Soft sensors are data-driven devices that allow for estimates of quantities that are either impossible to measure or prohibitively expensive to do so. DL (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. One of the most important aspects of building accurate soft sensors is feature representation. This research proposed novel technique in automation of manufacturing industry where dynamic soft sensors are used in feature representation and classification of the data. Here the input will be data collected from virtual sensors and their automation-based historical data. This data has been pre-processed to recognize the missing value and usual problems like hardware failures, communication errors, incorrect readings, and process working conditions. After this process, feature representation has been done using fuzzy logic-based stacked data-driven auto-encoder (FL_SDDAE). Using the fuzzy rules, the features of input data have been identified with general automation problems. Then, for this represented features, classification process has been carried out using least square error backpropagation neural network (LSEBPNN) in which the mean square error while classification will be minimized with loss function of the data. The experimental results have been carried out for various datasets in automation of manufacturing industry in terms of computational time of 34%, QoS of 64%, RMSE of 41%, MAE of 35%, prediction performance of 94%, and measurement accuracy of 85% by proposed technique.

In mixed reality (MR) remote collaborative assembly, remote experts can guide local users to complete the assembly of physical tasks by sharing user cues (eye gazes, gestures, etc.) and spatial visual cues (such as AR annotations, virtual replicas). At present, remote experts need to carry out complex operations to transfer information to local users, but the fusion of virtual and real information makes the display of information in the MR collaborative interaction interface appear messy and redundant, and local users sometimes find it difficult to pay attention to the focus of information transferred by experts. Our research aims to simplify the operation of remote experts in MR remote collaborative assembly and to enhance the expression of visual cues that reflect experts' attention, so as to promote the expression and communication of collaborative intention that user has and improve assembly efficiency. We developed a system (EaVAS) through a method that is based on the assembly semantic association model and the expert operation visual enhancement mechanism that integrates gesture, eye gaze, and spatial visual cues. EaVAS can give experts great freedom of operation in MR remote collaborative assembly, so that experts can strengthen the visual expression of the information they want to convey to local users. EaVAS was tested for the first time in an engine physical assembly task. The experimental results show that the EaVAS has better time performance, cognitive performance, and user experience than that of the traditional MR remote collaborative assembly method (3DGAM). Our research results have certain guiding significance for the research of user cognition in MR remote collaborative assembly, which expands the application of MR technology in collaborative assembly tasks.

In parallel with the fast growth of the second-hand aviation market, the importance of promoting remanufacturing analytics has increased. However, end-of-life (EoL) aircraft parts remanufacturing operations are still underdeveloped. Disassembly, the most challenging and central activity in remanufacturing, directly affects the EoL product recovery's profitability and sustainability. Disassembly sequence planning (DSP) devises ordered and purposeful parting for all potentially recoverable components before physical separations. However, the complexities and uncertainties of the EoL conditions engender unpredictable DSP decision inputs. The EoL DSP needs emergent evidence of cost-effective solutions in view of Industry 4.0 (I4.0) implications and stakeholders' benefits. Among the I4.0 technologies, X-reality (XR) particularly hits the mainstream as a cognitive and visual tool consisting of virtual reality, augmented reality, and mixed reality. Recently, with the advance of I4.0 phenomenon, lean management has been theorized and tested through complementary collaboration. Since the research of integrating lean and XR into the EoL DSP is underexplored in literature, XR and lean are investigated as assistive enablers in the DSP. This study has a two-fold purpose: (1) identifying the key concepts of DSP, I4.0, XR, and lean, and extending the literature by reviewing the previous efforts of EoL aircraft remanufacturing, XR-assisted DSP, and XR-lean applications; (2) proposing "Smart Disassembly Sequence Planning (SDSP)" as a new EoL decision-support agenda after analyzing relational advantages and evolving adaptability. The barriers and limitations are highlighted from the recent associated topics, concrete academic information for developing digitalized disassembly analytics is provided, and new trends are added for future disassembly research.

