Transformer-Based Interfaces for Mechanical Assembly Design: A Gear Train Case Study

TL;DR

A transformer model tailored for gear train assembly design, paired with two novel interaction modes: Explore and Copilot, are introduced, suggesting that hybrid workflows combining both modes can effectively support complex, creative engineering design processes.

Abstract

Generative artificial intelligence (AI), particularly transformer-based models, presents new opportunities for automating and augmenting engineering design workflows. However, effectively integrating these models into interactive tools requires careful interface design that leverages their unique capabilities. This paper introduces a transformer model tailored for gear train assembly design, paired with two novel interaction modes: Explore and Copilot. Explore Mode uses probabilistic sampling to generate and evaluate diverse design alternatives, while Copilot Mode utilizes autoregressive prediction to support iterative, context-aware refinement. These modes emphasize key transformer properties (sequence-based generation and probabilistic exploration) to facilitate intuitive and efficient human-AI collaboration. Through a case study, we demonstrate how well-designed interfaces can enhance engineers' ability to balance automation with domain expertise. A user study shows that Explore Mode supports rapid exploration and problem redefinition, while Copilot Mode provides greater control and fosters deeper engagement. Our results suggest that hybrid workflows combining both modes can effectively support complex, creative engineering design processes.

Figures (12)

• Figure 1: An illustration of GearFormer's inference process. A tokenized representation of the partially assembled gear mechanism is passed into a transformer model, which predicts the probability distribution over the next possible components. The next component is selected either as the most probable or by sampling from the distribution. The top predictions and their corresponding probabilities are shown on the right.
• Figure 2: Schematic overview of Explore Mode's sampling-based workflow. Users define design objectives and constraints, prompting GearFormer to generate multiple candidate assemblies, which are validated and then presented for rapid comparison.
• Figure 3: Overview of Copilot Mode's iterative design workflow. At each step, GearFormer provides ranked part and placement recommendations based on its autoregressive model. Designers select components and positions with confidence overlays, progressively building the assembly. Real-time feedback on feasibility and alignment with design objectives—such as speed ratio and output position—is shown alongside the evolving 3D model. The process continues until a complete, validated gear train is assembled.
• Figure 4: Explore Mode interface, illustrating the Design Objectives panel for specifying transformer inputs (left), an interactive Pareto front graph for visualizing trade-offs (top center), Generated Design Tiles for rapid performance comparisons (bottom), and sorting controls for ranking designs by cost or performance (right).
• Figure 5: Detailed inspection interface for Explore Mode. A selected design is rendered in an interactive 3D view (left), displaying each gear and shaft. A Bill of Materials (right) lists individual components by name, cost, and weight, while a metrics panel (top center) reports how closely the design meets the user's speed ratio and position targets.