Abstract: Advances in CAD and CAM have enabled engineers and design teams to digitally design parts with unprecedented ease. Software solutions now come with a range of modules for optimizing designs for performance requirements, generating instructions for manufacturing, and digitally tracking the entire process from design to procurement in the form of product life-cycle management tools. However, existing solutions force design teams and corporations to take a primarily serial approach where manufacturing and procurement decisions are largely contingent on design, rather than being an integral part of the design process. In this work, we propose a new approach to part making where design, manufacturing, and supply chain requirements and resources can be jointly considered and optimized. We present the Generative Manufacturing compiler that accepts as input the following: 1) An engineering part requirements specification that includes quantities such as loads, domain envelope, mass, and compliance, 2) A business part requirements specification that includes production volume, cost, and lead time, 3) Contextual knowledge about the current manufacturing state such as availability of relevant manufacturing equipment, materials, and workforce, both locally and through the supply chain. Based on these factors, the compiler generates and evaluates manufacturing process alternatives and the optimal derivative designs that are implied by each process, and enables a user guided iterative exploration of the design space. As part of our initial implementation of this compiler, we demonstrate the effectiveness of our approach on examples of a cantilever beam problem and a rocket engine mount problem and showcase its utility in creating and selecting optimal solutions according to the requirements and resources.

Abstract: Building and operating software-intensive systems often involves exploring decision spaces made up of large numbers of variables and complex relations among them. Understanding such spaces is often overwhelming to human decision makers, who have limited capacity to digest large amounts of information, making it difficult to distinguish the forest through the trees. In this article, we report on our experience in which we used dimensionality reduction techniques to enable decision makers in different domains (software architecture, smart manufacturing, automated planning for service robots) to focus on the elements of the decision space that explain most of the quality variation, filtering out noise, and thus reducing cognitive complexity.

Abstract: The emergence of powerful automated design tools in many domains is changing the nature of design, as human-intensive activities can be increasingly off-loaded to those tools. Rather than having a human consider only handful of options, as has been done historically, such tools now enable the generation of a large space of potential designs, exhibiting different tradeoffs among competing qualities of merit, and supporting systematic exploration of the design space. At the same time, this paradigm raises new challenges centered on enabling humans to effectively navigate that generated space in order to select a design that best meets their requirements. In this paper we describe our experience in the domain of generative manufacturing, in which we developed a novel design environment for airplane parts manufacturing that incorporates a number of sophisticated design tools and attempts to tackle the emergent problems of design space exploration that are faced by designers of those parts. We use this experience to highlight the challenges that we faced and reflect on their applicability more generally to tool-assisted software design environments.