Precise tooling is vital for defect-free production of micro injection moulded (μ-IM) or hot-embossed products. The demoulding stage of such moulding and forming processes poses a serious challenge to the integrity of thin miniature features because of friction, adhesion, and thermal stresses. Typically, micro moulds involve geometrically textured patterns or features such as linear ridges, pillars, channels, and holes, the characteristic dimensions of which range from 10 to 300 μm. Realistically complex mould designs, containing precision micro features (enhanced fillet radius and positive draft angle) and high surface quality, are presented in this work. Electropolishing based on forward pulse currents (PC) has been used to shape and polish Ni micro moulds that contain sets of micron-scaled linear ridges and star patterns in order to ease the separation of moulded polymeric parts from the metallic mould during ejection and demoulding. The use of forward pulsed currents improved the mould design by increasing the fillet radii and draft angle while keeping the surface roughness low and maintaining a good surface shine. An optimization study of forward PC using a green solution of nickel sulfamate varied EP times (0-70 min) and duty cycles (40, 50, 60, and 70%) at a process conditions of 2.8 V, 50 °C, and 250 rpm. The best topographical and morphological changes were observed for a typical microfluidic channel ( × , 100 × 110 μm) with an EP time of 70 min and 50% duty cycle: fillet radius increased by 3.8 μm, draft angle by 3.3°, and the channel width reduced by 11.4% while surface roughness changed by 8.6% and surface shine improved by 48.9%. Experimental validation was performed using hot embossing wherein the electropolished Ni mould replicated the micro channels and star patterns in PMMA chips with notably fewer burrs, material pile up, and no feature distortion. Moreover, there was a reduction in the side wall roughness of micro channels in PDMS casting with electropolished Ni mould by 16%. Hence, this work presents a significant scientific contribution to improving the efficiency of micro mould tools and reduces the defects caused by friction and adhesion in replicated polymeric parts.

A new variant of the inherent strain (IS) method is proposed to predict component distortion in powder bed fusion additive manufacturing (AM) that addresses some of the shortcomings of the previous work by accounting for both the compressive plastic strain formed adjacent to the melt pool and the thermal strain associated with the changing macroscale thermal field in the component during fabrication. A 3D thermomechanical finite element (FE) model using the new approach is presented and applied to predict the distortion of a component fabricated in an electron beam powder bed fusion (EB-PBF) machine. To improve computational efficiency, each computational layer is comprised of six powder layers. A time-averaged volumetric heat input based on beam voltage and current data obtained from the EB-PBF system was calculated and applied to each computational layer, consistent with the process timing. The inherent strains were applied per computational layer as an initial anisotropic contribution to the thermal strain at the time of activation of each computational layer, resulting in the sequential establishment of static equilibrium during component fabrication, which accounts for the variation in the local macroscale thermal field. The thermal field and distortion predicted by the thermomechanical model were verified using experimentally derived data. The model predicts in-plane compressive strains in the order of 10. Differences in the inherent strain were found at different locations in the component, consistent with differences in the macroscale thermal field. The proposed method is general and may also be applied to the laser powder bed fusion (L-PBF) process.

One of the challenges in the transfer of heat during the mechanical machining process is the coolant substance used in the internal cooling method which is generally liquid water or a water-based coolant. This limits the heat transfer capacity insofar as the thermal conductivity of liquid water is concerned. The other difficulty is the requirement for an external mechanical system to pump the coolant around the internal channel, providing efficient transfer of the accumulated thermal energy. This study proposes a novel method to address this issue by using liquid gallium which provides the means to transfer the excess heat generated during the cutting process by integrating the design into an aluminium oxide insert. Combining this with a magnetohydrodynamic drive, the coolant system operates without the need for mechanical input. Liquid gallium is nontoxic and has a much higher thermal conductivity over liquid water. Investigations of the novel cooling system is performance compared against liquid water through numerical modelling, followed by an experimental machining test to ascertain the difference in heat transfer effectiveness, tool wear rates and workpiece surface finish when compared to dry machining and external cooling conditions on stainless steel 316L. Without cooling, experimental machining tests employing a cutting speed of V = 250 m min resulted in a corner wear VB rate of 75 μm, and with the magnetohydrodynamic-based coolant on, produced a VB rate of 48 μm, indicating a difference of 36% in relative tool wear under the same cutting conditions. Increasing the cutting speed V to 900 m min, produced a corner wear VB rate of 357 μm without the active coolant and a VB rate of 246 μm with the magnetohydrodynamic-based coolant on, representing a decrease of 31% in relative tool wear. Further tests comparing external liquid water cooling against the liquid gallium coolant showed at V = 250 m min, a difference of 29% in relative tool wear rate reduction was obtained with the internal liquid gallium coolant. Increasing the cutting speed to V = 900 m min, the data indicated a difference of 16% relative tool wear reduction with the internal liquid gallium. The results support the feasibility of using liquid gallium as an internal coolant in cutting inserts to effectively remove thermal energy.

