While conventional electron microscopy effectively characterizes machining-induced surface damage, it lacks the sensitivity to detect quantitative subsurface phase evolution, leaving a critical diagnostic gap. To address this, the present study utilizes grazing-angle X-ray diffraction (GA-XRD) to provide complementary information to SEM based results. Key findings include: i) GA-XRD can create quantitative phase profiles by correlating incident angle to depth, and ii) the identification of distinct diffraction signatures can resolve diagnostic ambiguities of samples undergone different thermal load. These results demonstrate that integrating GA-XRD with microscopy is essential for a comprehensive, depth-resolved understanding of surface integrity.

The rise of multisection continuum robots (CRs) has captivated researchers and practitioners across diverse industries and medical fields. Researchers have devised a spectrum of model-based and learning-based strategies to conquer the modeling problem and elevate control performance. Despite the advancements in these approaches, they encounter challenges stemming from their complex design and intricate learning processes. This article introduces a simply structured, model-free fuzzy logic controller (FLC) for the closed-loop control of continuum robots. This controller boasts a built-in shape classification algorithm. This algorithm allows it to achieve robust control using only the feedback of end position and orientation, significantly reducing sensor dependence. It efficiently adapts to various nonlinearities such as hysteresis, cable elongation, and unexpected external disturbances. The experimental results conclusively demonstrate the accuracy and robustness of the proposed FLC. On a three-section, six-degree-of-freedom continuum robot, it achieved a minuscule trajectory tracking root mean square error from 0.31 to 1.00 mm, representing just 0.19% to 0.60% of the robot’s length. Additionally, the controller demonstrates robustness by successfully handling unexpected external disturbances during the trajectory tracking.

Additive manufacturing by laser cladding has gained popularity in recent years but optimising the outcomes (e.g. microstructure) remains a challenge due to the difficulty of heat placement control. To address this, research has been conducted to generate a more uniform temperature distribution to seek hinger mechanical properties by beam oscillation strategies. However, these approaches need to determine the optimal conditions from a significant number of parameter combinations, which requires extensive iterative trials. Moreover, fixed parameters and constant motion paths still generate localised laser energy concentrations. In this work we proposed a novel laser beam scanning optimisation approach, whereby the heat placement can be controlled by tuning the laser beam configuration through solving the inverse problem. Unlike conventional approaches, it directly derives optimised parameter patterns from a target temperature distribution, eliminating the need for iterative simulations and experiments. A speed-controlled beam path strategy is implemented to realise a uniform target temperature on the wire feeding surface during stainless steel 316L deposition. Thermal analysis proved that the optimised system achieved the uniform temperature distribution on the cladding surface, within the target temperature on the cladding surface for ensuring a thermally and microstructurally stable deposition. Experimental characteristics also confirmed that suppressing temperature fluctuations and the effectively restriction of grain coarsening and precipitation, leading to a substantially finer and more homogeneous grain structure compared to the conventional linear scan. This research also shows potential for future application of other laser heat placement control processes such as selective laser melting or laser heat treatments.

Dielectric elastomer actuators (DEAs), also recognized as artificial muscle, have been widely developed for the soft locomotion robot. With the complaint skeleton and miniaturized dimension, they are well suited for the narrow space inspection. In this work, we propose a novel low profile (1.1mm) and lightweight (1.8g) bi-stable in-plane DEA (Bi-DEA) constructed by supporting a dielectric elastomer onto a flat bi-stable mechanism. It has an amplified displacement and output force compared with the in-plane DEA (I-DEA) without the bi-stable mechanism. Then, the Bi-DEA is applied to a thin soft robot, using three electrostatic adhesive pads (EA-Pads) as anchoring elements. This robot is capable of crawling and climbing to access millimetre-scale narrow gaps. A theoretical model of the bi-stable mechanism and the DEA are presented. The enhanced performance of the Bi-DEA induced by the mechanism is experimentally validated. EA-Pad provides the adhesion between the actuator and the locomotion substrate, allowing crawling and climbing on various surfaces, i.e., paper and acrylic. The thin soft robot has been demonstrated to be capable of crawling through a 4mm narrow gap with a speed up to 3.3mm/s (0.07 body length per second and 2.78 body thickness per second).

