Showing papers on "Design tool published in 2019"

Deep Learning: A Rapid and Efficient Route to Automatic Metasurface Design.

Abstract: Metasurfaces provide unprecedented routes to manipulations on electromagnetic waves, which can realize many exotic functionalities. Despite the rapid development of metasurfaces in recent years, the design process of metasurface is still time-consuming and computational resource-consuming. Moreover, it is quite complicated for layman users to design metasurfaces as plenty of specialized knowledge is required. In this work, a metasurface design method named REACTIVE is proposed on the basis of deep learning, as deep learning method has shown its natural advantages and superiorities in mining undefined rules automatically in many fields. REACTIVE is capable of calculating metasurface structure directly through a given design target; meanwhile, it also shows the advantage in making the design process automatic, more efficient, less time-consuming, and less computational resource-consuming. Besides, it asks for less professional knowledge, so that engineers are required only to pay attention to the design target. Herein, a triple-band absorber is designed using the REACTIVE method, where a deep learning model computes the metasurface structure automatically through inputting the desired absorption rate. The whole design process is achieved 200 times faster than the conventional one, which convincingly demonstrates the superiority of this design method. REACTIVE is an effective design tool for designers, especially for laymen users and engineers.

Free-Form Diffractive Metagrating Design Based on Generative Adversarial Networks

Abstract: A key challenge in metasurface design is the development of algorithms that can effectively and efficiently produce high-performance devices. Design methods based on iterative optimization can push the performance limits of metasurfaces, but they require extensive computational resources that limit their implementation to small numbers of microscale devices. We show that generative neural networks can train from images of periodic, topology-optimized metagratings to produce high-efficiency, topologically complex devices operating over a broad range of deflection angles and wavelengths. Further iterative optimization of these designs yields devices with enhanced robustness and efficiencies, and these devices can be utilized as additional training data for network refinement. In this manner, generative networks can be trained, with a one-time computation cost, and used as a design tool to facilitate the production of near-optimal, topologically complex device designs. We envision that such data-driven design methodologies can apply to other physical sciences domains that require the design of functional elements operating across a wide parameter space.

Fractional X-shape controllable multi-scroll attractor with parameter effect and FPGA automatic design tool software

Abstract: This paper proposes a new fractional-order multi-scrolls chaotic system. More complex systems and flexible ranges of the chaotic behavior are obtained due to the extra parameters added by the fractional-order. The proposed system has novel complex chaotic behaviors. The effect of changing the system parameters on the system behavior is investigated and their bifurcation diagrams have been provided. The MLE for the proposed system in integer and fractional domain has been discussed. It shows that the proposed chaotic system is richer in the case of fractional-order. A novel FPGA design automation tool for the proposed system is also proposed. It provides an optimized FPGA implementation of the system according to different system parameters. Additionally, it provides a facility and fast way to accelerate the design process for the developer to design their own module while applying the system in different applications. The proposed tool has been tested using Xilinx's Virtex-5 XC5VLX50T FPGA and verified using MATLAB. The experimental results are provided for different design peripherals options. Finally, a procedure to generalize the proposed tool to involve any other chaotic system is presented.

A quantum computational compiler and design tool for technology-specific targets

Abstract: Quantum computing, once just a theoretical field, is quickly advancing as physical quantum technology increases in size, capability, and reliability. In order to fully harness the power of a general quantum computer or an application-specific device, compilers and tools must be developed that optimize specifications and map them to a realization on a specific architecture. In this work, a technique and prototype tool for synthesizing algorithms into a quantum computer is described and evaluated. Most recently reported methods produce technologically-independent reversible cascades comprised of a functionally complete set of operators with no regard to actual technologically-dependent cell libraries or constraints due to a device's pre-configured interconnectivity. In contrast, our prototype tool synthesizes algorithms into technologically-dependent specifications that consist of a set of primitives and connectivity constraints present in the computer architecture. The tool performs optimizations based on actual architectural constraints, and a high-quality technology-dependent synthesized result is achieved through the use of optimizing cost functions derived from real hardware and architecture parameters. Additionally, another important aspect of our tool is the incorporation of internal formal equivalence checking that ensures the initially specified algorithm is functionally equivalent to the optimized, technologically-mapped output. Experimental results are provided that target the IBM Q family of quantum computers.

