Application Of Machine Research Articles

Multi-damage detection of composite blades via the curvature modal shape approach in combination with surface interpolation and extreme learning machine

In recent years, there has been a rise in renewable energy, which has led to an increase in equipment maintenance. The damage detection technique for wind turbine blades (WTBs) can reduce maintenance costs. The majority are not immediately applicable to structures that are in an operating condition. Motivated by the current constraints, a novel two-stage multi-damage quantitative detection approach in WTBs during operation has been proposed. The multiple damage locations are first identified by calculating the curvature modal shape (CMS) difference for the WTBs after the interpolation of the operating deflection shape (ODS) using surface interpolation (SI). Then the relationship between the natural frequencies and damage severities for the WTBs is determined utilizing the finite element method (FEM). The established correlation provides a natural frequency database that can be utilized for identifying the corresponding damage severities through the application of the Extreme Learning Machine (ELM) method, upon measuring the natural frequencies of the damaged WTB. The efficacy of the suggested damage detection methodology has been confirmed through simulations conducted on WTBs featuring two or three instances of damage. Moreover, the ODS along with natural frequencies of the WTBs exhibiting two or three damages are experimentally assessed using a Scanning Laser Doppler Vibrometer. The simulation and experiment verify that the presented method has satisfactory effectiveness in damage detection.

Effect of Anions on Deformation of Gallium-Based Liquid Metal in Solution.

In this study, deformation behaviors of gallium-based liquid metals in acidified cupric sulfate or cupric chloride solutions with varying concentrations of chloride anion or sulfate anion were investigated to explore their potential applications in soft machines and electronics. Gallium-based liquid metals are known for their unique deformability, making them promising materials for various fields. Previous research has shown that deformation of the liquid metal can be achieved in the presence of acidified cupric or ferric salts. However, the specific influence of different anions on the deformation process remains unclear. Our findings indicate that the deformation rate of the liquid metal increases with higher concentrations of chloride ions and decreases with higher concentrations of sulfate ions in the solution. UV-vis absorbance spectra of the solutions were analyzed to identify the formation of hydrated cupric cations. It was observed that increasing the concentration of Cl- ions promotes the formation of cupric-chloro complexes, thereby reducing the concentration of hydrated cupric ions in the solution. Furthermore, the addition of sulfate ions to the solution enhances the ionic strength of the medium, leading to the dissociation of cupric-chloro complexes. Additionally, sulfate ions can form insoluble layers with gallium ions, which impede the deformation of the liquid metal. The deformation rate of the liquid metal was found to be inversely correlated with the concentration of cupric ions in the solution. These results provide valuable insights into the deformable behavior of gallium-based liquid metals and their potential applications in liquid metal-based soft robots. This study highlights the importance of understanding the role of different anions in the deformation process of liquid metals, shedding light on the design and optimization of soft machines and electronics utilizing these materials.

Extreme learning machine based on BDE feature selection to detect fault scenarios in grid-connected PV systems under MPPT mode

This paper considers real-time data-driven adaptive fault detection (FD) in grid-connected PV (GPV) systems under maximum power point tracking (MPPT) modes during large variations. Faults under MPPT modes remain undetected for longer periods, introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual Micro Phasor Measurement Unit (Micro-PMU) estimations. The high-dimensional and high-frequency multivariate features vary over time, and computational efficiency becomes crucial to realizing online adaptive FD. The goal of this study is to present an artificial intelligence (AI) technique for detecting seven faults: inverter fault, feedback sensor fault, grid anomaly, nonhomogeneous partial shading, open circuit in PV array, MPPT controller fault, and boost converter controller fault. In this work, it was found that the application of Extreme Learning Machine (ELM) plays an important role in fault detection and localization. Nine (9) statistical features and eight (8) wavelet packet parameters are extracted from the data based on multiple default values. These features were used as an input vector to train and test the ELM and determine whether the system is operating under normal conditions or is faulty. The BDE feature selection algorithm is adopted to optimize the seven-fault classification procedure to reduce the number of features. The results showed that the Extreme Learning Machine (ELM), based on statistical parameters followed by BDE, can detect faults with high accuracy (98.3%) compared to a case without optimization.