Micrometric cutting has attracted great research interest both in academic and industrial areas due to its vital role in microfabrication. Different from macro-cutting where many grains are involved in the process that averages/minimises the crystallographic effects, in micrometric cutting localised crystallographic deformation mechanisms can highly affect the machining results. However, experimental challenges of micrometric level cutting have confined most investigations to simulations or less-ideal tests (e.g. micro-scratching and single-point diamond turning), leaving the detailed interplay between chip flow and microstructure largely unexplored. In an apparent paradox, in orthogonal micrometric level cutting conditions, expected to yield forward (2D) chip flow, can produce pronounced sideway (3D) chip flow when grain-orientation anisotropy and boundary-induced kinematic constraints dominate; such aspects cannot be captured in macro-cutting. Based on these, when cutting at micrometric level it is important to understand how the slip system of the grains will be activated, what will happen when the grain boundary is encountered, as well as why the specific chip form and flow direction is generated under different conditions of these. To resolve this, grain orientations and boundaries were pre-characterised on a Ni-based superalloy sample, on which micrometric boss features were subsequently fabricated. Orthogonal grain-level cutting tests were then conducted on these structures, effectively isolating the deformation region, eliminating constraints from adjacent material, and allowing the chip to flow freely on both sides. Chip morphology, flow direction, and local deformation mechanisms were examined via advanced material characterisation technologies. Key findings that are specifically manifested at micro level include: The formation of serrated chips is influenced by the Schmid factor, resulting in variations in segment morphology across different crystallographic orientations. Sideway chip flow can be generated in micrometric orthogonal cutting process due to the selective activation of the slip-systems and inclined grain boundary guided sliding. Furthermore, when twin boundary exists, periodic extrusion-shear dominated material deformation cycle can happen in chip formation process due to the alternated stress. Therefore, we reveal for the first time that when cutting at grain levels, although geometrically defined orthogonal cutting was performed, the chip follows crystallographic rules imposed by slip planes and grain boundary conditions. These insights provide a new mechanistic framework for understanding micrometric level cutting anisotropy and the boundary-driven paradox of sideways chips, offering guidelines to optimise micrometric machining strategies in microfabrication applications.

In recent years, the widespread adoption of laser fusion processes for metal coalescence, including laser welding and laser cladding, is gaining ground due to increased demand for the fabrication of increasingly complex shapes and dissimilar materials. While thermal field management is important for controlled process quality, modulating heat input in laser processing has become a critical challenge. One approach is to control the melting behaviour through laser beam oscillation, which not only avoids the excessive heat input of conventional methods but also enables control of the temperature distribution to manage the metal fusion width and penetration depth, while preventing fusion defects. However, parameter optimisation for beam oscillation has mainly been adopted using experimental approaches (i.e., relying on empirical decisions). Consequently, in recent years, parameter optimisation approaches incorporating multi-physics simulations have also been developed to better understand and control the laser heat input process. This study reviews the recent improvements in laser fusion processes achieved through beam oscillation. It introduces two approaches (“direct problem” and “inverse problem”) that incorporate multi-physics simulation to address the increasing complexity of laser parameter optimisation. The inverse problem still presents several additional challenging modelling considerations, including mathematical boundary conditions, which provide important guidance for improving robustness and efficiency in future practical optimisation frameworks. Finally, this paper outlines future directions by integrating advanced equipment, data-driven simulation optimisation, and sensing systems for closed-loop control, aiming to establish a highly adaptable and efficient laser fusion process with strategies suitable for practical industrial applications.