Overview of computational intelligence for building energy system design

Abstract: Building energy systems, i.e. heating, ventilation, and air-conditioning (HVAC) systems, are essential for modern buildings. They provide a comfortable and healthy indoor environment. Design quality has significant impact on HVAC system efficiency. The typical building energy system design process involving several procedures is repetitive and time-consuming. It is often limited by the engineer's experience, capabilities, and time constraints; thus, the design in most cases barely satisfies building codes. In recent decades, computational intelligence (CI) has achieved substantial improvements in various fields. This paper presents a comprehensive review of using CI for HVAC system optimization design. Firstly, this paper analyzes seven procedures which constitute a typical HAVC system design process and finds that optimization problems encountered during design process can be divided into three categories: model estimation, decision making and uncertainty analysis. Then a brief introduction of CI techniques used to solve HVAC design optimization problems and detailed literature review of application examples are given. Though the design problem varies with each other, this paper outlines a typical workflow which is able to solve most HVAC optimization design problems. At last, a framework of an integrated HVAC automation and optimization design tool is proposed. The framework is developed based on building information modeling (BIM) and extracted typical design optimization workflow. It is able to connect various design stages by implementing structured information transfer between them and ultimately improve design efficiency and quality.

Ondulé: Designing and Controlling 3D Printable Springs

Abstract: We present Ondule-an interactive design tool that allows novices to create parameterizable deformation behaviors in 3D-printable models using helical springs and embedded joints. Informed by spring theory and our empirical mechanical experiments, we introduce spring and joint-based design techniques that support a range of parameterizable deformation behaviors, including compress, extend, twist, bend, and various combinations. To enable users to design and add these deformations to their models, we introduce a custom design tool for Rhino. Here, users can convert selected geometries into springs, customize spring stiffness, and parameterize their design with mechanical constraints for desired behaviors. To demonstrate the feasibility of our approach and the breadth of new designs that it enables, we showcase a set of example 3D-printed applications from launching rocket toys to tangible storytelling props. We conclude with a discussion of key challenges and open research questions.

Editing Spatial Layouts through Tactile Templates for People with Visual Impairments

Abstract: Spatial layout is a key component in graphic design. While people who are blind or visually impaired (BVI) can use screen readers or magnifiers to access digital content, these tools fail to fully communicate the content's graphic design information. Through semi-structured interviews and contextual inquiries, we identify the lack of this information and feedback as major challenges in understanding and editing layouts. Guided by these insights and a co-design process with a blind hobbyist web developer, we developed an interactive, multimodal authoring tool that lets blind people understand spatial relationships between elements and modify layout templates. Our tool automatically generates tactile print-outs of a web page's layout, which users overlay on top of a tablet that runs our self-voicing digital design tool. We conclude with design considerations grounded in user feedback for improving the accessibility of spatially encoded information and developing tools for BVI authors.

Prototyping canvas: Design tool for planning purposeful prototypes

Abstract: While prototypes are critical to the creation of successful products and innovative solutions, building a prototype is characterized by large sunk costs and a plethora of unknowns. The versatility and effectiveness of prototypes paired with the ambiguous nature of developing a prototype can lead to wasted resources. Recent studies support this claim, demonstrating that under certain circumstances, designers often prototype without a clear purpose, building prototypes as a function of the design process rather than as a function of the design. These findings motivated the creation of the Prototyping Canvas, a tool to aid designers in planning for purposeful prototypes by identifying critical assumptions and questions to guide development. Business and engineering design literature influenced the development of the canvas, which was first tested with a client project in the SUTD-MIT International Design Centre (IDC). The feedback and insights from the design team guided revisions to the canvas. The updated canvas was then validated with 55 professionals during a design project sprint. The purpose of this paper is to present the Prototyping Canvas as a valid and effective design tool.