Experimental study on laser-assisted machining of SiCp/Al composites considering laser preheating history based on in-situ observation

The application of laser-assisted machining (LAM) has been demonstrated to improve the surface integrity of SiCp/Al composites. The dynamic process of laser preheating affects the deformation mechanism of materials. In this paper, the laser preheating history under different parameters is characterized by the changes in peak temperature and instantaneous preheating temperature. SiCp/Al cutting experiments under different laser preheating histories are carried out based on the high-speed imaging system. The influence of laser preheating history on the shear angle and the spatial–temporal distribution of the physical information in the shear zone are analyzed by the digital image correlation (DIC) method. The deformation characteristics during the SiCp/Al composites cutting process under different laser preheating histories are discussed. Additionally, the influence of the laser preheating history on the surface integrity is analyzed according to the changes of the Al matrix and SiC particles under different combinations of peak temperature and instantaneous preheating temperature. It has been demonstrated that the deformation degree in the shear zone during LAM of SiCp/Al composites is depended on the material softening degree related to the peak temperature and the material deformation temperature dominated by the instantaneous preheating temperature. The deformation uniformity is primarily determined by the deformation temperature. An appropriate material softening degree and deformation temperature can improve the surface integrity. The results provide a foundation for further mechanism studies for cutting load and surface integrity in LAM of SiCp/Al composites.

Combined simple adaptive and integral terminal sliding mode control for industrial feed drive systems

In the manufacturing industry, feed drive systems are utilized for various applications in industrial machines. These systems are crucial components for ensuring precision in motion control and are, therefore, expected to offer high tracking performance. This study focuses on application of integral terminal sliding mode control (ITSMC) to a feed drive system because of its robustness against disturbances and convergence of the tracking error in finite-time. However, its design requires plant dynamics model. To alleviate this concern, a solution of modifying the original ITSMC law to adaptive ITSMC law based on simple adaptive control (SAC) technique is used. To implement SAC-like adaptive law to a real plant, a property of “almost strictly positive real (ASPR)” is required to be satisfied. ASPR property is satisfied using velocity-based augmented output signal that ensures the stability. In experimental results, the adaptive controller follows the reference trajectory more precisely than conventional methods.

Analysis of activation energy, chemical reaction, and variable density on magnetically driven heat transportation: Applications in nanofluid lubrication and machining

The importance of this investigation is to examine the heat and mass transportation of magneto nanofluid movement along a heated sheet with exponential temperature-dependent density, entropy optimization, thermal buoyancy, activation energy, and chemical reaction aspects. The influence of these factors in cutting tools by means of machining and nanofluid lubrication is a significant process in cutting zone, chip cleaning, lubricating, and cooling productivity in milling. The corresponding energy activation and chemical process are essential to understand the thermal behavior of nanofluid. The appropriate transformations are used to solve nonlinear partial differential equations within the framework of ordinary differential equations using stream functions and similarity variables. The Keller box method is employed to efficiently solve these equations computationally under the Newton–Raphson approach. Through tables and figures, the fluid velocity, temperature distribution, and concentration consequences are sketched using various controlling parameters. It is seen that the fluid temperature function increases with noticeable amplitude as the Eckert factor, variable density, chemical-reaction, and activation energy increase. It is found that the noticeable enhancement in heat and mass transportation is deduced for maximum Brownian motion and thermophoresis. This work is important in various applications such as cutting fluids, drilling, brake oil, engine oil, minimum quantity lubrication, enhanced oil recovery, and controlled friction between the tool-chip and tool-work during machining operations.

Enhancing Surface Quality and Tool Longevity in EDM of D2 Steel Using Copper Composite Tools

Hard material machining and die manufacture are the main applications for electrical discharge machining (EDM). For EDM, conventional electrode materials include graphite, steel, brass, pure copper, and alloys based on copper. Unfortunately, excessive tool wear is a common problem with these materials, which raises the cost of machining. In this investigation, hardened D2 steel was machined using a composite tool made of 90% copper and 10% silicon carbide. The powder metallurgy method was used to create the composite tool. Surface roughness (SR) and electrode wear rate (EWR) were compared to a number of process variables. For experimentation, a response surface methodology based on CCD design in design of experts software was used. SR and electrode wear rate models were created using the CCD approach. Utilizing analysis of variance, the most important factors and their effects on EWR and SR were determined. The lowest tool wear rate of 0.39 gm/min is achieved with a current of 5 Amps, a pulse duration of 100 µs, and a pulse interval of 10 µs with an R2 of 0.9957, which accounts for 99.57% of the variability, the tool wear rate model fits the data very well and obtained lowest surface roughness of 2.8 µm.