The numerical simulation of metal cutting processes requires material data for constitutive equations, which cannot be obtained with standard material testing procedures. Instead, inverse identifications of material parameters within numerical simulation models of the cutting experiment itself are necessary. This report presents the findings from a large-scale study of dry orthogonal cutting experiments on Ti6Al4V (Grade 5) and Ck45 (AISI 1045). It includes material characterization through microstructural analysis and tensile tests. The study details the measurement of cutting insert geometries and cutting edge radii, evaluates process forces, deduces friction coefficients and coefficients for Kienzle's force model, and analyzes chip forms and thicknesses as well as built-up edge formation depending on the process parameters. The collected data, stored in pCloud, can support other researchers in the field, e.g. for recomputations within numerical models or inverse parameter identifications. The dataset includes force measurements, cutting edge scans, and chip images including longitudinal cross-sections of chips.

Electric-field assisted (EA) manufacturing is a promising hybrid manufacturing technique, offering significant advantages over conventional manufacturing methods. Extensive experimental and numerical studies have demonstrated that the application of electric current reduces flow stress in metals and alloys, thereby improving their manufacturability. This enhancement is attributed to the synergistic effects of electroplasticity and Joule heating, both induced by the applied current during processing. Several key manufacturing processes have garnered substantial interest from the research community for their potential enhancement through electric fields. Here, we present a comprehensive review of recent developments in EA manufacturing over the last decade. The findings of various researchers investigating different EA manufacturing processes are discussed, accompanied by detailed tables summarizing the materials and electric current parameters employed in each process.

Wire arc additive manufacturing (WAAM) is suitable for building large-scale engineering structures with high deposition rates and relatively low costs. However, in a typical plasma transferred arc (PTA)-based WAAM process using an inclined wire and vertical torch, keyhole defects can occur due to the high arc pressure, and the process is sensitive to the wire-feeding position with respect to the workpiece. Therefore, in this study, a PTA-based WAAM process with a new configuration employing a vertical wire and an inclined plasma torch was investigated for the potential of mitigation of keyhole formation and improvement of process tolerance. In particular, detailed investigations were carried out on the metal transfer mechanisms and bead formation characteristics under various processing conditions. The results show that the new configuration significantly reduces the likelihood of keyhole formation compared with the conventional approach due to the changes in arc pressure and heat distribution. Systematic analysis reveals that process parameters, including wire feed speed, arc current, and plasma gas flow rate, strongly influence droplet transfer stability, melt pool dynamics, and final bead morphology, which offer guidance for future process optimisation.

The deep drawing process in the automotive industry generates up to 45% material waste. To address this issue, the folding-shearing process was developed as a drop-in solution, enabling the formation of parts in pure shear with minimal thickness variation. This process involves folding a blank while collecting the excess material in a region called the 'beak', which is subsequently sheared in-plane using a single set of tools moving in one forming direction. This paper investigates the extent to which the curvature of the geometry of the beak influences the resulting thickness distribution. A combination of physical and numerical trials demonstrates that a beak design with a negative Gaussian curvature reduces the maximum thickening by 65%. This reduction in thickening helps minimise the forming loads and tool wear, thereby improving the overall robustness of the process. An analytical model is proposed to predict the resulting thickness distribution and demonstrates accuracy within a 12.5% deviation from experimental results. Finally, a design map is proposed to instantly identify the optimal beak design parameters without the need for extensive numerical or physical validations while ensuring a minimal thickness change.

Accurate tracking of modal characteristics is a valuable diagnostic tool for condition monitoring of machine tool spindle units. While experimental modal analysis (EMA) is the conventional method used for machine tool modal identification, it is often impractical to implement in production settings due to the invasive and manual nature of the impact hammer test. In this study, a new technique for operational modal analysis (OMA) based on output-only vibration measurements obtained during a milling operation with variable spindle speed is proposed. Modal identification is performed using two OMA standard methods, namely stochastic subspace identification (SSI) and frequency domain decomposition (FDD). The modal characteristics are compared to values obtained from conventional EMA from impulse hammer testing on the static spindle, and from the operational spindle during cutting using force measurements collected by a table dynamometer. The percentage difference between the natural frequencies identified by the proposed OMA method and frequencies identified by conventional impulse hammer testing was less than 10%, and for the operational spindle during cutting tests, the difference was less than 3%. These results demonstrate the validity of a new modal identification method that can be practically implemented in production.

