Constructing an effective and scalable anti-counterfeiting system against cyber-physical attacks has become an increasingly crucial problem. While mass production and digitalization across many domains profoundly transformed our society with an abundance of goods and hyper-connectivity, they have also made it harder to track products and associated information, thereby raising authentication risks. In response to such risks, leveraging random physical traces that are generated as byproducts in manufacturing processes has gained great attention as a robust and efficient replacement for existing approaches. Yet, as current methods of generating such inherently unique patterns rely on complex process setups and specific materials, they face significant limitations to be applied and scaled up as a product authentication system in various manufacturing industries. To address this issue, we propose a Physically Unclonable Identifier (PUID) that exploits the inherent randomness and process characteristics of Pulsed Cold Spray (PCS) to fabricate a unique physical identifier. In addition, we develop a complementary framework that leverages the resulting spectral features for reliable product identification. Specifically, our framework utilizes Implicit Neural Representations (INRs) and Fast Fourier Transform (FFT)-based cross-correlation as key strategies to create, register, manage, and authenticate PUIDs. Through experiments, we validate the robustness, applicability, and scalability of our approach by rapidly generating 54 PUIDs on an arbitrary substrate and successfully authenticating each one. These results highlight the significant potential of PUIDs for deployment across various industries.

Safety is a crucial requirement for robotic systems operating in manufacturing environments, where perception and motion errors lead to unsafe physical interactions and process failures. While recent advancements in embodied AI and learning-based robotic systems have significantly improved adaptability, their black-box nature and sensitivity to uncertainties make safety assurance challenging. In response to such challenges, this article provides a comprehensive review of safety-oriented robotic perception and motion planning, along with complementary safety layers such as Digital Twins (DTs) that support the validation and monitoring of AI-driven robotic systems in manufacturing. In particular, our review first establishes optimization- and feasibility-based problem formulations to analyze and categorize this broad topic from a unified perspective. Then, we discuss mathematical principles and methodologies for improving safety in robotic perception and motion planning, including their learning-based counterparts, as well as their synergies with existing safety layers in smart manufacturing. Finally, we identify key challenges and future research directions toward safer and more trustworthy AI-driven robotic manufacturing.

This research introduces an innovative method for real-time monitoring thin film growth and surface roughness using a single mode optical fiber without any additional treatment. The cleaved end of the fiber was installed within the deposition chamber, allowing the thin film to be deposited directly onto the fiber tip. During the deposition process, a Fabry-Perot interferometer was formed with its cavity length equal to the film thickness. As the thin film grows, it alters the cavity length, resulting in a measurable interferogram. In cases where the film surface is not optically smooth, surface roughness becomes a function of deposition time. By leveraging this phenomenon, this research demonstrates a method for calculating film thickness and surface roughness using Hilbert Transform along with an iterative method. It was found that the measured film thickness fluctuates around the ground truth given by a simulation, with an error on the nanometer scale (<= 4 nm) and the reflectivity error is less than 0.004 (maximum error percentage of 5.86 %). Compared to traditional quartz crystal microbalance counterpart, the proposed method directly measures the film thickness rather than mass of the thin film. Furthermore, the compact probe design allows it to be placed closer to the substrate, enhancing monitoring precision. This method offers a simple, quick, and affordable approach to monitor film thickness and surface roughness, effectively addressing the current challenges in the field.