Research Progress on Precision Tool Alignment Technology in Machining

In the field of numerical control machining, tool alignment technology is a key link to ensure machining accuracy and quality. Tool alignment refers to determining the correct position of the tool relative to the workpiece, and its accuracy directly affects the precision of part machining. With the development of precision machining technology, the research and application of cutting technology are increasingly valued. Tool alignment methods are mainly divided into two categories: contact and non-contact. The contact type tool alignment method relies on direct contact between the tool and the workpiece or tool alignment instrument to measure the position. Among them, the trial cutting method is a traditional contact type tool alignment method that determines the tool position through actual cutting, which is intuitive but inefficient. The contact type tool presetter uses specialized equipment to improve the accuracy and efficiency of tool presetting through contact measurement. The non-contact tool alignment method does not rely on physical contact, while the image method uses image recognition technology to determine the tool position, making it suitable for high-precision applications. The laser diffraction method and the laser direct method use laser technology for non-contact measurement. The laser diffraction method determines the position of the tool by analyzing the diffraction mode of the laser beam, while the laser direct method directly measures the distance between the laser and the tool. This article mainly introduces the classification of tool alignment, commonly used knife alignment methods and common tool alignment devices, as well as the development status of international tool alignment instrument products.

Flexible organic crystals with multi-stimuli-responsive CPL for broadband multicolor optical waveguides.

Flexible organic crystals, capable of transmitting light and responding to various external stimuli, are emerging as a new frontier in optoelectronic materials. They hold immense potential for applications in molecular machines, sensors, displays, and intelligent devices. Here, we report on flexible organic crystals based on single-component enantiomeric organic compounds, demonstrating multi-stimuli-responsive circularly polarized light (CPL). These crystals exhibit remarkable elasticity, responsiveness to light and acid vapors, and tunable circularly polarized optical signals. Upon exposure to acid vapors, the fluorescence of the crystals shifts from initial yellow emission to green emission, attributable to the protonation-induced inhibition of excited-state intramolecular proton transfer. Under UV irradiation, the fluorescence emission undergoes a red-shift, resulting from the molecular transformation from an enol configuration to a ketone configuration. Notably, both processes are reversible and can be restored under daylight. The integration of reversible fluorescence changes under light and acid vapors stimuli, CPL signals, and flexible optical waveguides within a single crystal paves the way for the application of organic crystals as all-organic chiral functional materials.

Assessing the impact of sediment characteristics on vegetation recovery in debris flow fans: A case study of the Ohya Region, Japan

What factors influence natural vegetation recovery in debris flow-prone areas, and how do sediment dynamics play a role? This study investigates these questions in the Ohya debris flow fan, Japan, utilizing various analytical techniques. Through the application of Support Vector Machine (SVM) classification, grain size analysis, accuracy assessment, debris flow analysis, and hotspot analysis, this study assess the distribution of sediments, vegetation classes, and the extent of debris flow events. The findings shed light on the dynamics of vegetation recovery and its relationship with sediment dynamic. The SVM classification outcomes reveal distinct trends in sediment and vegetation distribution along the flow path, particularly noting a significant decrease in vegetation cover from upstream to downstream sections. By employing SVM classification, this study successfully identified 3,282,910 sediment particles and determined their average, minimum, maximum, and standard deviation grain sizes as 7.3 cm, 0.27 cm, 415 cm, and 9.18, respectively. Accuracy assessments of image classification and grain size measurements demonstrate high levels of accuracy, with an overall classification accuracy of 98.82 % and a kappa coefficient of 0.977. Validation of grain size measurements reveals a strong correlation (R2 = 0.997 and y = 1.005× + 0.0661) between field-observed sediment sizes and sizes derived from classified images. Debris flow analysis reveals that the total area affected by debris flow in 2022 was 36,232.7 square meters, decreasing to 14,213.9 square meters in 2023. Hotspot analysis identifies regions of both high and low sediment size concentrations, providing valuable insights into sediment distribution patterns. Examining the natural recovery of vegetation, present study identifies vegetation spots across different study sections. Results show that 55 % of naturally recovering vegetation areas are located in regions unaffected by debris flow events between 2022 and 2023. Among the study sections, the area affected by debris flow in 2023 exhibits the lowest density of vegetation spots. Overall, this study highlights the new generation vegetation recovery and its association with sediment dynamic in the Ohya debris flow fan. The findings contribute valuable insights for understanding natural recovery processes in highly dynamic sedimentation areas, informing the development of effective strategies for ecosystem restoration and management in debris flow-prone regions.