Continuum robots (CRs) require precise shape sensing for reliable operation in constrained industrial and medical environments. We present a unified neural network that improves fiber Bragg grating (FBG) shape sensing across two distinct CRs (a 165 mm fully actuated robot and a 1.5-m hybrid robot in a mock aeroengine) and multiple fiber configurations. Using camera-tracked ground truth, a single per-fiber model corrects distal positions from raw FBG estimates, implicitly compensating for twist and placement errors. On the 165-mm robot, we achieve 0.95-mm root mean square error (RMSE) (0.57% length); on the 1.5-m robot, we achieve 2.02 mm (0.13%) in 2-D and 1.78 mm (0.11%) in 3-D, while reducing worst case errors by up to an order of magnitude versus baselines. The compact model runs at >100 Hz and enables real-time, camera-free operation.

The design of efficient, lightweight stator hairpins, i.e., featuring both Aluminium and Copper (Al-Cu) windings, is at the core of the future of electric vehicle powertrains. However, reliable dissimilar Al-Cu hairpin joints remain a challenge due to their differing thermal properties and the formation of brittle intermetallic compounds (IMCs) during welding. In this paper, a predetermined sample placement, laser beam modulation, and trajectory optimisation are proposed to control heat flow and interfacial reactions during Al-Cu welding. Using a spiral laser beam path with a multi-pass thermal cycle yields stable joints free of defects, with significantly improved mechanical and electrical properties. The optimised beam modulation reduces localised overheating leading to homogeneous melt-pool dynamics, and suppresses excessive IMC growth, favouring a fine-grained interfacial microstructure. Quantitative analyses show a 32% decrease in IMC fraction, up to 70% decrease in porosity and 75% increase in tensile strength when compared to traditional welding (beam path). These results clarify the key role of spatial-temporal energy control in the design of microstructural evolution at dissimilar metal interfaces and show a feasible path for the design of high-performance and low-resistance Al-Cu connections for the next-generation of high-frequency electric motor systems.

In order to mitigate the challenges associated with exploration and inspection within confined environments, a cost‐effective (<£150) hand‐wearable system (i.e., EyeGlove) is proposed to help operators track their hand movements when performing camera‐based inspection in confined environments by leveraging the inherent dexterity of the human hands as manipulators. The EyeGlove system integrates two low‐cost cameras and two sets of contact pads (working with magnets), thereby creating a stereo camera system characterized by disjointed camera configurations. When wearing the EyeGlove system, operators harness the manipulation capabilities of their hands to pose the cameras, enabling real‐time in situ visual inspection within confined spaces. While the construction of the measurement function has been reported, we are now enhancing the facility by enabling hand tracking for in situ maintenance and service. This provides a comprehensive solution encompassing inspection, measurement, and tracking capabilities, setting the EyeGlove system apart from other systems. Building upon the unique design of the EyeGlove system, which features disjointed camera configurations, this paper proposes a novel hand tracking method tailored to the low‐cost EyeGlove camera, based on sparse optical flow techniques, i.e., the Lucas–Kanade method. The proposed tracking method utilizes optical flow‐based keypoint/feature match techniques in both stereo match and frame match to achieve robust hand movement tracking, which is specifically customized for the low‐cost cameras of the EyeGlove system. Finally, validation experiments have been conducted to demonstrate the tracking performance of the EyeGlove system in confined environments with various lighting conditions.

Long cable-driven continuum robots (CDCRs) often suffer from backlash due to cable elongation and friction, which severely impairs control accuracy. This paper presents a novel in-situ cable displacement sensing approach based on optical flow sensors and an adaptive method for real-time backlash estimation and compensation. These lightweight, low-cost sensors are embedded in the robot’s distal section. Experimental validation was conducted on single-cable setups and complete CDCR configurations, including a straight configuration and a simulated aircraft engine combustor environment with complex passive section geometry. The results demonstrate that the proposed method effectively compensates for the backlash in real time, significantly improving the trajectory tracking accuracy. Under different passive section shapes, the open-loop trajectory tracking RMSE is reduced by up to 66.0% compared to the baseline case with no compensation. The proposed method does not require external illumination or complex computation and exhibits strong robustness to ambient lighting variations, making it suitable for deployment in confined, dark, or flashing environments. This work offers a scalable and practical solution for mitigating backlash in long and flexible CDCRs, thereby enhancing control performance in industrial applications such as combustion chamber inspection.

