Remote center-of-motion (RCM) mechanisms are widely used in robotic palms and wrists, orientation platforms, and medical devices. A spherical mechanism provides a compact RCM realization by enforcing the intersection of all joint axes at a common center. This article introduces a topology-morphing 7R spherical mechanism that switches from a single-loop configuration with compliant motion into a double-loop spherical scissors configuration with substantially increased stiffness. The architecture is modeled using a graph-based description in which the mobility is expressed as a function of the number of links, holes, and tails. A unified kinematic and static formulation is then derived for both topology modes using a consistent joint coordinate set and Jacobian structure. Reconfiguration conditions are established from the pinhole coincidence constraints, and an algorithm is provided to compute admissible reconfiguration configurations. The reachable orientation workspace and manipulability are evaluated by sampling on the unit sphere. Under identical end-effector loading (0-2N), rigid-body simulation predicts an equivalent stiffness increase from 0.3N/mm to 571N/mm. Prototype experiments further confirm compliance with rigid switching, with stiffness increasing from 0.106N/mm to 0.525N/mm. These results indicate that topology-morphing on the sphere can provide a compact mechanism-level strategy for switching mobility and effective stiffness under the same actuation scheme.
Remote center of motion (RCM) mechanisms are widely used in minimally invasive surgery (MIS) due to their ability to provide rotational motion about a fixed spatial point through geometric mechanical constraints. In this article, a family of partially decoupled 2R1T RCM mechanisms is presented, characterized by three distinct features: base-mounted prismatic actuators, unilateral arrangement of branches, and partial motion decoupling between translation and rotation. First, the motion combination principle underlying this family of partially decoupled 2R1T RCM mechanisms is established. Second, based on the Lie group and submanifold method, a systematic type synthesis is performed, yielding numerous mechanism configurations that satisfy the above three characteristics. Third, the kinematics and the unified Jacobian matrix of a representative mechanism are derived. Finally, the singularity and workspace of the selected mechanism are analyzed, with particular attention to verifying the partial decoupling property. The results show that the proposed 2R1T RCM mechanisms meet the kinematic and workspace requirements typical of minimally invasive surgery.
Abstract Communication plays a central role in how engineering design teams generate ideas, negotiate decisions, and make progress. Interaction Dynamics Notation (IDN) offers a structured way to study these interactions, but its reliance on manual, post hoc coding limits how widely and quickly it can be used. This article introduces a system that automatically classifies IDN symbols in real time from live team conversations. The system combines speech transcription with a lightweight language model and a short conversational context window to label interactions as they occur. The approach is evaluated in a professional design sprint and compared against human-coded IDN data. Results show that the real-time system achieves overall accuracy comparable to human inter-coder reliability and to prior automated methods that operate only after a conversation has ended. Hidden Markov model analysis further indicates that the AI-coded data captures the same dominant interaction patterns and transitions observed in human coding, while smoothing some finer distinctions. A sensitivity analysis of context window size highlights practical trade-offs between accuracy and latency. Overall, this work makes IDN more accessible for studying team interactions and supports future tools that help teams understand and reflect on their communication as it unfolds.
Abstract Engineering design is undergoing a profound transformation driven by Generative Artificial Intelligence in Design (GAIDe). By unlocking high-dimensional, multimodal exploration across design ideation, synthesis, and evaluation for materials, manufacturing, and beyond, GAIDe enables a fundamental shift from traditional stage-gated engineering workflows to stage-coupled human-AI collaborative processes. This special issue brings together 15 accepted papers that capture this paradigm shift. The collection is structured into four thematic groups: (1) AI-assisted ideation, multi-agent collaboration, and creativity, (2) representation modalities, human-AI decision-making, and aesthetics, (3) design knowledge formalization, requirements, and agentic workflows, and (4) physics-informed, reliability- and manufacturing-constrained generative design. By mapping these contributions to primary design activities, problem definition, concept generation, design development/analysis, and optimization/validation, this editorial establishes a foundational framework for understanding how GAIDe strengthens early-stage understanding of design problems and brings downstream considerations directly into early-stage design synthesis.
Sketching is seen as a key part of the early-stage design process, enabling designers to communicate conceptual ideas to one another and to external stakeholders. The introduction of image-generative artificial intelligence (AI) opens up new possibilities for AI-assisted sketching, which may be a useful tool for designers. In the present study, undergraduate product design students received introductory training in an image-generative AI tool, vizcom, which uses multimodal text- and sketch-based input to create more photorealistic rendered outputs. The purpose of the study was to characterize the relationship between the qualities of the sketch inputs and the AI-generated outputs. The input sketches and output renders were also compared in terms of understandability. Investigating what kinds of sketches are most useful to use as inputs for generative AI tools is important for both design instruction and practice. Our study found that line quality, proportionality, and understandability of the input sketches were all positively correlated with the proportionality and understandability of the AI-generated outputs from vizcom. We also found no significant differences in the level of understandability between the input sketches and the AI-generated outputs. Our findings indicate that input sketches with correct proportions and clean lines are ideal inputs for AI-aided sketching that utilizes vizcom or similar programs to represent design concepts.