Machine Learning, Software, Applications, and Websites For Language Instruction

This study aims to examine the latest developments in the application of machine learning, software, applications, and websites in language teaching. With the advancement of technology, language learning is increasingly facilitated by the use of digital tools that utilize artificial intelligence (AI) and machine learning to improve the learning experience in an adaptive and interactive manner. The study reviewed a variety of platforms that use this technology, including key features that support personalization, automated feedback, and student performance assessments. In addition, this study discusses the effectiveness of the technology in supporting independent and formal learning in the classroom, including implementation challenges in various educational settings. It was found that although this technology provides a great opportunity in accelerating language learning, there are some limitations, such as limited access in regions with low digital infrastructure, as well as lack of integration with relevant cultural and social contexts in language learning. This study concludes that machine learning-based technology in language teaching has great potential, but further research is needed to optimize its application in various educational contexts.

Repetitive Rolling of Triptycene-Based Molecules on Cu Surfaces.

The metal surface-supported rotation of artificial molecular structures is technologically important for developing molecular-level devices. The key factors leading to the practical applications of these molecular machines on metal surfaces are the atomic-scale control of the rotation and the counterbalance of the temperature-driven instability of the molecules. In this work, we present a means by which triptycene-based molecular wheels can roll repetitively on a metal surface. Our results show that regularly stepped surfaces are the perfect candidate not only for stabilizing the molecule on the metal surface but also for providing the pivot points needed for repetitive vertical rotation of the molecule at higher temperatures. In addition to the geometrical compatibility of the substrate and the molecule, intermittent application of the external electric field is needed for rolling the molecule on a metal-stepped surface in a controllable manner.

Advanced fine-tuning procedures to enhance DNN robustness in visual coding for machines

Video Coding for Machines (VCM) is gaining momentum in applications like autonomous driving, industry manufacturing, and surveillance, where the robustness of machine learning algorithms against coding artifacts is one of the key success factors. This work complements the MPEG/JVET standardization efforts in improving the resilience of deep neural network (DNN)-based machine models against such coding artifacts by proposing the following three advanced fine-tuning procedures for their training: (1) the progressive increase of the distortion strength as the training proceeds; (2) the incorporation of a regularization term in the original loss function to minimize the distance between predictions on compressed and original content; and (3) a joint training procedure that combines the proposed two approaches. These proposals were evaluated against a conventional fine-tuning anchor on two different machine tasks and datasets: image classification on ImageNet and semantic segmentation on Cityscapes. Our joint training procedure is shown to reduce the training time in both cases and still obtain a 2.4% coding gain in image classification and 7.4% in semantic segmentation, whereas a slight increase in training time can bring up to 9.4% better coding efficiency for the segmentation. All these coding gains are obtained without any additional inference or encoding time. As these advanced fine-tuning procedures are standard-compliant, they offer the potential to have a significant impact on visual coding for machine applications.

Cell-membrane coated nanoparticles: Role of machine learning and applications in diagnosis and therapy

The advancement of biomedical technologies has introduced a promising approach for targeted drug delivery—utilizing nano and microscale systems. These compounds provide the potential for precise drug delivery to specific tissues, enhancing diagnostic accuracy and therapeutic efficacy, thereby minimizing off-target effects and improving site-specific accumulation. They enable controlled release of therapeutic agents, reducing the systemic toxicity often associated with conventional drugs. This review particularly highlights the critical role of cell membrane-decorated nanocompounds (CDCs), which represent a significant innovation in this field. Artificial Intelligence (AI) plays a pivotal role in enhancing the precision of CDCs, utilizing advanced algorithms to predict optimal delivery routes and release mechanisms. Moreover, AI-driven analytics are instrumental in the real-time monitoring and adjustment of drug release rates, ensuring maximum efficacy and patient safety. This review delves into the applications of CDCs, exploring their characterization, drug delivery potential, and future prospects, with a specific focus on how cell membrane coatings can revolutionize the landscape of drug delivery. Furthermore, the integration of AI in these processes is emphasized, particularly in how it can significantly impact future therapeutic strategies.