Pulsed cold spray (PCS) is a type of cold spray metal coating technique that incorporates cyclical compressed gas pulses to control the gas-powder for improved surface deposition. In recent years, PCS has garnered great attention owing to its unique pulsed nature in the domains of dense coatings, metal matrix composite coatings, cellular metallic structures, etc. However, research on the PCS to uncover process-structure-property relationships of this emerging deposition technique is limited. To this end, this study thoroughly investigates PCS to gain a deeper understanding of this coating technique. First, a PCS system incorporating a converging-diverging (CD) nozzle is designed and prototyped. Next, two-phase flow (i.e., gas + powder) within the PCS is modeled using computational fluid dynamics (CFD). The modeling results are then experimentally validated using particle image velocimetry (PIV), followed by a case study on surface deposition. The results show that the optimal powder injection window occurs when the gas inlet pressure is at least 99 % of the set inlet pressure, achieving a steady-state gas flow for 100 ms. CFD modeling showed that Mach diamonds formed at the nozzle exit by 30 ms, though powder velocity does not reach steady-state until 50 ms. Numerical modeling captured the average particle with an error of approximate to 8 % as compared to the PIV measurements. Furthermore, surface deposition experiments showed that the PCS can create dense coatings with remarkably less porosity (i.e., 1.73-fold) as compared to the traditional CS. Overall, this study unravels the intricacies of designing and modeling of a PCS system with a CD nozzle, complemented by surface deposition experiments.

This research introduces how to find the analytic spectrum of a Fabry-P & eacute;rot interferometer (FPI) with arbitrary reflectivities of mirrors by giving the mathematical expression of the spectrum. It does not require the weak reflection assumption of the two mirrors which treats the spectrum as sinusoidal function instead of an Airy function. In this research, the relationships between the instantaneous frequency, the instantaneous phase, and the cavity length were given. The proposed method also illustrates that the conclusions given by the two-beam interference approximation are also applicable for high reflection conditions if the spectrum covers multiple complete free spectrum ranges. Compared to other methods of tracking interference maxima and minima, this method provides absolute cavity length measurements and does not encounter problems with losing track of the maxima and minima when working with unstable or noisy spectrum. Compared to the Fourier transform method, this approach considers the chirped nature of the spectrum instead of assuming it as a periodic signal. Also, this method has a higher resolution than the Fourier transform method if the spectrum covers a narrow wavelength range. We used experimental spectra from our previous research to illustrate the performance of this method. A precision at nanometer level and an accuracy at sub-micrometer level was achieved. Therefore, this method demonstrates strong potential for real-time and high-precision monitoring processes.

While a Computer Numerical Control (CNC) machine automates most of the machining processes, the pre-and post-processes are still manually and inefficiently done by a human operator. Specifically, failure to eliminate chips from a worktable completely can adversely affect the machining process, leading to incorrect clamping and cutting of a workpiece and internal dimension measurements. An operator blows high-pressure air or coolant at various angles and positions in a random manner to remove the debris on a worktable, however, chips often disperse in unintended directions. Furthermore, blind spots such as corners or areas covered by other parts hinder full inspection and cleaning processes. Thus, optimizing air-blowing direction depending on the feature of chips and devising a vision system inspecting the inside of a CNC machine with diverse angles and locations is essential for an autonomous and robust chip removal algorithm. However, simulating diverse conditions in a physical CNC machine for optimizing air-blowing directions consumes many resources and may cause damage to a machine. To tackle this, this preliminary study developed a DT environment to train an autonomous chip removal deep learning model for a collaborative robot (cobot). In a DT environment including a transplanted virtual CNC machine, a chip cluster localization deep learning model was trained using a data set synthetically generated by scattering chip models in the virtual CNC model. Annotating a data set, Gray Level Co-occurrence Matrix (GLCM) energy, a texture analysis method, was implemented since it presented different values on the region with and without a chip cluster. The YOLOv8 algorithm was used to build a deep learning model of localizing chip clusters. The deep learning model predicted the coordinates of chip clusters in new cases in the DT environment correctly, and the vector from the center of the worktable to the predicted location of a chip cluster was calculated to estimate an air-blowing direction. Next, coordinate conversion from an image to a cyber space was performed for the visualization of an estimated air-blowing direction. Lastly, a virtual reality (VR) interface was utilized to record human skills cleaning chips from a worktable.