Direct laser deposition, a specialized form of additive manufacturing, shows good potential in numerous high-value applications such as the repair of aeroengine blades. However, the traditional setup for this technique is bulky and not suited for in-situ repair, requiring the costly disassembly of the aeroengine. This letter presents a miniaturized high-repeatability tendon-driven robot that showed good potential for delivering additive manufacturing equipment for in-situ techniques like direct laser deposition. The integrated actuation and ruggedized control unit make the robot portable and suitable for a variety of aeroengines. The design of the robot actuation prevents excessive bending and damage to the fiber optic. Continuum robots have the advantage of flexible and redundant structures but present limited accuracy and repeatability. The optimized kinematics and actuation of the robot presented permitted to achieve an excellent repeatability with a standard deviation of 0.02 mm on a linear path and below 0.1 mm on a path that simulates the reconstruction of a blade. The robot showed excellent linearity on each segment of the path with a coefficient of determination to the 3D best-fit line of 0.999, while maintaining the commanded velocity magnitude of the end effector with a standard deviation along the whole path of 0.05 mm/s.

Interconnected intelligent systems in multi-stage smart machining environments are an advancing area of research, demonstrating many real-life opportunities that can benefit from the development and integration of cyber-physical systems into machining habitats, while different automation levels in industrial manufacturing sites call for flexibility of core strategies towards smart machining ecosystems. This article introduces a versatile and smart multi-stage machining environment for the controlled clamping and machining of low-rigidity structures in an interconnected cyber-physical factory. This is exemplified by a deformation-prone thin-wall workpiece, which undergoes controlled clamping, enabled by interchangeable robotic automation and automation via human-cyber-physical systems, as well as digital-twin-assisted corrective machining enabled by the swift estimation of workpiece deformations and multi-stage communication between machining habitats. The underlying digital twin presents a fast, lightweight simulation approach, based on a mass-spring-lattice model, allowing information flow from and to systems, which is utilized by the CNC machine as well as the interchangeable robot- and human-in-the-loop clamping enablers. By employing this controlled clamping approach workpiece deformations are aimed to be minimized. At the same time, a desired total clamping force is achieved in order to perform subsequent digital-twin-assisted machining corrections to reduce deformation-caused flatness errors. Ultimately, this article presents an intelligent multi-stage machining scenario where digital-twin enabled information moves along with thin-wall structures and branches out for knowledge-based control and corrections to robots, humans and CNC machines respectively, showcasing a real-life example for versatile, information-driven smart machining ecosystems.

The current research aims to provide a basic understanding of decoding sensor signals with chip formation and surface integrity during the machining of Ni-based superalloys. Force signal analysis provides cutting energy information, offering insight into chip formation and a limited understanding of surface integrity. Researchers have studied the correlation of acoustic emission (AE) signals with surface quality to address this limitation. However, there remains a fundamental gap in understanding how AE signal variations relate to sub-surface deformation in the machined region. The main complications of understanding the AE signal in milling are due to multiple tooth engagement, where one tooth cuts while others drag material and create flying chips. These actions result in cumulative AE signals, which makes it challenging to understand the fundamental relationship between material deformation and AE signals. To address these limitations, a pendulum-based cutting test methodology is proposed. This approach simplifies the milling process, isolating a single cutting-edge, and chip generation. In addition, the pendulum setup also allows for the analysis of a wide range of cutting speeds within a single test to obtain optimal cutting conditions. Furthermore, the study compares fine-edge and round-edge cutting tools. It reveals that fine-edge tools generate higher amplitude AE signals despite lower cutting forces. This indicates more aggressive cutting action and localised plastic deformation, leading to intense carbide cracking and subsurface damage. Conversely, round-edge tools produce lower amplitude AE signals, suggesting a more distributed stress pattern and reduced carbide cracking.

