Abstract Title: to be Announced

Abstract Atomic-scale defects in materials (including impurity atoms, vacancies, and others) have a decisive impact on material properties. Such defects are not only unavoidable products of material synthesis but have also become critical tools for performance modulation in modern materials science, with wide applications in the engineering of alloys, semiconductors, and functional materials. With advances in materials growth techniques and atomic-scale characterization methods, the precise design and control of atomic defects has become feasible, showing great potential for practical applications. However, the configurational space of defects is vast, and research approaches relying solely on experimental trial-and-error or conventional first-principles calculations are insufficient for systematic exploration. To address this challenge, we propose an integrated solution combining high-throughput first-principles calculations, database construction, and artificial intelligence techniques. Specifically, we aim to establish a defect genome engineering database and develop intelligent predictive algorithms to accelerate materials research. This work will present progress in the following areas: (i) thermodynamic/kinetic properties and electronic mechanisms of semiconductor defects; (ii) atomic-scale characterization and identification techniques of defect structures; (iii) a high-throughput computational database of material defects; and (iv) defect property prediction models based on physics-informed graph neural networks.

Abstract This research explores the integration of human-robot collaboration (HRC) in intelligent disassembly processes for end-of-life (EoL) products. The unpredictable condition and variety of returned items make full automation economically unviable. This work proposes a hybrid system where a robot performs precise, repetitive tasks and handles hazardous components, while a human operator provides high-level decision-making, adaptability, and dexterity for complex operations. Leveraging intelligent perception systems like computer vision and AI, the robot identifies products, assesses condition, and plans disassembly sequences in real-time. The collaborative framework ensures safety and efficiency through intuitive interfaces. This HRC approach significantly enhances productivity and reduces human exposure to hazardous environments, presenting a viable and sustainable model for modern recycling and circular economy initiatives.

Abstract This study presents a digital thread framework for design-to-print integration in Wire-Laser Directed Energy Deposition (WL-DED), addressing the lack of WL-DED-specific design constraints and verification standards in mainstream generative design (GD) workflows. A dual-gate methodology, comprising a design gate and process gate, is developed to unify performance optimization, manufacturability verification, and evidence archiving within a single traceable workflow. Using an aerospace gear as the demonstrator, the digital thread links GD with human-in-the-loop reconstruction, finite-element verification, slicing, AM-DED thermal simulation, and trial printing. Compared with the baseline model, the optimized design achieved around 23–24% mass reduction, lowered equivalent stress and strain, and maintained modal safety margins well above operating frequencies. Process simulation and experimental validation confirmed controllable thermal distortion and path continuity, supporting reliable manufacturability. The proposed framework transforms the digital thread from a conceptual data conduit into a decision-governance mechanism, where quantitative gate criteria and archived evidence enhance reproducibility, comparability, and risk traceability. This work demonstrates a practical pathway to close the design-manufacture verification loop for WL-DED components, advancing digital-thread implementation in additive manufacturing toward more auditable, data-driven, and scalable design-to-print practice.

Abstract Despite Digital Twins (DTs) hold the potential to change the way in which manufacturing systems are designed, managed, and optimized, the still is a significant gap between academic research on and their implementation in industry. This is enlarged by a fundamental misalignment: the absence of a shared definition and conceptual understanding of what a DT entails. Without this common foundation, the development of practical guidelines and tools to transfer knowledge from academia to industry is severely hampered. This presentation outlines a two-phase roadmap to bridge this gap. The first phase aims to provide a manufacturing-centric synthesis of DT knowledge, emphasizing its granular nature to achieve terminological alignment. The second phase aims to addresses operationalization by providing a framework to assess industry maturity and target specific application needs. Once a clear objective is defined, the focus shifts to leveraging practical tools for the systematic design, development, and deployment of these systems. This structured approach aims to support the translation the theoretical potential of DTs into tangible value for the manufacturing industry.

Abstract The global surge in electronic waste underscores the critical challenge of managing End-of-Life (EoL) Printed Circuit Boards (PCBs), whose complex, heterogeneous design often renders disassembly—a prerequisite for a circular economy—impractical. Current evaluation methods, focused predominantly on time and cost, fail to capture the structural and operational barriers inherent in real-world disassembly processes. This project posits the Digital Twin (DT) as a novel decision-support tool to bridge this gap. By integrating product data, semantic models, and reasoning capabilities, a DT can simulate diverse EoL scenarios. This enables designers to proactively identify disassembly barriers, assess the feasibility of circular strategies like reuse and high-quality recycling, and receive actionable feedback for design modifications. Consequently, the research reframes DTs as key enablers of disassemblability, offering both a conceptual extension of disassembly assessment and a practical system to embed circularity directly into PCB design, thereby closing the loop between beginning and end-of-life.