This study introduces a non-invasive approach to monitor operation and productivity of a legacy pipe bending machine in real-time based on a lightweight convolutional neural network (CNN) model and internal sound as input data. Various sensors were deployed to determine the optimal sensor type and placement, and labels for training and testing the CNN model were generated through the meticulous collection of sound data in conjunction with webcam videos. The CNN model, which was optimized through hyperparameter tuning via grid search and utilized feature extraction using Log-Mel spectrogram, demonstrated notable prediction accuracies in the test. However, when applied in a real-world manufacturing scenario, the model encountered a significant number of errors in predicting productivity. To navigate through this challenge and enhance the predictive accuracy of the system, a buffer algorithm using the inferences of CNN models was proposed. This algorithm employs a queuing method for continuous sound monitoring securing robust predictions, refines the interpretation of the CNN model inferences, and enhances prediction outcomes in actual implementation where accuracy of monitoring productivity information is crucial. The proposed lightweight CNN model alongside the buffer algorithm was successfully deployed on an edge computer, enabling real-time remote monitoring.

Being a problem that has long plagued the field of electrochemical machining process, real-time and high precision monitoring of the interelectrode gap is a difficult task. In this research, we introduce a method for monitoring the interelectrode gap using a tool electrode equipped with an optical fiber sensor. This method does not require large numbers of parameters such as conductivity of the electrolyte, electric current density distribution, shape of the electrodes, etc. and therefore reduces the complexity of the monitoring system. The optical fiber, moving together with the tool, forms an open Fabry-Perot interferometer consists of a reflection surface of the fiber end and a reflection surface of the workpiece. Additionally, we introduce a spectrum analysis method specifically designed for the Fabry-Perot interferometer spectrum. This method provides the absolute distance between the optical fiber and the target, demonstrating robustness to noise and abrupt spectrum changes. Consequently, it can be utilized in harsh environments for highprecision distance monitoring, a task challenging for traditional methods using optical interferometers. With the proposed method, a difference of +/- 2.5 mu m was achieved compared with demodulating the spectrum manually. The average process time of a spectrum with 16,001 sampling points and a distance resolution of 1 nm is 0.148 s. Real-time monitoring and control of an electrochemical machining process was also implemented. The interelectrode gap was successfully maintained at 200 +/- 5 mu m throughout the machining process and the final machining depth measured by the proposed method and an optical measurement system has a difference of 6 mu m.

Cold spray (CS) particle deposition, also known as cold spray additive manufacturing, has garnered great attention as an advanced additive manufacturing (AM) and surface deposition technology, facilitating rapid and scalable production of functional parts and surfaces in a solid-state manner. In CS, consistent and precise feeding of functional feedstock powders is crucial for achieving effective particle deposition. However, vibratory-based powder feeders often face challenges associated with powder delivery and powder segregation. This underscores the critical need for a precise diagnostic framework to effectively control powder flow during cold spraying. To this end, the present study proposes a powder flow monitoring framework for the CS process using a stethoscope sound-guided interpretable deep learning (IDL) model. Internal sound data from the vibrated powder feeder is collected through a stethoscope sensor to train a two-stage model. In the first stage, a convolutional autoencoder (CAE) is trained to build an unsupervised learning-based anomaly detector, which identifies classification thresholds based on the receiver operation characteristic curve. In the second stage, a convolutional neural network (CNN) model is trained as the powder flow diagnostic tool by considering process anomalies, namely i) no powder flow; ii) feeder clogging; and iii) no gas flow. The results reveal that the stethoscope sound-guided model achieves a classification accuracy of 95% on the test set, significantly outperforming benchmark utilizing typical external-sound recording microphones in diagnosing CS powder flow. Furthermore, the model is visualized and interpreted by employing t-distribution stochastic neighbor embedding and integrated gradients techniques to enhance reliability of CS powder flow diagnosis. This research highlights the effectiveness of the stethoscope sound-guided IDL model for in-situ powder flow monitoring and process diagnosis in the domain of cold spray additive manufacturing, contributing to effective particle deposition. (c) 2024 The Authors. Published by ELSEVIER Ltd.

