Keynote Speakers

Abstract
This presentation focuses on the strategic roadmap, overcoming current limitations, and the integration of additive manufacturing (AM) into large-scale industrial production. The key deficiencies of the currently available AM techniques will be discussed in addressing the speed and scale, automation and post-processing and hybrid manufacturing through the research conducted at Michigan State University (MSU) till 2024 and currently at San Diego State University (SDSU). The discussion starts with one of the older AM techniques, binder jet printing (BJP), to improve the attainable density by optimizing powder distribution and incorporating sintering additives. Building on this advancement, a novel technique called scalable and expeditious additive manufacturing (SEAM) has been developed to enhance the productivity. SEAM prints the mixture of metal powder and photopolymer using DLP projectors and, after printing is completed, the photopolymer holding the powder must be burnt out from the printed part while promoting the necking among the powder. Similar to BJP, the part is subsequently sintered in a vacuum furnace. Using Selective Laser melting (SLM), a unique functionality in shape memory alloys can be produced by manipulating the processing parameters such as laser power, speed, hatching speed and layer thickness. The processing conditions of electron beam melting (EBM) were optimized and concluded that the fatigue strength is consistent if the processing conditions are restricted to attain near full density. The subsequent HIPing conditions significantly impact the fatigue strength. At SDSU, two new approaches are being explored, (1) the utilization of modulated electricity to facilitate neck formation among powder particles, followed by sintering in an environment-controlled furnace and (2) the utilization of friction stir welding and burnishing of wire mesh/powder. This talk will also provide a comparative analysis of various additive manufacturing techniques, highlighting their respective advantages and limitations.

Abstract
Atomic and close-to-atomic scale manufacturing (ACSM) aims to realize cost-effective, deterministic, and scalable manufacturing of next-generation products with atomic-level precision by addressing quantum uncertainty in atomic-level material manipulation (removal, migration, and addition). It is an inevitable outcome shaped by the three fundamental paradigms of manufacturing advancement. This talk will systematically introduce the background, key driver and research framework of ACSM, including its key scientific values, issues and objectives. The key focuses of the talk are on the underpinning fundamental knowledge and theory, and potential industrially viable processing technologies for realising ACSM, followed by scientific and technological challenges, potential solutions and future research perspectives.

Abstract
TBA

Abstract
Diamond is a critical platform material for next-generation power electronics, quantum information devices, and advanced photonics. Yet its extreme hardness and chemical inertness pose formidable challenges to achieving atomically smooth surfaces, especially for as-grown substrates and polycrystalline wafers exhibiting significant grain-boundary steps. This keynote highlights recent advances in plasma-assisted polishing (PAP) that fundamentally redefine the manufacturability of diamond substrates.
For single-crystal diamond, PAP enables surface finishing with surface roughness on the order of 0.1 nm (Sa) across all crystallographic orientations, demonstrating orientation-independent, damage-free material removal. For polycrystalline diamond substrates, PAP achieves sub-nanometer smoothness with essentially negligible grain-boundary steps, thereby realizing near–grain-boundary-step-free surfaces even on engineering-scale wafers. The integration of laser trimming for rapid planarization with PAP for final finishing dramatically reduces total processing time for flattening and smoothing as-grown substrates, establishing a highly efficient workflow suitable for industrial deployment.
Furthermore, density functional theory (DFT) simulations have been conducted to elucidate the mechanistic origin of experimentally observed differences in polishing rate and plate wear among various polishing plate materials. The computational results qualitatively account for the trends in chemical reactivity, bond strength, and surface stability of candidate plate materials, thereby providing a fundamental explanation for why certain materials enable higher polishing efficiency and reduced wear under plasma-assisted conditions.
Together, these developments position PAP as a transformative approach for high-performance diamond manufacturing, delivering unprecedented throughput, controllability, and surface quality for both single-crystal and polycrystalline substrates.

Abstract
In ultra-precision machining, the deformation behaviour of materials is profoundly affected by the transient physicochemical state and interactions of the workpiece’s near-surface layer. Distinct materials present inherent challenges. Brittle materials are prone to uncontrolled fracture and subsurface damage, whereas ductile metals suffer from excessive plastic flow and material pile-up. Consequently, achieving damage-free surfaces relies not only on processing parameters but also on overcoming these intrinsic material limitations. These issues highlight the need for mechanisms that can temporarily modulate surface properties without permanently altering bulk characteristics. Therefore, this study investigates such mechanisms designed for specific materials. In single-crystal rutile TiO₂, lattice-intercalation-based modulation is explored, where the insertion of foreign ions induces a controllable solid-solution layer that significantly reduces hardness and mitigates brittle fracture. This is also confirmed as a highly reversible modification pathway that ensures the final component retains its intended physical properties post-machining. On ductile copper, water weakens metallic bonding through combined physisorption, chemisorption, and nanoscale diffusion, leading to reduced cutting resistance. In contrast, on ionic CaF₂, water primarily decreases surface energy through physisorption, which promotes crack propagation, prevents ductile-regime machining, and worsens machining damage. These findings demonstrate that machinability modulation should be matched to the material’s physicochemical nature, and that transient physicochemical interactions serve as a key factor in determining machining performance.

