Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding

In this study, we present a data-driven generative design approach that can augment human creativity in product shape design with the objective of improving system performance. The approach consists of two modules: 1) a 3D mesh generative design module that can generate part-aware 3D objects using variational auto-encoder (VAE), and 2) a low-fidelity evaluation module that can rapidly assess the engineering performance of 3D objects based on locally linear embedding (LLE). This approach has two unique features. First, it generates 3D meshes that can better capture surface details (e.g., smoothness and curvature) given individual parts’ interconnection and constraints (i.e., part-aware), as opposed to generating holistic 3D shapes. Second, the LLE-based solver can assess the engineering performance of the generated 3D shapes to realize real-time evaluation. Our approach is applied to car design to reduce air drag for optimal aerodynamic performance

An Exploratory Study on Fairness-Aware Design Decision-Making

With advances in machine learning (ML) and big data analytics, data-driven predictive models play an essential role in supporting a wide range of simple and complex decision-making processes. However, historical data embedded with unfairness may unintentionally reinforce discrimination towards minority groups when using data-driven decision-support technologies. In this paper, we quantify unfairness and analyze its impact in the context of data-driven engineering design using the Adult Income dataset. First, we introduce a fairness-aware design concept. Subsequently, we introduce standard definitions and statistical measures of fairness to the engineering design research. Then, we use the outcomes from two supervised ML models, Logistic Regression and CatBoost classifiers, to conduct the Disparate Impact and fair-test analyses to quantify any unfairness present in the data and decision outcomes. Based on the results, we highlight the importance of considering fairness in product design and marketing, and the consequences, if there is a loss of fairness

Design representation for performance evaluation of 3D shapes in structure-aware generative design

Data-driven generative design (DDGD) methods utilize deep neural networks to create novel designs based on existing data. The structure-aware DDGD method can handle complex geometries and automate the assembly of separate components into systems, showing promise in facilitating creative designs. However, determining the appropriate vectorized design representation (VDR) to evaluate 3D shapes generated from the structure-aware DDGD model remains largely unexplored. To that end, we conducted a comparative analysis of surrogate models’ performance in predicting the engineering performance of 3D shapes using VDRs from two sources: the trained latent space of structure-aware DDGD models encoding structural and geometric information and an embedding method encoding only geometric information. We conducted two case studies: one involving 3D car models focusing on drag coefficients and the other involving 3D aircraft models considering both drag and lift coefficients. Our results demonstrate that using latent vectors as VDRs can significantly deteriorate surrogate models’ predictions. Moreover, increasing the dimensionality of the VDRs in the embedding method may not necessarily improve the prediction, especially when the VDRs contain more information irrelevant to the engineering performance. Therefore, when selecting VDRs for surrogate modeling, the latent vectors obtained from training structure-aware DDGD models must be used with caution, although they are more accessible once training is complete. The underlying physics associated with the engineering performance should be paid attention. This paper provides empirical evidence for the effectiveness of different types of VDRs of structure-aware DDGD for surrogate modeling, thus facilitating the construction of better surrogate models for AI-generated designs

PHYSICAL VALIDATION OF JOB PLACEMENT OPTIMIZATION IN COOPERATIVE 3D PRINTING

Cooperative 3D printing (C3DP) is an emerging technology designed to overcome the limitations of traditional 3D printing, including speed and scalability. C3DP achieves this by partitioning prints into smaller jobs, e.g., chunks, and assigning them to a team of mobile 3D printers that work cooperatively in parallel allowing for autonomous additive manufacturing of large objects via a swarm-based system. Our prior work established a framework for optimizing job placement by connecting geometric partitioning algorithms with path planning and scheduling algorithms. However, this framework was not physically validated. In this paper, we present the first physical validation of the job placement algorithm by chunking and printing two objects using the proposed algorithm. The objects used in the test cases vary in size and complexity, from a small and simple object to a large object with intricate geometry. We demonstrate that our optimized placement algorithm provides results comparable to the physical C3DP system, providing a significant step forward in the practical implementation of C3DP technology.Mechanical Engineerin

Time-Optimal Path Planning for Heterogeneous Robots in Swarm Manufacturing

Time-optimal path planning is a critical problem in any system employing mobile robots. Much of the literature on this problem operates under two major assumptions: the environment is static, and the robots’ effective shapes are constant and discretized either as a circle or a sphere, disregarding the impact of the robot’s orientation on the overall task. These assumptions are restrictive in swarm manufacturing, where the environment is dynamically changing and manufacturing robots have geometric constraints for operation. For example, robots can not only move but also change their effective shapes depending on the parts they carry or the material deposited. These variations can significantly impact the path-planning solution. In this paper, we propose a methodology to find time-optimal paths in swarm manufacturing while explicitly considering the effective and changing shapes of mobile robots and the dynamic obstacles surrounding them.Mechanical Engineerin