Triboelectric nanogenerators (TENGs) are cutting-edge energy harvesting devices to convert abundant mechanical energy sources into electrical power. High-throughput manufacturing of triboelectric surfaces with improved performance and reliability is the key for the large-scale deployment of TENG technology. In the present study, the cold spray (CS) particle deposition technique is employed to fabricate functional surface structures to realize TENGs and smart sensors. Both soft (e.g., tin) and hard (e.g., copper) micron-scale particles are deposited on the target substrates to create the functional surfaces. Additionally, CS is coupled with an electroless deposition process to produce electrically conductive micro-porous (R-a= 2.17 mu m) copper surfaces. These surfaces serve as a positive friction layer and a back-electrode for the TENGs. The fabricated surfaces are subsequently utilized to develop innovative TENGs, demonstrating the potential of these surface structures in triboelectric energy harvesting and smart sensing applications. The results reveal that the CS-based functional surfaces lead to significantly improved energy harvesting performance (approximate to 3.5-fold higher performance) compared to friction layer and back-electrodes made of conventional materials, such as copper and aluminum foil.

The ex-situ incorporation of the secondary SiC reinforcement, along with the in-situ incorporation of the tertiary and quaternary Mg3N2 and Si3N4 phases, in the primary matrix of Mg2Si is employed in order to provide ultimate wear resistance based on the laser-irradiation-induced inclusion of N2 gas during laser powder bed fusion. This is substantialized based on both the thermal diffusion- and chemical reaction-based metallurgy of the Mg2Si–SiC/nitride hybrid composite. This study also proposes a functional platform for systematically modulating a functionally graded structure and modeling build-direction-dependent architectonics during additive manufacturing. This strategy enables the development of a compositional gradient from the center to the edge of each melt pool of the Mg2Si–SiC/nitride hybrid composite. Consequently, the coefficient of friction of the hybrid composite exhibits a 309.3% decrease to –1.67 compared to –0.54 for the conventional nonreinforced Mg2Si structure, while the tensile strength exhibits a 171.3% increase to 831.5 MPa compared to 485.3 MPa for the conventional structure. This outstanding mechanical behavior is due to the (1) the complementary and synergistic reinforcement effects of the SiC and nitride compounds, each of which possesses an intrinsically high hardness, and (2) the strong adhesion of these compounds to the Mg2Si matrix despite their small sizes and low concentrations.

The demand for flexible electronic materials used in wearable devices has experienced a significant surge in recent years. Wearable devices typically incorporate an electronic material or system that can be mounted on a human body. It is imperative that these materials are composed of substances compatible with the human body. Consequently, numerous studies have been undertaken to develop flexible electronic devices with various performance capabilities. In this study, nanowire patterns were manufactured on nanofibers and utilized as patches. To create a nanowire pattern, a direct-write spraying process was employed to investigate changes in electrical characteristics using process variables. The process involved depositing silver nanowires on the surface of nanofibers using a pneumatic spray nozzle. Generated patterns were found to be suitable for use as sensors capable of withstanding skin-attached deformation.

Wafer quality control is one of the important processes to improve the yield rate of semiconductor products. Profile quality and defects in the wafer are two key factors that should be taken into consideration. In this research, we introduce a method that measures the profile of the upper surface and the thickness of the wafer at the same time using an optical fiber cascaded Fabry-P & eacute;rot interferometer working at wavelength of 1550 nm. Therefore, the 3D profile of the wafer can be reconstructed directly. Testing results show that both accuracy and precision of the Fabry-P & eacute;rot interferometer are within a nanometer scale. Defects, especially those embedded inside the wafer, will be detected by monitoring the leaky field with treating wafers as slab waveguides. With the leaky field detection, defects on the lower surface of the wafer were successfully detected by monitoring the leaky field above the upper surface of the wafer. Compared with traditional methods such as radiographic testing or computed tomography testing, the proposed methods provide a cost-effective alternative for wafer quality evaluation.

