A knowledge-driven framework for synthesizing designs from modular components (2311.18533v1)

Abstract: Creating a design from modular components necessitates three steps: Acquiring knowledge about available components, conceiving an abstract design concept, and implementing that concept in a concrete design. The third step entails many repetitive and menial tasks, such as inserting parts and creating joints between them. Especially when comparing and implementing design alternatives, this issue is compounded. We propose a use-case agnostic knowledge-driven framework to automate the implementation step. In particular, the framework catalogues the acquired knowledge and the design concept, as well as utilizes Combinatory Logic Synthesis to synthesize concrete design alternatives. This minimizes the effort required to create designs, allowing the design space to be thoroughly explored. We implemented the framework as a plugin for the CAD software Autodesk Fusion 360. We conducted a case study in which robotic arms were synthesized from a set of 28 modular components. Based on the case study, the applicability of the framework is analyzed and discussed.

• The paper demonstrates Combinatory Logic Synthesis, a knowledge-driven framework that automates the integration of modular components using type theory.
• It integrates with Autodesk Fusion 360 to enable seamless design exploration and iterative refinement, reducing design time by up to two orders of magnitude.
• A case study on robotic arm design validates the framework's ability to rapidly generate accurate functional design alternatives.

Introduction

The field of design and engineering has always been about creating tangible solutions for various problems. However, as technology advances, so does the complexity of products, leading to increased customization and the need for iterative design processes. In the manufacturing context, these design processes involve combining modular components, which can be time-consuming and repetitive, especially when multiple alternatives must be compared.

Combinatory Logic Synthesis in Design

The paper centers around an innovation called Combinatory Logic Synthesis (CLS), a technique that automates the composition of modular parts using a knowledge-driven framework. CLS uses well-established type theory to generate a repository of components that can be combined in meaningful ways to create functional designs. By specifying types for each modular component, the system is able to understand the potential connections and functions, thereby allowing the automated synthesis of new design alternatives. The significance of this technique lies in its ability to conform to a predefined knowledge base, ensuring that each synthesized design upholds the established criteria.

Framework and Workflow

The framework is composed of front-end and back-end elements and is implemented as a plug-in for popular computer-aided design (CAD) software, Autodesk Fusion 360. This approach ensures that designers can easily explore the design space and apply their expertise directly in the CAD environment. The workflow is divided into two phases: the setup phase, where developers encode knowledge about components by building taxonomies and annotating types, and the exploration phase, where design alternatives are generated and examined.

A significant feature of the framework is its user-friendly implementation as a front-end plugin, which also handles synchronization with a backend database, aiding designers in refining and customizing resulting designs without leaving the CAD software.

Case Study and Performance

A case paper was conducted that focused on synthesizing designs for robotic arms using a predefined set of modular components. The demonstrated process highlights the practical applicability and the capability of the framework to generate numerous functional design alternatives quickly and accurately. More importantly, the case paper showcases substantial time savings in both the initial design and iterative modification stages when compared to manual human design efforts—in the order of two magnitudes faster.

Conclusion

Automating design implementation within the manufacturing context has the potential not only to enhance efficiency but also to enable broader exploration of design spaces. The proposed framework uniquely compleits these objectives through knowledge-driven automation and integration with standard CAD tools. The implementation, thus, results in a significant reduction in time and effort within the design process, all while avoiding common human errors. As advancements continue, possible enhancements such as machine learning recommendations and optimization techniques promise to further improve this process.