Abstract
Near-field optical scanning microscopy, which integrates optical measurement with scanning probe technology, retains the advantages of both methods while imposing no special requirements on the measurement environment or test samples. It achieves nanoscale lateral resolution and enables simultaneous multi-parameter measurement of topography and optical information, thereby providing structural, optical field, electrical, and chemical information of the relevant samples. To address challenges in existing topography measurement and near-field optical detection, we developed a series of near-field optical and atomic force probes and corresponding imaging methods. To overcome the dependency of fiber probes on radially polarized light, we designed fiber probes with asymmetric half-rings, azimuthal asymmetry, and asymmetric prism structures. These probes enable focused light fields at the tip under linearly polarized light incidence, significantly simplifying the optical path system of near-field optical microscopes. Furthermore, by optimizing the probe structure and adopting a platform design to enhance the focusing intensity of the tip optical field, the measurement signal-to-noise ratio was improved. In scattering-type near-field optical microscopy, high-resolution and high signal-to-noise ratio near-field optical imaging was achieved by collecting higher-order near-field signals. For complex topographies such as high-aspect-ratio structures, we developed carbon nanotube composite probes, enabling both topographic and near-field optical imaging of such intricate architectures. We successfully imaged large aspect ratio grating samples and achieved a far higher degree of shape reconstruction than with ordinary probes using the aforementioned series of probes.

Abstract
Ultra-low friction and wear resistance under high pressure are essential for advanced mechanical systems, such as ultra-precision spindles, aerospace bearings, and high-temperature gears. Fundamentally, wear begins with the fracture of surface asperities under high local stress. Therefore, enhancing tribological performance requires synergistic optimization of surface precision (achieving sub-nanometer roughness and nanometric form accuracy) and lubrication control (active regulation or boundary lubrication modulation). A particularly effective approach is close-to-atomic-level polishing combined with solid-liquid synergistic lubrication, enabling ultra-low friction under elevated temperatures. For instance, steel with nanometer-level roughness, coated with layered materials and lubricated with ionic liquids, achieves outstanding friction reduction and wear resistance under high temperatures. This synergy leverages atomic-scale surface engineering, advanced solid lubrication, and effective boundary film formation. This integrated strategy has broad applications: it effectively suppresses blackening in polyimide bearing cages, prevents batch failures in turbine blade tenons, and offers a promising breakthrough for critical sealing components.

Abstract
Deep learning-driven fringe structured light 3D reconstruction and measurement, represented by typical fringe projection profilometry (FPP) technology, has become a growing research focus, enabling real-time and high-precision 3D sensing. However, the internal 3D reconstruction mechanism of FPP-including the multi-step grating-to-depth transformation, the unique image phase characteristics, and the demand on micron-level accuracy-poses fundamental challenges that are not adequately addressed by conventional deep learning architectures. Further, the measured objects tend to possess complex and diverse surface characteristics in practical scenarios and the problem of high dynamic imaging range is inevitably encountered. To address problems aforementioned, we first propose a physically aligned system design methodology, including high-precision system calibration, background and space dependent FPP 3D dataset construction strategy, and multi-path physics-supervised single-frame end to end 3D reconstruction (MPS). Benefiting from the proposed system and pretraining on a dataset of roughly 1,000 groups, MPS achieves a reduction of nearly 50% in absolute phase mean absolute error (MAE), marking a substantial advancement in the precision of SF-FPP techniques. Meanwhile, an end-to-end deep learning network based on high dynamic structured light, namely HDRSL-Net was developed, achieving automatic compensation in overexposed and underexposed regions, breaking through the bottleneck of insufficient speed in traditional multi-exposure methods, and realize robust 3D reconstruction under conditions of complex illumination and strong reflection. These technical schemes offer paradigms tailored for FPP-3D and holds great promise for advancing 3D reconstruction in intelligent manufacturing and precision inspection.

Abstract
Nature-inspired functional surfaces offer extraordinary capabilities in regulating wettability and adhesion, yet bridging laboratory design with industrial scalability remains a critical challenge. As we advance into the era of Atomic and Close-to-Atomic Scale Manufacturing (ACSM), the manufacturing paradigm is shifting toward deterministically manipulating matter at the molecular level to enhance macroscopic performance. This keynote address presents a comprehensive framework developed at Dalian University of Technology, synergizing scalable nanomanufacturing with precise molecular regulation. We elucidate a “Design-Manufacturing-Performance” nexus that integrates top-down techniques, such as template-based manufacturing, with bottom-up ACSM strategies aimed at controlling intermolecular interactions and atomic stacking sequences. Specifically, we will discuss how regulating chemical bonding states and atomic-scale assembly allows for the construction of complex hierarchical structures with high fidelity and functional longevity. By coupling micro-scale structural design with atomic-scale surface energy control, we address the trade-off between mechanical durability and functional sensitivity. Key applications include mechanically durable superhydrophobic coatings for anti-icing and drag reduction, as well as conductive micro-architectures for high-sensitivity flexible sensors. We aim to demonstrate that integrating ACSM principles—specifically atomic-scale inputs via molecular regulation—into scalable manufacturing processes unlocks new potentials in energy and smart devices, offering a future perspective on achieving robust, multifunctional surface integration.