Coating sprays play a crucial role in extending the capabilities of optical measuring systems, especially when dealing with reflective surfaces, where excessive reflections, caused by incident light hitting the object surface, lead to increased noise and missing data points in the measurement results. This work focuses on metal additively manufactured parts, and explores how the application of a sublimating matting spray on the measured surfaces can improve measurement performance. The use of sublimating matting sprays is a recent development for achieving temporary coatings that are useful for measurement, but then disappear in the final product. A series of experiments was performed involving measurement by fringe projection on a selected test part pre- and post-application of a sublimating coating layer. A comparison of measurement performance across the experiments was run by computing a selected set of custom-developed point cloud quality indicators: rate of surface coverage, level of sampling density, local point dispersion, variation of selected linear dimensions computed from the point clouds. In addition, measurements were performed using an optical profilometer on the coated and uncoated surfaces to determine both thickness of the coating layer and changes of surface texture (matte effect) due to the presence of the coating layer.

The UK is the twelfth-largest manufacturing nation globally, yet its adoption of digital manufacturing technologies (DMTs) lags behind other European countries. In an era where industrial automation and digital transformation are essential for maintaining competitiveness, understanding the human factors influencing the acceptance and implementation of these technologies is critical. This study examines the perceptions of 313 UK manufacturing employees regarding the usefulness, ease of use, and workplace impact of DMTs. Findings indicate that while employees recognise the potential benefits of DMTs such as increased productivity, improved product quality, and enhanced competitiveness, concerns remain regarding ease of use, workforce upskilling, and physical interaction with new technologies. Notably, employees with lower educational qualifications expressed greater scepticism about the applicability of DMTs. Furthermore, those working in companies that had already implemented digital technologies reported more positive perceptions compared to non-users, emphasising the role of experience in shaping attitudes. The study highlights the need for targeted training and change management strategies to facilitate smoother workforce adaptation to digital advancements. These findings provide insights for policymakers, industry leaders, and system designers aiming to integrate human-centric approaches in the transition to Industry 4.0 and beyond.

The growing complexity of manufacturing processes and the increasing diversity of decision-making tools present challenges in selecting effective approaches for process optimisation. Many existing tools are either too narrowly focused or inconsistently applied across sectors, limiting their broader impact. Additionally, the lack of clear integration strategies often hinders their full implementation in industrial settings. This systematic review examines decision-making tools that enable comparative assessments applied at the unit process level in manufacturing, covering both the selection between competing manufacturing routes and the optimisation of specific processes. A total of 37 journal articles were selected through a structured database search and evaluation process. The review analyses commonly used tools such as Multi-Criteria Decision Analysis (MCDA), Life Cycle Assessment (LCA), and Direct Comparison, highlighting their applications, benefits and limitations. Findings show that MCDA offers robust, multi-dimensional evaluations but is often constrained by complexity and data demands. In contrast, simpler methods like Direct Comparison provide more accessible insights but with a limited scope. Advanced tools such as Deep Learning and Computational Simulations hold promise but face challenges in scaling beyond the process level. Notably, there is limited integration of sustainability metrics within process-level decision-making. To address this, the study proposes a structured framework to guide future research and implementation, focusing on data management, AI integration and tool scalability. The results highlight the need for hybrid approaches that combine different tools to balance trade-offs and support long-term sustainability and operational efficiency in manufacturing systems.

