Cyber-Physical Systems, Blended Tool Environments, and Playful Creativity

All tools humanity uses are extensions of their physical and/or virtual reach, towards a specific purpose or to fulfill a particular, specified, or dedicated task. The tool is handled, initiated and actively guided to participate in interaction, perception, and/or interpretation of the world around us. Tools mediate in action and interaction, like handling a toothbrush to gain a fresh set of cleaned teeth or to use a hammer to pound nails in a material. The real physicality of these human interactions convey a lot of information and creates knowledge in various levels of insight and understanding. Not only in terms of feeling satisfied in the accomplishment of a task, but also in the experience of tool use and succesful interaction. Furthermore, metacognitive aspects of tool use occur when human beings and tools work together and can be seen as an action-based method of advancing knowledge. In the quotidian, a mixture of tools (i.e. used, embedded) and tool activities occur to directly or indirectly interact with our physical and virtual surroundings, things, or systems. Analogue tools, like e.g. knives, pens, chairs and cars have different complexities, but through communicated ’meaning’ (Dewey, 2005) [9], these artifacts possess a distinct quality and intrinsic interaction of use. Some of these tools have very simple but effective use qualities and therefore are most of the time easy to understand in function and use. Other more sophisticated tools imply more study and demand lots of exercise (i.e. high learning threshold) in order to get the full benefit, function and gain in user experience (UX) and results. In the digital and virtual realms many varieties of computational tools are encountered. As a consequence, many categories and levels of tool use, usage through interaction, usability, user-skills and UX happen. The last decades showed a plethora of tool applications and tool interactions that eluded many users, consequently leading to misinterpretation, misguidance, frustration, reduction and inert mediocrity. Not to speculate that digital innovations and tools are defunct gadgets or not worthy of inclusion in daily life. On the contrary, digital technology plays a crucial role in our understanding of the physical and virtual worlds that co-exists and give us much broader boundless experiences and perspectives than ever before. The problem with most digital tools is, the constructed user interface (UI) and user interaction (UA) between a user and machine, as shown in, for example; Carroll, 1991 [5], Carroll, 2002 [6], Dix, 2009 [10], Hartson, 2003 [16], Piumsomboon et al., 2017 [31], Wendrich, 2016 [44], Rogers, 2011 [33]. This in turn has lead to more study and research being conducted on this subject over the last decades, what somehow lead to more confusion and misapprehension. Incremental improvements in UI have been explored and became a sort of standard, new approaches to UIs and UAs have appeared and wiped others, in some cases e.g. multi-touch sensing surfaces became a next step in interacting with the digital-virtual realms. This in turn lead to a leap in applications software (app) design to create tools that were easy to manipulate and use by swiping fingers across high-definition interactive icons to work the tool. However, how feebly, fleetly or superficial this type of mediated interactions may seem, somehow it became a prefered way of ’doing things.’ Gradually this kind of interaction became the standard, encroached with instant gratification and satisfaction. Eventually, everything is an approximation with human frailty, so is tool use and are tools, Figure 19.1.

Peridynamics for Numerical Analysis

Discrete data analysis and numerical solutions to boundary and initial value problems of ordinary/partial differential equations are essential in almost every branch of science. Although the differentiation process is usually more direct than integration in analytical mathematics, the reverse is true in computational mathematics, especially in the presence of a jump discontinuity or a singularity. Integration is a nonlocal process because it depends on the entire range of integration. However, differentiation is a local process.

System Design Analytics with Low-Fidelity Models

Establishing product requirements for the customer is usually the first step in the product development process. Indeed, identifying and fulfilling customer requirements is the key for successful product development. However, satisfying all the customer requirements is not always possible. Therefore, the best design is the design that fulfils a set of the most important customer requirements. Due to this, design process needs to be agile and iterative. Design and its requirements need to be effectively iterated.

A Machine Learning Framework for Decision Support in Design and Manufacturing

The widespread adoption of computer-aided design (CAD) and manufacturing (CAM) tools has resulted in the acceleration of the product development process, reducing the time taken to design a product [46]. However, the product development process, for the most part, is still decentralized with the design and manufacturing reviews being performed independently, leading to differences between as-designed and as-manufactured component. A successful product needs to meet its specifications, while also being manufacturable. In general, the design engineer ensures that the product is able to function according to the specified requirements, while the manufacturing engineer gives feedback to the design engineer about its manufacturability. This iterative process is often time consuming, leading to longer product development times and higher costs. Recent researches in integrating design and manufacturing [24, 28, 46] have tried to reduce these differences and making the product development process easier and accessible to designers, who may not be manufacturing experts. In addition, there have been different efforts to enable a collaborative product development process and reduce the number of design iterations [8, 10, 41]. However, with the increase in complexity of designs, integrating the manufacturability analysis within the design environment provides an ideal solution to improve the product design process.