Deep learning–based inverse method for layout design

Abstract: Layout design is encountered in many fields of engineering and science. Those with complex constraints are particularly challenging to solve due to the non-uniqueness of the solution and the difficulties in incorporating the constraints into the conventional optimization-based methods. In this paper, we propose a design method based on the recently developed machine learning technique, variational autoencoder (VAE). We utilize the learning capability of the VAE to learn the constraints and the generative capability of the VAE to generate design candidates that automatically satisfy all the constraints. As such, no constraints need to be imposed during the design stage. In addition, we show that the VAE network is also capable of learning the underlying physics of the design problem, leading to an efficient design tool that does not need any physical simulation once the network is constructed. We demonstrated the performance of the method on two cases: inverse design of surface diffusion–induced morphology change and mask design for optical microlithography.

Robiot: A Design Tool for Actuating Everyday Objects with Automatically Generated 3D Printable Mechanisms

Abstract: Users can now easily communicate digital information with an Internet of Things; in contrast, there remains a lack of support to automate physical tasks that involve legacy static objects, e.g. adjusting a desk lamp's angle for optimal brightness, turning on/off a manual faucet when washing dishes, sliding a window to maintain a preferred indoor temperature. Automating these simple physical tasks has the potential to improve people's quality of life, which is particularly important for people with a disability or in situational impairment. We present Robiot -- a design tool for generating mechanisms that can be attached to, motorized, and actuating legacy static objects to perform simple physical tasks. Users only need to take a short video manipulating an object to demonstrate an intended physical behavior. Robiot then extracts requisite parameters and automatically generates 3D models of the enabling actuation mechanisms by performing a scene and motion analysis of the 2D video in alignment with the object's 3D model. In an hour-long design session, six participants used Robiot to actuate seven everyday objects, imbuing them with the robotic capability to automate various physical tasks.

Convergence and Validation in ParaPower: A Design Tool for Phase Change Materials in Electronics Packaging

Abstract: Integration of solid-liquid phase change materials (PCMs) into electronics packaging has demonstrated the potential to reduce the transient temperature rise of components that experience pulsed thermal loads. However, the impact on local temperature histories resulting from incorporating PCMs in different locations and configurations within an electronics package is not easy to analytically determine, due in large part to the non-linear response of PCMs to a transient heat load. ParaPower is a new parametric design tool to facilitate the design of electronics packages. The tool has the capability of easily incorporating arbitrarily located PCM volumes and evaluating their effect on temperature distributions within the electronics package as a function of time. This paper determines the spatial and temporal order of convergence for the reduced order phase-change thermal model which underlies ParaPower. The results are compared and validated against both an analytical solution and a higher-fidelity commercial finite element analysis (FEA) tool. This paper has shown the fast-solving methods used in the ParaPower tool give results with comparable accuracy to those obtained using high-fidelity commercial software. Reasonably good accuracy can be obtained with fairly large time steps and grid spacing allowing fast-solving design space exploration with this option to increase fidelity within the tool to obtain higher accuracy when necessary. This research quantifies the trade-off between time steps, grid size, and accuracy such that a useful balance can be obtained. Design tools, such as ParaPower, have the potential to significantly advance design theory to reduce size and cost as well as minimize the prevalence of overdesign.

Inherently safer design tool (i-SDT): A property-based risk quantification metric for inherently safer design during the early stage of process synthesis