Conductive metallization of polymer surfaces, owing to the integration of unique features of dissimilar materials (i.e., polymer + metal), is becoming the central focus in flexible polymer electronics. However, fabrication of multifunctional surfaces on polymers in a high-throughput and robust manner at ambient conditions remains challenging. In this study, we employ the cold spray (CS) particle deposition technique to produce multifunctional hybrid surfaces on a flexible polymeric substrate (PET) toward flexible electronics. In this regard, soft metal particles (Sn), are deposited on the polymer surface as an “interlayer” followed by the over-coating of hard metal (Cu) film to create hybrid (Sn + Cu) surfaces. Studies on microstructure, adhesion strength, and water contact angle are conducted to characterize the resulting surface structure. By leveraging the optimum CS settings, multifunctional surfaces with promising electrical conductivity (5.96 × 105 S.m−1), flexibility, adhesive strength, and hydrophobicity (contact angle ≈ 122°) were achieved. Moreover, the antibacterial performance of the surface is confirmed by the in vitro antibacterial tests in a manner that > 99

Cardiovascular disease is a significant health concern worldwide, and varied effective treatment and prevention methods have been developed. Among these, tailored biomaterials-based strategies such as stents, scaffolds, patches, and drug delivery systems have emerged as a promising avenue. These devices are designed to match the mechanical and biological mechanisms of the cardiovascular system, ensuring optimal performance and compatibility. By effectively treating or preventing cardiovascular diseases, these devices have the potential to improve patient health outcomes significantly. They can restore blood flow by addressing blocked arteries and regenerate damaged cardiac tissue by delivering bioactive agents or cells directly to the affected area in a targeted, sustained, and controllable manner. Therefore, the objective of this article is to summarize the available evidence on these tailored biomaterial-based tunable cardiovascular devices. This knowledge can help to transform cardiovascular medicine for the treatment or prevention of cardiovascular disease and restore cardiac function to improve patients' quality of life.

Cold Spray (CS) is an emerging metal additive manufacturing and surface coating technology, where metallic microscale powders are accelerated to supersonic velocities followed by impact onto a target substrate. In CS, the design of the cold spray nozzle is the pillar, determining the efficacy and quality of the deposited layers as it both affects the gas dynamics and powder flow. However, comparative studies on the gas-powder flow dynamics within different nozzle profiles are limited. In this paper, two prominent CS nozzle exit profiles - circular and rectangular - are extensively studied using numerical modeling by considering both soft and hard feedstock powders (i.e., tin (Sn) and copper (Cu)). In this regard, computational fluid dynamics (CFD) simulations are performed to investigate the effect of inlet gas pressure and temperature on supersonic jet formation and powder dispersion characteristics. In regard to the gas flow, the rectangular nozzle showed flow separation much earlier in the nozzle compared to the circular nozzle under the same process settings. Notably, this phenomenon was found to be more drastic at lower gas pressures. As for the powder flow, it was observed that the rectangular nozzle is less effective at accelerating powders compared to the circular nozzle but allows for powders to reach higher temperatures. Moreover, particle deposition experiments on a composite substrate (GFRP) revealed that while the rectangular nozzle produced more uniform particle deposition (Ra = 8.12), it led to a thinner metal coating (approximate to 53 %) with significantly higher electrical resistance (143-fold) compared to the circular nozzle. The results provide valuable insights into understanding the advantages and limitations of both the circular and rectangular nozzle profiles in CS, contributing to more efficient and high-quality particle deposition.

