A Framework for Interactive Structural Design Exploration

Rapid advances in additive manufacturing and topology optimization enable unprecedented levels of design freedom for realizing complex structures. The challenge is that the increasing design freedom is accompanied by increasing complexity, such that it can become difficult for either computational algorithms or human designers alone to search these expansive design spaces effectively. Our goal is to establish an interactive design framework that is both data-driven and designer-guided so that human designers can work together with computational algorithms to search structural design spaces more effectively. The framework builds upon classical topology optimization techniques to build a library of designs for a class of problems. A conditional generative adversarial network (cGAN) is trained to establish a latent representation of the library and to support rapid exploration of candidate designs. The library of designs is clustered based on visual similarity. The user selects clusters with desirable features, and the underlying latent representation is manipulated to generate visually similar candidate designs with adjustable levels of diversity or similarity to the selected clusters. The framework enables designers to use their expertise and intuition to guide the algorithm towards promising solutions by screening designs quickly and eliminating clusters of designs that may not be desirable for reasons that are difficult to embed within the optimization itself but are recognizable and significant to a human designer (e.g., secondary functionality, aesthetics).

A COMPUTATIONAL APPROACH TO GENERATE DESIGN WITH SPECIFIC STYLE

Creativity is crucial in design. In recent years, growing computational methods are applied to improve the creativity of design. This paper aims to explore an approach to generate creative design images with specific feature or design style. A Generative Adversarial Network model is applied in the approach to learn the specific design style. The target products will be projected into the latent space of model to transfer their styles and generate images. The generated images combine the features of the specific design style and the features of the target product. In the experiment, the approach using the generated images to inspire the human designer to generate the creative design in according styles. According to the primary verification by participants, the generated images can bring novelty and surprise to participants, which gain the positive impact on human creativity.

Visualisation of Control Software for Cyber-Physical Systems

Cyber-physical systems are typically composed of a physical system (plant) controlled by a software (controller). Such a controller, given a plant state s and a plant action u, returns 1 iff taking action u in state s leads to the physical system goal or at least one step closer to it. Since a controller K is typically stored in compressed form, it is difficult for a human designer to actually understand how “good” K is. Namely, natural questions such as “does K cover a wide enough portion of the system state space?”, “does K cover the most important portion of the system state space?” or “which actions are enabled by K in a given portion of the system space?” are hard to answer by directly looking at K. This paper provides a methodology to automatically generate a picture of K as a 2D diagram, starting from a canonical representation for K and relying on available open source graphing tools (e.g., Gnuplot). Such picture allows a software designer to answer to the questions listed above, thus achieving a better qualitative understanding of the controller at hand.

Beyond typologies, beyond optimization: Exploring novel structural forms at the interface of human and machine intelligence

This article presents a computer-aided design framework for the generation of non-standard structural forms in static equilibrium that takes advantage of the interaction between human and machine intelligence. The design framework relies on the implementation of a series of operations (generation, clustering, evaluation, selection, and regeneration) that allow to create multiple design options and to navigate in the design space according to objective and subjective criteria defined by the human designer. Through the interaction between human and machine intelligence, the machine can learn the nonlinear correlation between the design inputs and the design outputs preferred by the human designer and generate new options by itself. In addition, the machine can provide insights into the structural performance of the generated structural forms. Within the proposed framework, three main algorithms are used: Combinatorial Equilibrium Modeling for generating of structural forms in static equilibrium as design options, Self-Organizing Map for clustering the generated design options, and Gradient-Boosted Trees for classifying the design options. These algorithms are combined with the ability of human designers to evaluate non-quantifiable aspects of the design. To test the proposed framework in a real-world design scenario, the design of a stadium roof is presented as a case study.

Solving Multiple Isolated, Interleaved, and Blended Tasks through Modular Neuroevolution

Many challenging sequential decision-making problems require agents to master multiple tasks. For instance, game agents may need to gather resources, attack opponents, and defend against attacks. Learning algorithms can thus benefit from having separate policies for these tasks, and from knowing when each one is appropriate. How well this approach works depends on how tightly coupled the tasks are. Three cases are identified: Isolated tasks have distinct semantics and do not interact, interleaved tasks have distinct semantics but do interact, and blended tasks have regions where semantics from multiple tasks overlap. Learning across multiple tasks is studied in this article with Modular Multiobjective NEAT, a neuroevolution framework applied to three variants of the challenging Ms. Pac-Man video game. In the standard blended version of the game, a surprising, highly effective machine-discovered task division surpasses human-specified divisions, achieving the best scores to date in this game. In isolated and interleaved versions of the game, human-specified task divisions are also successful, though the best scores are surprisingly still achieved by machine discovery. Modular neuroevolution is thus shown to be capable of finding useful, unexpected task divisions better than those apparent to a human designer.

Validation of schema mappings with nested queries

With the emergence of the Web and the wide use of XML for representing data, the ability to map not only flat relational but also nested data has become crucial. The design of schema mappings is a semi-automatic process. A human designer is needed to guide the process, choose among mapping candidates, and successively refine the mapping. The designer needs a way to figure out whether the mapping is what was intended. Our approach to mapping validation allows the designer to check whether the mapping satisfies certain desirable properties. In this paper, we focus on the validation of mappings between nested relational schemas, in which the mapping assertions are either inclusions or equalities of nested queries. We focus on the nested relational setting since most XML?s Document Type Definitions (DTDs) can be represented in this model. We perform the validation by reasoning on the schemas and mapping definition. We take into account the integrity constraints defined on both the source and target schema. We consider constraints and mapping?s queries which may contain arithmetic comparisons and negations. This class of mapping scenarios is significantly more expressive than the ones addressed by previous work on nested relational mapping validation. We encode the given mapping scenario into a single flat database schema, so we can take advantage of our previous work on validating flat relational mappings, and reformulate each desirable property check as a query satisfiability problem.

Representation: Structural Complexity of Assemblies to Create Neural Network Based Assembly Time Estimation Models

Assembly time estimation is traditionally a time intensive manual process requiring detailed geometric and process information to be available to a human designer. As a result of these factors, assembly time estimation is rarely applied during early design iterations. This paper explores the possibility that the assembly time estimation process can be automated while reducing the level of design detail required. The approach presented here trains artificial neural networks (ANNs) to estimate the assembly times of vehicle sub-assemblies at various stages using properties of the connectivity graph at that point as input data. Effectiveness of estimation is evaluated based on the distribution of estimates provided by a population of ANNs trained on the same input data using varying initial conditions. Results suggest that the method presented here can complete the time estimation of an assembly process with +/− 15% error given an initial sample of manually estimated times for the given sub-assembly.