Novel and Efficient Mathematical and Computational Methods for the Analysis and Architecting of Ultralight Cellular Materials and their Macrostructural Responses

Nature benefits from high stiffness and strength low-weight materials by involving architected cellular structures. For example, trabecular bone, beaks and bones of birds, plant parenchyma, and sponge optimize superior mechanical properties at low density by implementing a highly porous, complex architected cellular core [25]. The same engineering and architectural principles at the material scale have been used by humankind to develop materials with higher mechanical efficiency and lower mass in many weight-critical applications. The emergence of advanced manufacturing technologies such as additive manufacturing and three-dimensional (3D) laser lithography offer the opportunity to fabricate ultralight metallic and composite materials with intricate cellular architecture to location-specific requirements. For example, the world’s lightest metal [26,32], Fig. 7.1, and reversibly assembled ultralight carbon-fiber-reinforced composite materials [4], Fig. 7.2, with architected cellular structures have been recently fabricated at Hughes Research Laboratories (HRL) in California and MIT Media Lab-Center for Bits and Atoms, respectively.

Mesoscale Lattice Structure Design and Simulation with the Support of a Property Database

The lattice structure is a type of cellular materials [1] that has truss-like structures with interconnected struts and nodes in a three-dimensional (3D) space. Compared to other cellular materials such as random foams and honeycombs, the lattice structures exhibit better mechanical performance [2]. Some examples of lattice structures are shown in Figure 8.1. The first one is a randomized lattice structure. Due to the disordered lattice cells, the properties of this type of lattice structures are stochastic and difficult to control. But it can be used as implants in orthopedic surgeries. The second and the third are lattice structures with periodic unit cells. The difference is that the strut thickness of the second one is uniform, which is called homogeneous lattice structures. However, the third one has non-uniform strut thickness for specific loading conditions, which is called heterogeneous lattice structures. By properly adjusting the material in vital parts of the lattice structure, the heterogeneous periodic lattice structure can have a better mechanical performance than the homogeneous one with the same weight. Plenty of design and optimization methods [3-5] have been proposed for lattice structures to pursue better performance in different engineering applications. For example, the lattice structure is applied to achieve lightweight [3, 4], energy absorption [6], and thermal management [7]. Due to the complexity of the geometry, the fabrication of lattice structures had been the most critical issue. However, with the development of Additive Manufacturing (AM) processes, the difficulty in the fabrication was largely relieved.

Efficient Multi-Material Topology Optimization

This chapter focuses on generating optimized topologies using multiple materials. The interest in multi-material topology optimization (MMTO) stems from the well-recognized synergy between topology optimization (TO) and additive manufacturing (AM), where organic structures created through TO can be directly fabricated by a variety of AM processes. Given the rapidly increasing capabilities of AM, there is an opportunity to improve the performance of consumer products, biomedical, and aerospace components, through simultaneous optimization of topology and distribution of multiple materials.

Proper Generalized Decomposition Based Model Order Reduction Applied to Problems Defined in Degenerated Domains

Nowadays, the use of simulation in industrial applications is a routine practice. However, the need of considering high fidelity models and their associated solutions is demanding for more efficient solvers. Despite the remarkable progress accomplished in hardware and software in the last decades, the ability to simulate high fidelity 3D models, taking into consideration all their richness is still challenging. For example, if we wish to simulate a 3D problem including 10 parameters, each of them taking 10 different values we should solve 1010 3D problems. This issue is known as the curse of dimensionality, and compromises the solution of models defined in high dimensional spaces.

Learning and Tracking Ad Hoc Fiducial Markers in Spatial Augmented Reality

We describe a spatial augmented reality system with a tangible user interface used to control computer simulations of complex systems. In spatial augmented reality, the user’s physical space is augmented with projected imagery, blending real objects with projected information, and a tangible user interface enables users to manipulate physical objects as controllers for interactive visualizations. Our system learns ad hoc objects in the user’s environment as fiducial markers (i.e., objects that are visually recognized and tracked). When combined with simulation and visualization tools, these interfaces allow the user to control simulations or ensembles of simulations via physical objects using apt metaphors. While other research has leveraged the use of depth cameras, our system enables the use of standard cameras in readily available smartphones and webcams and has an implementation that runs completely in JavaScript in the web browser. We discuss the prerequisite object-recognition requirements for such tangible user interfaces and describe computer-vision and machine-learning algorithms meeting those requirements. We conclude by presenting example applications, which are also available online.

A Connected Autonomous Vehicle Emulator (CAVE) for Testing Multi-agent, Conventional–Autonomous Mixed Vehicle Traffic Scenarios

We discuss a software infrastructure that provides a virtual proving ground for designing, training, and auditing the computer programs used to pilot connected autonomous vehicles (CAVs). This effort does not concentrate on developing the piloting computer programs (PCPs) responsible for path planning in autonomous vehicles (AVs). Instead, we have established a first version of an emulation platform that changes the PCP design/test/improve process, which is often times carried out covertly [46], or in actual traffic conditions with potentially fatal consequences [45, 47].