Abstract: In this work, an Inherently Safer Design Tool (i-SDT) is presented for early stage process synthesis to characterize and track the risk associated with different life-cycle phases of industrial processes. It also helps to develop characteristic equations for different safety parameters (i.e., flammability, explosiveness, toxicity, etc.) under various operating conditions. This property-based inherent safety quantification metric is a tailor made semi-quantitative safety analysis tool which provides safety assessment in a continuous manner to overcome the subjective nature of the existing available safety metrics. The core of this design and safety assessment tool is probabilistic risk quantification using accident and incident investigation (with over 600 incidents and within 27 years of time span), a property integration model and an exponential curve fitting method. The proposed safety metric has the flexibility to operate by identifying the major accident-prone units/sections of a process, as well as the major safety and operating parameters. The final output of this i-SDT tool is a cluster safety parameter score (CSP) which provides insights regarding the investigated unit/section or process for carrying out inherent safer design using a very limited amount of process information. The developed i-SDT tool was applied to compare different technologies of Ammonia processes in order to assess the safer option in terms of risks associated with the accident-prone unit/section and to highlight the areas of safety improvement in any existing process using the inherent safer design principles. In the future, this metric can can be embedded into a techno-economic framework to perform the cost and safety analysis simultaneously using available materials, design and accident information.

A mixed reality tool for end-users participation in early creative design tasks

Abstract: While mixed prototyping has proved to be effective for the assessment of prototypes, this research aims to explore the use of mixed prototyping for the generation of early prototypes. To satisfy end-user’s needs, new products need to be designed with an early integration of end-user requirements. An efficient way to achieve this is to directly integrate the end-users in the design process and give them an intelligible and interactive tool to perform specific design tasks. Current interactive tools to integrate end-users in the design process provide either a high level of immersion (e.g. CAVE) or a high level of control over the virtual prototype (e.g. configurators). We designed a new mixed reality design tool which simultaneously allows end-users to be immersed in a virtual environment (immersion) and to interact with a virtual prototype and to modify it (control), resulting in effective end user-interactions. In two design use-case scenarios, we assessed the end-user experience and satisfaction while using the tool and we also evaluated the impact of the tool on the creative process and the design outcomes. The findings show that, when users are provided with a tool that allows to directly perform design tasks and modify a virtual prototype, as compared to when they have no control, they are more engaged in the design tasks, more satisfied with the design process and they produce more creative outcomes.

High-permittivity pad design tool for 7T neuroimaging and 3T body imaging

Abstract: Purpose High-permittivity materials in the form of flexible "dielectric pads" have proved very useful for addressing RF inhomogeneities in high field MRI systems. Finding the optimal design of such pads is, however, a tedious task, reducing the impact of this technique. We present an easy-to-use software tool which allows researchers and clinicians to design dielectric pads efficiently on standard computer systems, for 7T neuroimaging and 3T body imaging applications. Methods The tool incorporates advanced computational methods based on field decomposition and model order reduction as a framework to efficiently evaluate the B1 + fields resulting from dielectric pads. The tool further incorporates optimization routines which can either optimize the position of a given dielectric pad, or perform a full parametric design. The optimization procedure can target either a single target field, or perform a sweep to explore the trade-off between homogeneity and efficiency of the B1 + field in a specific region of interest. The 3T version further allows for shifting of the imaging landmark to enable different imaging targets to be centered in the body coil. Results Example design results are shown for imaging the inner ear at 7T and for cardiac imaging at 3T. Computation times for all cases are approximately a minute per target field. Conclusion The developed tool can be easily used to design dielectric pads for any 7T neuroimaging and 3T body imaging application within minutes. This bridges the gap between the advanced design methods and the practical application by the MR community.

Tactlets: Adding Tactile Feedback to 3D Objects Using Custom Printed Controls

Abstract: Rapid prototyping of haptic output on 3D objects promises to enable a more widespread use of the tactile channel for ubiquitous, tangible, and wearable computing. Existing prototyping approaches, however, have limited tactile output capabilities, require advanced skills for design and fabrication, or are incompatible with curved object geometries. In this paper, we present a novel digital fabrication approach for printing custom, high-resolution controls for electro-tactile output with integrated touch sensing on interactive objects. It supports curved geometries of everyday objects. We contribute a design tool for modeling, testing, and refining tactile input and output at a high level of abstraction, based on parameterized electro-tactile controls. We further contribute an inventory of 10 parametric Tactlet controls that integrate sensing of user input with real-time electro-tactile feedback. We present two approaches for printing Tactlets on 3D objects, using conductive inkjet printing or FDM 3D printing. Empirical results from a psychophysical study and findings from two practical application cases confirm the functionality and practical feasibility of the Tactlets approach.

