whether there is prevalence of the sense of vision over the one of smell when determining the direction of an odor, is described. The experimental setup is based on low cost Virtual Reality (VR) technologies. In particular, the system is based on a custom directional olfactory display, an Oculus Rift Head Mounted Display (HMD) to deliver both visual and olfactory cues and an input device to register subjects’ answers. The VR environment is developed in Unity3D. The paper describes the design of the olfactory interface as well as its integration with the overall system. Finally the results of the initial testing are reported in the paper.

Reliability Analysis Using Deep Learning

Over the last few decades, reliability analysis has gained more and more attention as it can be beneficial in lowering the maintenance cost. Time between failures (TBF) is an essential topic in reliability analysis. If the TBF can be accurately predicted, preventive maintenance can be scheduled in advance in order to avoid critical failures. The purpose of this paper is to research the TBF using deep learning techniques. Deep learning, as a tool capable of capturing the highly complex and nonlinearly patterns, can be a useful tool for TBF prediction. The general principle of how to design deep learning model was introduced. By using a sizeable amount of automobile TBF dataset, we conduct an experiential study on TBF prediction by deep learning and several data mining approaches. The empirical results show the merits of deep learning in performance but comes with cost of high computational load.

Exploring the Design Space Using a Surrogate Model Approach With Digital Human Modeling Simulations

Computational human factors tools are often not fully-integrated during the early phases of product design. Often, conventional ergonomic practices require physical prototypes and human subjects which are costly in terms of finances and time. Ergonomics evaluations executed on physical prototypes has the limitations of increasing the overall rework as more iterations are required to incorporate design changes related to human factors that are found later in the design stage, which affects the overall cost of product development. This paper proposes a design methodology based on Digital Human Modeling (DHM) approach to inform designers about the ergonomics adequacies of products during early stages of design process. This proactive ergonomics approach has the potential to allow designers to identify significant design variables that affect the human performance before full-scale prototypes are built. The design method utilizes a surrogate model that represents human product interaction. Optimizing the surrogate model provides design concepts to optimize human performance. The efficacy of the proposed design method is demonstrated by a cockpit design study.

Real-Time Occlusion Between Real and Digital Objects in Augmented Reality

The method presented in this work reduces the frequency of virtual objects incorrectly occluding real-world objects in Augmented Reality (AR) applications. Current AR rendering methods cannot properly represent occlusion between real and virtual objects because the objects are not represented in a common coordinate system. These occlusion errors can lead users to have an incorrect perception of the environment around them when using an AR application, namely not knowing a real-world object is present due to a virtual object incorrectly occluding it and incorrect perception of depth or distance by the user due to incorrect occlusions. The authors of this paper present a method that brings both real-world and virtual objects into a common coordinate system so that distant virtual objects do not obscure nearby real-world objects in an AR application. This method captures and processes RGB-D data in real-time, allowing the method to be used in a variety of environments and scenarios. A case study shows the effectiveness and usability of the proposed method to correctly occlude real-world and virtual objects and provide a more realistic representation of the combined real and virtual environments in an AR application. The results of the case study show that the proposed method can detect at least 20 real-world objects with potential to be incorrectly occluded while processing and fixing occlusion errors at least 5 times per second.

Automatic Fault Tree Generation From Multidisciplinary Dependency Models for Early Failure Propagation Assessment

Safety engineering for complex systems is a very challenging task and the industry has a firm basis and trust on a set of established methods like the Probabilistic Risk Assessment (PRA). New methodologies for system engineering are being proposed by academia, some related to safety, but they have a limited chance for successful adoption by the safety industry unless they provide a clear connection and benefit in relation to the traditional methodologies. Model-Based System Engineering (MBSE) has produced multiple safety related applications. In past work system models were used to generate event trees, failure propagation scenarios and for early human reliability analyses. This paper extends previous work, on a high-level interdisciplinary system model for early defense in depth assessment, to support the automatic generation of fault tree statements for specific critical system components. These statements can then be combined into fault trees using software already utilized by the industry. The fault trees can then be linked to event trees in order to provide a more complete picture of an initiating event, the mitigating functions and critical components that are involved. The produced fault trees use a worst-case scenario approach by stating that if a dependency exists then the failure propagation is certain. Our proposed method doesn’t consider specific failure modes and related probabilities, a safety expert can use them as a starting point for further development. The methodology is demonstrated with a case study of a spent fuel pool cooling system of a nuclear plant.

Identification of Human Errors During Early Design Stage Functional Failure Analysis

Detection of potential failures and human error and their propagation over time at an early design stage will help prevent system failures and adverse accidents. Hence, there is a need for a failure analysis technique that will assess potential functional/component failures, human errors, and how they propagate to affect the system overall. Prior work has introduced FFIP (Functional Failure Identification and Propagation), which considers both human error and mechanical failures and their propagation at a system level at early design stages. However, it fails to consider the specific human actions (expected or unexpected) that contributed towards the human error. In this paper, we propose a method to expand FFIP to include human action/error propagation during failure analysis so a designer can address the human errors using human factors engineering principals at early design stages. To explore the capabilities of the proposed method, it is applied to a hold-up tank example and the results are coupled with Digital Human Modeling to demonstrate how designers can use these tools to make better design decisions before any design commitments are made.

Automating Structural Modeling of Composites Enriched by X-Ray Micro-Tomography and Digital Imaging Data

The manufacturing processes of Fiber Reinforced Polymers (FRPs) as composite materials are frequently prone to the creation of various types of undesired morphologies and defects. These can include layer waviness, inclusions, and voids. Structural modeling for Finite Element Analysis (FEA) of structures including such morphologies and defects has not been practically realizable until recent developments in X-ray microtomography enabled the detection of such defects in a nondestructive manner. In the present work we present our initial steps toward the FEA modeling of FRP composite structures that leverage utilization of X-ray and regular digital imaging data as well as semi-automated methods for generating appropriate FEA models. Emphasis is given in defining waviness-driven curvilinear coordinate systems, defect identification and integration of both waviness and defects to FEA analysis including a planestrain application of a curved composite bracket under four-point bending conditions.