Injection moulding (IM) tools with embedded sensors can significantly improve the process efficiency and quality of the fabricated parts through real-time monitoring and control of key process parameters such as temperature, pressure and injection speed. However, traditional mould tool fabrication technologies do not enable the fabrication of complex internal geometries. Complex internal geometries are necessary for technical applications such as sensor embedding and conformal cooling which yield benefits for process control and improved cycle times. With traditional fabrication techniques, only simple bore-based sensor embedding or external sensor attachment is possible. Externally attached sensors may compromise the functionality of the injection mould tool, with limitations such as the acquired data not reflecting the processes inside the part. The design freedom of additive manufacturing (AM) enables the fabrication of complex internal geometries, making it an excellent candidate for fabricating injection mould tools with such internal geometries. Therefore, embedding sensors in a desired location for targeted monitoring of critical mould tool regions is easier to achieve with AM. This research paper focuses on embedding a wireless surface acoustic wave (SAW) temperature sensor into an injection mould tool that was additively manufactured from stainless steel 316L. The laser powder bed fusion (L-PBF) "stop-and-go" approach was applied to embed the wireless SAW sensor. After embedding, the sensor demonstrated full functionality by recording real-time temperature data, which can further enhance process control. In addition, the concept of novel print-in-place venting design, applying the same L-PBF stop-and-go approach, for vent embedding was successfully implemented, enabling the IM of defectless parts at faster injection rates, whereas cavities designed and tested without venting resulted in parts with burn marks.

Hydrophobicity plays a pivotal role in mitigating surface fouling, corrosion, and icing in critical marine and aerospace environments. By employing ultrafast laser texturing, the characteristic properties of a material's surface can be modified. This work investigates the potential of an advanced ultrafast laser texturing manufacturing process to enhance the hydrophobicity of aluminium alloy 7075. The surface properties were characterized using goniometry, 3D profilometry, SEM, and XPS analysis. The findings from this study show that the laser process parameters play a crucial role in the manufacturing of the required surface structures. Numerical optimization with response surface optimization was conducted to maximize the contact angle on these surfaces. The maximum water contact angle achieved was 142º, with an average height roughness (Sa) of 0.87 ± 0.075 µm, maximum height roughness (Sz) of 19.4 ± 2.12 µm, and texture aspect ratio of 0.042. This sample was manufactured with the process parameters of 3W laser power, 0.08 mm hatch distance, and a 3 mm/s scan speed. This study highlights the importance of laser process parameters in the manufacturing of the required surface structures and presents a parametric modeling approach that can be used to optimize the laser process parameters to obtain a specific surface morphology and hydrophobicity.

Subtractive manufacturing is undergoing a transformative shift towards sustainability and zero-defect manufacturing. This shift is driving the need for more efficient machining strategies such as dynamic milling. The real-time implementation of dynamic milling toolpaths, composed of circular and cycloidal curve patterns, is challenging due to the kinematic constraints in computer numerically controlled machine tools. Resulting from a rigorous analytical analysis of kinematics, the limitations of current approaches to finite impulse response (FIR) interpolation of circular arc (G02/G03) motion are addressed. A novel hybrid FIR interpolation method is presented which modifies the interpolation style depending on the fundamental geometry of commanded circular motion. The method globally satisfies kinematic constraints and tool centre point position tolerances during circular motion and allows consideration of machine dynamics (i.e., resonant frequencies) within the interpolation strategy. The proposed method outperformed current state-of-the-art methods during benchmarking tests which included a high-performance machine tool and two commercial controllers. Reductions of up to 38% in manufacturing cycle times were demonstrated when interpolating high-speed trochoidal toolpaths with the proposed method.

In heavy industries like oil and gas, and shipbuilding, maintaining process quality is challenging. These sectors face inconsistent manual procedures and a shortage of skilled operators regarding thermal cutting and bevelling for welding preparation tasks. Manual fitting and repetitive quality control modifications, especially during thermal cutting, significantly increase time consumption and hinder productivity. Traditional thermal cutting methods are prone to human error, resulting in inconsistent cut quality, and demand high expertise leading to variability in cut precision, increased rework, and material wastage. The objective of this work is to address these challenges by introducing real-time ultrasonic sensing into a robotic plasma cutting control system to automate the steel plate bevelling process. The ultrasonic sensor enables the system to dynamically adapt to variations in steel plate thickness before cutting, ensuring precise and consistent results. The solution begins by presenting an automated method for measuring thickness and computing bevel distance per sample. Secondly, it proposes adaptive adjustments to cutting parameters per sample, leveraging the ultrasonic sensor data to enhance accuracy and reduce the need for manual intervention. Finally, the approach introduces adaptive robotic path generation for cutting and utilizing real-time ultrasonic sensor data to optimize cutting paths. The outcome of this study is the successful development and validation of an adaptive robotic plasma cutting system for steel plate bevel applications, which leverages real-time ultrasonic sensor data to automate the parameter input process and robotic motion planning, demonstrating improved accuracy and efficiency compared to traditional approaches. The results demonstrate that ultrasonic-driven robotic cutting significantly reduces the average error cut percentage to 4.47% with deviations ranging from 0.13 to 0.23° for the bevel angle and 14.27% with deviations between 0.02 and 0.05 mm for root face deviation, compared to the standard cutting approach which has an average error of 18% with deviations ranging from 0.10 to 0.38 mm and 77.1% with deviation between 0.48 to 0.90°, respectively. This paper highlights the benefits of using advanced sensing technology, particularly ultrasonic sensors, to automate plasma bevel cutting for metal plates in the steel fabrication and welding sectors.