Conceptual design for assembly in aerospace industry: A method to assess manufacturing and assembly aspects of product architectures

Abstract: In recent years, the air transport market has quickly grown, creating new civil aircrafts demand, challenging the actual production rate of aerospace industries. The bottleneck of the current civil aircrafts production rate lies in the capability of the manufacturing and assembly facilities in relation to the aircrafts architecture design. The aim of this work is to develop a methodology and a related mathematical model that can be used at the conceptual design phase for the assessment of criticalities related to the product assemblability. The methodology allows to recognize modules and/or interfaces which are mostly affecting the assembly time providing a design tool for the comparison and evaluation of product architecture alternatives. A preliminary application has been done on the nose-fuselage of a civil aircraft for passenger transport. The test case provides interesting outcome in the identification of modules and module interfaces which are strongly affecting the assembly phase and required a re-arrangement (new architecture design) for the process improvement.

Design Principle Cards: Toolset to Support Innovations With Additive Manufacturing

Abstract: Additive manufacturing (AM) continues to play an important role in product development, and many companies are searching for how to best integrate AM into their products, business models, and design processes. Often, AM is integrated into later stages of the design process for products during manufacturing and production. However, there is an opportunity to introduce AM in early-stage design, which could spark new business models and services in addition to re-thinking manufacturing for products. The central research question for this paper is what is an appropriate and useful tool to support innovations with AM early in the design process? Prior work has extracted and validated AM design principles. This paper describes the strategic development of AM Principle Cards from these design principles. The cards are a vehicle for codified AM design principles to be shared and understood in a way that inspires learning, creativity, and AM considerations during the early stages of the design process. They implement a number of best-known practices from an inductive principle-extraction study and literature related to the use of design stimuli, learning theory, design by analogy, and creativity. The AM cards were awarded a Singapore Good Design Award (SG Mark) for 2019. The AM Principle Cards were validated in two studies. In this paper, an ideation study is conducted with 85 designers to elicit feedback about the cards’ effectiveness to explain concepts related to AM and their ability to inspire creativity and new innovations. An additional ideation study was conducted with 61 participants that showed significant improvement in quality and novelty of ideas. The full deck of the final 27 AM Principle Cards is shared for design educators and practitioners to use.

A heuristic approach to module synthesis in the design of reconfigurable manufacturing systems

Abstract: While it is well recognized in the literature that modularity is a very important enabler for reconfigurability in manufacturing systems, there is very limited practical guidance on how the various required functions of a production system should be grouped into modules. In this work, a new heuristic approach is developed and presented, to aid the system developer in the identification and synthesis of potential modules during the early design stage of a reconfigurable manufacturing system. The work involves the identification of key module drivers for such systems, and of key criteria that can be used to facilitate the optimization of the granularity and effectiveness of the system. The results are presented in the form of a semi-algorithmic design tool that can be easily understood and used by the developer. The tool is applied to three very different industrial case studies, and is shown to be applicable to various manufacturing scenarios and sub-sectors. The use of the new tool is compared to the use of a design structure matrix approach to function clustering for module synthesis, and is shown to be easier and more objective in its application.

Data aggregation processes: a survey, a taxonomy, and design guidelines

Abstract: Data aggregation processes are essential constituents for data management in modern computer systems, such as decision support systems and Internet of Things systems, many with timing constraints. Understanding the common and variable features of data aggregation processes, especially their implications to the time-related properties, is key to improving the quality of the designed system and reduce design effort. In this paper, we present a survey of data aggregation processes in a variety of application domains from literature. We investigate their common and variable features, which serves as the basis of our previously proposed taxonomy called DAGGTAX. By studying the implications of the DAGGTAX features, we formulate a set of constraints to be satisfied during design, which helps to check the correctness of the specifications and reduce the design space. We also provide a set of design heuristics that could help designers to decide the appropriate mechanisms for achieving the selected features. We apply DAGGTAX on industrial case studies, showing that DAGGTAX not only strengthens the understanding, but also serves as the foundation of a design tool which facilitates the model-driven design of data aggregation processes.