An instantaneous and precise coating inspection method is imperative to mitigate the risk of flaws, defects, and discrepancies on coated surfaces. While many studies have demonstrated the effectiveness of automated visual inspection (AVI) approaches enhanced by computer vision and deep learning, critical challenges exist for practical applications in the manufacturing domain. Computer vision has proven to be inflexible, demanding sophisticated algorithms for diverse feature extraction. In deep learning, supervised approaches are constrained by the need for annotated datasets, whereas unsupervised methods often result in lower performance. Addressing these challenges, this paper proposes a novel deep learning-based automated visual inspection (AVI) framework designed to minimize the necessity for extensive feature engineering, programming, and manual data annotation in classifying fuel injection nozzles and discerning their coating interfaces from scratch. This proposed framework comprises six integral components: It begins by distinguishing between coated and uncoated nozzles through gray level co-occurrence matrix (GLCM)-based texture analysis and autoencoder (AE)-based classification. This is followed by cropping surface images from uncoated nozzles, and then building an AE model to estimate the coating interface locations on coated nozzles. The next step involves generating autonomously annotated datasets derived from these estimated coating interface locations. Subsequently, a convolutional neural network (CNN)-based detection model is trained to accurately localize the coating interface locations. The final component focuses on enhancing model performance and trustworthiness. This framework demonstrated over 95

Triboelectric nanogenerators (TENGs) have gained remarkable attention in energy harvesting and smart sensing, allowing for converting mechanical energy into electrical energy. Despite great potential and progress made in this field, there remains a high demand for high-performance electrodes that are produced with sustainable, lowcost, lightweight, and durable materials such as polymers. Here, by combining the material extrusion 3D printing and the cold spray particle deposition methods, we employ a complete additive manufacturing (AM) approach to fabricate functionalized electrodes on 3D-printed parts for TENG technology. First, polylactic acid (PLA) parts were produced by material extrusion printing. Next, the cold spray process (CS) was utilized as just a one-step fabrication method of the conductive electrodes on the printed parts, eliminating the need for surface activation, over-plating, curing, and/or post-processing. Additionally, the process-structure-property relationships of the CS process were uncovered to fabricate high-performance electrodes for TENGs. The resulting electrodes demonstrate promising electrical conductivity (9.8 x 10(4) S.m(-1)), adhesive strength, stability, and microroughness (R-a = 6.32 mu m). The TENG with the fabricated electrode generates an open-circuit voltage of 174 V, which is nearly 1.85-2.9-fold higher than that of the control TENGs. It achieves the short-circuit density of approximate to 55 mA/m(2), and the power density of 1676 mW/m(2). Besides, to address the low-spatial resolution of the cold spray metallization, a manufacturing pathway is proposed, aiming to achieve higher line resolution (1 mm linewidth) electrodes for polymer electronics. This work provides a manufacturing strategy that can advance the field of TENG and polymer electronics by addressing the limitations of conventional electrode manufacturing techniques.

A hatching-distance-controlled lattice of 65.1Co28.2Cr5.3Mo is additively manufactured via laser powder bed fusion with a couple of periodic and aperiodic arrangements of nodes and struts. Thus, the proposed lattice has an amorphous-inspired structure in the short- and long-range orders. From the structural perspective, an artificial intelligence algorithm is used to effectively align lattices with various hatching distances. Then, the metastable lattice combination exhibits an unexpectedly high specific compression strength that is only slightly below that of a solid structure. From the microstructural perspective, the nodes in the newly designed lattice, where the thermal energy from laser irradiation is mainly concentrated, exhibit an equiaxial microstructure. By contrast, the struts exhibit a columnar microstructure, thereby allowing the thermal energy to pass through the narrow ligaments. The heterogeneous phase differences between the nodal and strut areas explain the strength-deteriorating mechanism, owing to the undesirable multi-phase development in the as-built state. However, solid-solution heat treatment to form a homogeneous phase bestows even higher specific compression strength. Furthermore, electrochemical leaching leads to the formation of nanovesicles on the surface of the microporous lattice system, thereby leading to a large surface area. A more advanced valve cage for use in a power plant is designed by using artificial intelligence both to (i) effectively preserve its mechanical stiffness and (ii) actively dissipate the generated stress through the large surface area provided by the nanovesicles.