Active interactions at liquid-to-solid interfaces can significantly impact the mechanical response of solid substrates. Traditionally, these have been regulated through surface-active media, such as ionic liquids, used in a static (time-invariant) manner that relies on chemical tuning to induce specific mechanochemical responses. This study introduces a novel and sustainable class of Deep Eutectic Solvents (DESs) to demonstrate a dynamic (time-variant) mechanochemical effect, achieved through molecular electro-actuation at the fluid-to-solid interface. The dynamic micro-mechanochemical effect was demonstrated using a DES mixture consisting of citric acid and choline chloride in a 1:1 M ratio, applied to a nickel single-crystal micro-cantilever substrate. The findings show how the DES coating alone induced compressive surface stress, resulting in a 34 % increase in principal stress. More notably, when the substrate surface was polarized with a +/- 5 V potential, electro-actuation amplified this mechanochemical effect by up to 51 %, confirming a clear dynamic response. Further validation was presented at the macroscale in a polycrystalline material setting, where a similar response was observed. These findings give insight into the possible development of smart surfaces coated with DESs, where a single chemical system can dynamically alter materials' mechanical response through simple electro-actuation, offering versatile applications across micro and macro scales.

Aluminium-based SiC particle-reinforced metal matrix composites are widely used in engineering applications due to their exceptional mechanical properties. However, their machining remains challenging due to the mismatch of material properties between the brittle SiC particles and the ductile aluminium matrix, causing tool wear and surface damage. To mitigate these issues, laser inverse problem scanning is utilised to strategically melt the top layer aluminium matrix, allowing the SiC particles to sink and promoting more ductile Al-matrix. The strategic hybrid laser milling approach optimises particle sinking, reduces tool wear and enhances surface integrity by minimising particle fracture, pullout and refining microstructure. (c) 2025 The Author(s). Published by Elsevier Ltd on behalf of CIRP. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Understanding the role of grain morphology in the chip formation mechanism during orthogonal cutting is crucial to achieve reliable surface integrity. This is especially important when machining additively manufactured alloys due to the inherent presence of crystallographic textures from their unique grain growth patterns. In this work, wrought and laser powder bed fusion (LPBF) fabricated Alloy 718, representing equiaxed and elongated grains respectively, were employed as the case studies to investigate the influence of grain morphology on the chip formation. A new customised Quick-Stop module was designed for a pendulum-based cutting device to retain the chip on the workpiece. This approach allows the characterisation of oriented grain deformation and quantification of texture evolutions from the workpiece to the chip. By retaining deformed grains near the primary shear zone, it was found that the elongated grains in LPBF-fabricated Alloy 718 significantly influenced the material pile-up behaviour along the shear direction when compared with the equiaxed grains in the wrought sample, leading to an increase in shear angle and decrease in chip ratio. This is because the transition of shear deformation from the cutting edge to free surface is hindered by long grain boundaries that are perpendicular to the cutting direction. Since the shear bands are hard to cross long grain boundaries, the deformation is governed by grain boundary bending in the primary shear zone. In addition, it is revealed that the original textures in the wrought and LPBF-fabricated Alloy 718 tend to evolve into A-type and C-type shear textures respectively during chip formation. This indicates that the slip system family {111}< 112 > is easier to be activated in wrought 718 but < 110 > -directional slip is dominated in LPBF-fabricated 718.

In-plane thin dielectric elastomer actuators (DEAs) represent a promising solution for miniaturised soft robots capable of navigating confined spaces. However, most existing in-plane DEAs are either fabricated using off-the-shelf materials or rely on membranes attached to rigid frames, which limit their actuation performance and pose challenges for integration into locomotion-based soft robots. This work introduces a novel in-plane DEA-based thin soft-rigid hybrid robot for fast movement. The innovative design features a multi-layer silicone-based elastomer tensioned by an in-plane elastic PETG frame. A detailed spin coating fabrication method is presented for producing multilayer silicone-based in-plane DEAs. The robot demonstrated effective crawling on flat surfaces and resonance-driven high-speed locomotion at 53 Hz, achieving a peak velocity of approximately 12.3 mm s-1 which is 34.2% of its body length per second and 224% of body thickness per second. This study highlights the potential of DEAs for advancing miniaturised soft robotics, especially in applications that demand lightweight, flexible, and thin profile actuators.