‘Materials as a Design Tool’ Design Philosophy Applied in Three Innovative Research Pavilions Out of Sustainable Building Materials with Controlled End-Of-Life Scenarios

Abstract: Choosing building materials is usually the stage that follows design in the architectural design process, and is rarely used as a main input and driver for the design of the whole building’s geometries or structures. As an approach to have control over the environmental impact of the applied building materials and their after-use scenarios, an approach has been initiated by the author through a series of research studies, architectural built prototypes, and green material developments. This paper illustrates how sustainable building materials can be a main input in the design process, and how digital fabrication technologies can enable variable controlling strategies over the green materials’ properties, enabling adjustable innovative building spaces with new architectural typologies, aesthetic values, and controlled martial life cycles. Through this, a new type of design philosophy by means of applying sustainable building materials with closed life cycles is created. In this paper, three case studies of research pavilions are illustrated. The pavilions were prefabricated and constructed from newly developed sustainable building materials. The applied materials varied between structural and non-structural building materials, where each had a controlled end-of-life scenario. The application of the bio-based building materials was set as an initial design phase, and the architects here participated within two disciplines: once as designers, and additionally as green building material developers. In all three case studies, Design for Deconstruction (DfD) strategies were applied in different manners, encouraging architects to further follow such suggested approaches.

Designing in virtual reality: a comparison of problem-solving styles between desktop and VR environments

Abstract: With recent breakthroughs in VR devices, new potential of CAD as a design tool is anticipated. Based on the influence of design tools in ideation, we compared the problem-solving styles of WIMP (Wi...

Electric Machine Design Tool for Permanent Magnet Synchronous Machines

Abstract: Since various machine parameters of existing electric machines are subject to confidentiality agreements of manufacturers, machine data sheets are often incomplete, making an accurate simulation and validation of machine design tools difficult but inevitable. Therefore, an electric machine design tool for permanent magnet synchronous machines (PSM) is introduced in this paper, enabling a holistic electric machine design for existing and new machines using few input parameters. The electric machine design tool is published under an LGPL open source license.

Concept-Level Design Analytics for Blended Courses

Abstract: Although many efforts are being made to provide educators with dashboards and tools to understand student behaviors within specific technological environments (learning analytics), there is a lack of work in supporting educators in making data-informed design decisions when designing a blended course and planning learning activities. In this paper, we introduce concept-level design analytics, a knowledge-based visualization, which uncovers facets of the learning activities that are being authored. The visualization is integrated into a (blended) learning design authoring tool, edCrumble. This new approach is explored in the context of a higher education programming course, where teaching assistants design labs and home practice sessions with online smart learning content on a weekly basis. We performed a within-subjects user study to compare the use of the design tool both with and without the visualization. We studied the differences in terms of cognitive load, design outcomes and user actions within the system to compare both conditions to the objective of evaluating the impact of using design analytics during the decision-making phase of course design.

Efficient Calibration of Embedded MPC

Abstract: Model Predictive Control (MPC) is a powerful and flexible design tool of high-performance controllers for physical systems in the presence of input and output constraints. A challenge for the practitioner applying MPC is the need of tuning a large number of parameters such as prediction and control horizons, weight matrices of the MPC cost function, and observer gains, according to different trade-offs. The MPC design task is even more involved when the control law has to be deployed to an embedded hardware unit endowed with limited computational resources. In this case, real-time system requirements limit the complexity of the applicable MPC configuration, engendering additional design tradeoffs and requiring to tune further parameters, such as the sampling time and the tolerances used in the on-line numerical solver. To take into account closed-loop performance and real-time requirements, in this paper we tackle the embedded MPC design problem using a global, data-driven, optimization approach We showcase the potential of this approach by tuning an MPC controller on two hardware platforms characterized by largely different computational capabilities.