Abstract To address the challenge that flexible pressure/strain sensors often struggle to simultaneously achieve high sensitivity and high linearity in practical applications, this work proposes a bioinspired design approach that incorporates digital twin concepts. By analyzing the mechanosensory strategies of arthropods in complex environments, key features are summarized, including multi-scale structural organization, mechanical modulation, and front-end signal preprocessing. Based on these insights, a flexible sensing system integrating structural design, material regulation, and signal processing is developed, enabling the coordinated improvement of sensitivity and linearity over a relatively wide operating range, and partially alleviating the performance trade-offs in conventional sensors. In addition, data-driven methods and digital twin design are introduced to establish a basic framework of “physical sensing-virtual mapping-signal interpretation,” enhancing the system’s adaptability to complex conditions. This work explores the design of flexible high-precision pressure/strain sensors from a biological perspective and may provide useful references for related sensor development.

Abstract Digital twins for manufacturing should not only predict machine behavior, but also indicate when their predictions are reliable enough to support action. This presentation introduces an uncertainty-aware digital twin framework for thermal error compensation in precision machine tools. A vertical machining center instrumented with a dense thermal sensor network is used to construct a high-resolution representation of machine thermal states. A temporal prediction model is combined with quantile regression and split conformal calibration to generate calibrated predictive intervals for tool-center-point thermal displacement. These intervals are further translated into a supervisory compensation logic with accept, conservative, and hold actions. Long-duration thermal drift experiments show that calibrated uncertainty improves prediction trustworthiness and supports safer compensation decisions by reducing residual tail risk and over-correction. The study highlights a pathway for transforming manufacturing digital twins from passive prediction models into decision-ready systems that integrate sensing, uncertainty calibration, and engineering action.

Abstract This talk presents a research journey towards establishing physics-based digital twins as design instruments for targeted brain therapies. We begin by addressing a fundamental question: why is drug transport in the brain so difficult to predict? Early work revealed that fluid-structure interactions at the microscale, between interstitial flow and deformable neural structures, govern macroscopic transport behaviour. Building on this, we developed microstructure-informed models linking tissue architecture to permeability and transport properties. We then demonstrate how these mechanisms enable design insights, such as optimising catheter placement in anisotropic brain tissue to control drug distribution, and understanding how tumour microenvironment regulates transport. Finally, we present a fully validated, image-informed framework capable of predicting brain-wide transport in vivo. This progression illustrates how mechanistic modelling evolves into digital twins that support virtual prototyping, design optimisation, and personalised therapeutic planning.

Abstract Grounded in the context of Industry 5.0,  this talk introduces the Super Systems Digital Twin (SSDT), a digital twin-driven design approach that directly supports early-stage engineering design and decision verification for Smart Product-Service Systems (Smart PSS). Moving beyond conventional asset-mirroring, the SSDT enables designers to construct and simulate large scale of usage scenarios and service scenarios both in virtual and real environment, allowing design alternatives to be explored and validated before physical implementation. The application cases in intelligent connected vehicle (ICV) domain illustrates how the SSDT supports early-stage product development and design decision-making, leading to more value-driven and service-oriented outcomes.

Abstract This talk presents a Design for Excellence (DFX) framework for product design and development in the AI era, structured around two core principles: "Doing the Right Things" and "Doing Things Right." For "Doing the Right Things," we integrate fundamental research with Design Thinking, QFD, and TRIZ throughout the product lifecycle—from requirements to production—to uncover blind spots, address pain points, and create hit products. For "Doing Things Right," we adopt quantitative DFX balancing DFF, DFAA, DFM, DFC, and DFR. The design process is treated as complex decision-making, enabling proactive quality control, prevention of production issues, shortened NPI time, and enhanced knowledge reuse. This report provides both a conceptual perspective and practical insights into implementing DFX in the AI era, supported by integrated toolchains and real-world considerations for innovation-driven product development.

Abstract The increasing activity in lunar exploration, particularly around the Lunar South Pole, abundant in resources, highlights the urgent need for collaboration and interoperability between diverse stakeholders. Digital twins (DTs) have emerged as a powerful tool to support coordination, decision-making, and mission reliability, yet current implementations remain fragmented, relying on proprietary tools and lacking standard interfaces. We developed a proof-of-concept for standard-based interoperability between distributed and disparate Digital Twins (DT) systems for lunar exploration. The project was undertaken by a team from California State University Northridge (CSUN), NASA Jet Propulsion Laboratory (JPL), Verses AI Inc., and the Spatial Web Foundation. We also leveraged Large Language Models (LLMs) and Multimodal Language Models (MLMs) (e.g., vision-language or video-language models), which provide human-like reasoning, summarization, and few-shot learning capabilities. We reconstruct DTs by retrieving CAD models and generating corresponding Cosmos physical AI models in the built Omniverse virtual Lubar South Pole environment.

Abstract The transition to a circular aluminum economy is hindered by inefficient sorting of mixed alloys, which degrades material quality and drives downcycling. While Laser-Induced Breakdown Spectroscopy (LIBS) offers precise, composition-based sorting, its industrial deployment is severely limited by scrap flow instability and measurement sensitivity. This presentation introduces a high-fidelity digital twin framework designed to optimize scrap flow and enable robust LIBS sorting. The proposed architecture integrates the complex, elastoplastic geometries of real-world scrap particles alongside real-time virtual camera sensing and adaptive process control. By achieving stable particle singulation virtually, this "design via digital twin" approach can mitigate physical testing bottlenecks, paving the way for sorting high-specification recycled alloys and advancing industrial circularity.