In manufacturing, cutting tools and component integrity are subjected to high-performance thresholds. The role of cutting fluids is pivotal in mitigating heat generation and friction at the tool-workpiece interface. This study explores the application of specifically designed, unconventional, and eco-friendly media, Deep-Eutectic Fluids (DEFs), which provide optimized fluid delivery to the cutting zone, regulating lubrication and cooling, while maintaining the surface integrity of the machined parts. To benchmark DEFs against traditional material removal methods, including dry, and wet (emulsion-based, Hocut 3380) processes, grinding was selected due to its thermal and lubrication demands. The results indicate that DEFs reduce the formation of severely deformed layers by 47% in comparison to conventional water-based coolants exhibiting superior lubricity, yielding more consistent deformation profiles and lower surface roughness. The generated residual stresses are closely comparable to those achieved using water-based metalworking fluids. This was substantiated by micromechanical testing, revealing a coherent failure mechanism at the machined edges for both DEF and wet-cutting media, significantly mitigating the adverse effects of dry machining. These findings highlight DEFs’ potential for industrial-scale adoption as a sustainable alternative in material removal processes, underscoring their capability to enhance process efficiency and environmental sustainability, or as an in-field portable cutting fluid.

High-strain-rate shear deformation of advanced alloys is the bases of a wide range of processing methods (e.g. cutting, forming, shot peening) for highly engineered components used in a wide range of industries (e.g. aerospace, nuclear, automotive). When such shear deformations occur, layers of very fine equiaxed grains have been widely reported which are commonly explained via a continuous dynamic recrystallization (CDRX) mechanism. However, employing a cutting operation to induce shear deformations at high strain rates (104-105 s-1) in a Ni-based superalloy we found features that cannot be explained by this classical approach. Here we quickly stopped the shear deformation process so that the phenomena leading to grain refinement can be inferred by examining the deformation zones in a time successive manner. Our analysis using Transmission Kikuchi Diffraction (TKD) and Transmission Electron Microscopy (TEM), we prove that the grain refinement is much more complex than previously reported as this is the result of a bi-modal mechanism where Geometric Dynamic Recrystallization (GDRX) combines with CDRX leading to unique microstructural features. We further supported the proposed bi-modal grain refinement mechanism by showing differences in mechanical properties by performing micro-pillar compression tests within targeted deformation zones (i.e. dominated by CDRX and GDRX+CDRX). These findings highlight new mechanisms of dynamic recrystallization caused by high-strain-rate shear deformations which have pivotal importance on how to conduct key manufacturing processes so that the properties of resultant recrystallized layers can be controlled.

Continuum robots, with their slender configuration and high redundancy, gain increasing interest in industrial applications such as intervention within confined spaces. However, when the robot end effector is required to travel a long distance, the existing products need a large actuation pack and complicated control strategy for a decent accuracy. This paper presents a continuum robot with a novel stiffness-adjustable mechanism designed to address conditions requiring high tip accuracy in long-reach confined spaces. Key innovations include (1) a section capable of inflating its diameter tenfold for the support of a 6-DoF continuum section, (2) a predictive model for a hybrid stiffening arm, and (3) a manual insertion approach that reduces actuation complexity. The proposed design was validated through a prototype that performed repairs on a thermal barrier coating within an aeroengine. During trials, a 12.6 mm diameter arm was inserted through an access port with a diameter of < 15 mm, inflated to 120 mm to securely lock in place, and enabled precise six degrees of freedom (6-DoF) control. The predictive model achieved a Root Mean Square Error below 1.14 mm under payload, demonstrating enhanced positional accuracy compared to traditional continuum robots. These results mark a significant advancement towards robust, precise operations in restricted industrial environments.