Finishing machining of Ti6Al4V, known for its high strength and heat conduction resistance, demands optimisation to achieve high-quality end products. This study explores modifying the chip contact length on the rake face and altering the flank face with a cavity to minimise process forces and temperatures while maintaining cutting edge integrity. The research validates the manufacturability of ultra-short pulsed laser-ablated tool geometry modifications, indicating potential for industrial scale-up. Extensive experimental evaluations under dry conditions assess the impact of tool modifications at various feed rates for planing and turning. Significant reductions in process forces and temperatures were observed with rake face modifications, particularly at a cavity distance of approximately 34 µm. Ideal performance was noted for feed rates between 0.035 and 0.045 mm for planing and 0.040 to 0.045 mm/rev for turning. Smoothed Particle Hydrodynamics (SPH) simulations employing a Johnson-Cook material model were used to analyse chip formation and to predict the process forces. These simulations revealed a clear change in the chip formation and lower process forces and temperatures. The SPH results closely matched experimental outcomes, with a discrepancy of less than 7 % in cutting forces for both tool types, although feed forces were underestimated by about 50 %. The effect of the tool modification is reflected accurately at the respective feeds.

The aim of this paper is to study the mechanical behavior of corrugated board boxes, focusing attention on the strength that the boxes are able to offer in compression under stacking conditions. A preliminary design of the corrugated cardboard structures starting from the definition of each individual layer, namely the outer liners and the innermost flute, was carried out. For this purpose, three distinct types of corrugated board structures that include flutes with different characteristics, namely the high wave (C), the medium wave (B), and even the micro-wave (E), were comparatively evaluated. More specifically, the comparison is able to show the potential of the micro-wave which would eventually allow a significant saving of cellulose in the fabrication process of the boxes, thus reducing the manufacturing costs and causing a lower environmental footprint. First, experimental tests were carried out to determine the mechanical properties of the different layers of the corrugated board structures. Tensile tests were performed on samples extracted from the paper reels used as base material for the manufacturing of the liners and flutes. Instead, the edge crush test (ECT) and box compression test (BCT) were directly performed on the corrugated cardboard structures. Secondly, a parametric finite element (FE) model to allow, on a comparative basis, the study of the mechanical response of the three different types of corrugated cardboard structures was developed. Lastly, a comparison between the available experimental results and the outputs of the FE model was carried out, with the same model being also adapted to evaluate additional structures where the E micro-wave was usefully combined with the B or C wave in a double-wave configuration.

We present a scalable and environmentally friendly method for fabricating mechanically robust superamphiphobic coatings on aluminum alloy and zinc substrates using a dual-step process combining sandblasting (SB) and steam treatment (ST), followed by surface energy reduction with fluorinated molecules. This approach creates hierarchical micro/nano structures essential for omniphobic performance. On Al-alloy SB + ST surfaces we measured static contact angles of 162.0° (water), 156.1° (ethylene glycol), and 154.4° (peanut oil), while the corresponding Zn surfaces reached 160.1°, 156.0°, and 152.8°, respectively, with sliding angles below 5° across all tested liquids. The coatings retained high repellency after 50 tape-peeling cycles and 100 cm of sandpaper abrasion under a 500 g load (e.g., ethylene glycol > 140° and peanut oil ≈ 120°). They also showed resistance to water jet impact, excellent self-cleaning, and anti-fogging performance. Compared to conventional hot water treatment or chemical etching, this ST-based method enables faster, cleaner fabrication and significantly enhances mechanical durability making it a promising candidate for large-scale applications in anti-fouling, anti-corrosion, and protective surface technologies.