3D Modeling Simulation and Innovative Design of a Saw Cutting Mechanism for a Movable Buckling Machine

In this research, a sawing mechanism of a mobile buckling machine was innovatively designed for the forest resources and environmental characteristics of the northeast region of China. The northeast region of China is rich in forest resources, but the climate is cold, which puts high demands on the performance and adaptability of mechanical equipment. In this study, the three-dimensional modeling of the saw-cutting mechanism was completed by Pro/Engineer 5.0 modeling software, and an in-depth dynamics simulation analysis was carried out using dynamics simulation software. The study aims to assess the applicability of this saw-cutting mechanism in the northeast region of China, analyze its design characteristics, and explore possible directions for performance optimization. Through this study, we expect to provide valuable references for future technological innovations, as well as to promote the practical application and development of mobile buckling machines in cold regions. This research not only has regional characteristics, but also reflects the innovative idea of combining mechanical equipment design with environmental adaptability.

Mathematical Model of the Electronic Cam in Terms of Application in a Dosing Machine

The article analyses the use of servomotors in the control systems of industrial equipment, focusing on the alternative offered by position and speed synchronization in relation to classical mechanical mechanisms. A complete methodology is presented to determine the dynamic parameters of the adopted kinematic system using electronic motion profiles. The results obtained constitute a mathematical model of the execution chain and an analysis of the basic quantities for linear motion, supported by actual measurements of the drive parameters. The merit of the article is to show that the servomotors can significantly simplify the design of the device, make it more flexible in adaptation to different assortments, and allow integration with systems predicting the technical condition of the device. The analysis of the results revealed significant differences in the constant rotational speed of the servomotor, which do not align with previous findings. The results suggest that changing the angular working range of the assembly to the range (205°;270°) could significantly affect the generated linear acceleration, reducing the risk of stalling. The calculations and graphs conducted allowed for the accurate representation of the actual mechanical system, considering its dynamic characteristics. The key conclusion is that precise mathematical modelling is essential to ensure the stability and durability of engineering components.

Improvement in mechanical as well as magnetic properties of a (FeCoNi)90Ti10-xAlx complex concentrated alloy series by tuning the chemical order

There is considerable interest in magnetic materials which also possess good mechanical properties. Hence, the effect of Ti/Al ratio on the microstructure, mechanical and magnetic properties of (FeCoNi)90Ti10-xAlx complex concentrated alloys (CCA) was investigated. An increase in the Ti/Al ratio in these CCA enhanced chemical ordering and substantially improved selected mechanical and magnetic properties. As the Ti/Al ratio changed from 10 to 0, the ductility increased from 7.5 to close to 50 %, the saturation magnetization (Ms) increased from 115.2 to 136.7 emu/g, and the coercivity (Hc) decreased from 17.9 to 4.2 Oe. The Fe30Co30Ni30Ti5Al5 alloy exhibit higher UTS×EL value than available soft magnetic materials and has relatively higher Ms and lower Hc compared with other CCA. These results provide a methodology to modulate the chemical order in the Fe-Co-Ni system by Al and Ti additions and synergistically tune the mechanical and magnetic properties for high performance rotating electrical machine applications.

Effect of Deposition Parameters on Micromechanical Properties and Machining Performance of CrN Coating for Wet Finish Turning of Ti6Al4V Alloy.

This study aims to optimize the performance of CrN coatings deposited on WC cutting tools for machining Ti6Al4V alloy, where the formation of built-up edge (BUE) is a prevalent and critical issue. In-house CrN coatings were developed using the PVD (Physical Vapor Deposition) process, with variations in deposition parameters including nitrogen gas pressure, bias voltage, and coating thickness. A comprehensive experimental approach encompassing deposition, characterization, and machining performance evaluation was employed to identify the optimal deposition conditions. The results indicated that CrN coatings deposited at a nitrogen gas pressure of 4 Pa, a bias voltage of -50 V, and a thickness of 1.81 µm exhibited superior performance, significantly reducing BUE formation and tool wear. These optimized coatings demonstrated enhanced properties, such as a higher elastic modulus and a lower coefficient of friction, which contributed to improved tool life and machining performance. Comparative studies with commercial CrN coatings revealed that the in-house developed coatings outperformed the commercial variants by approximately 65% in tool life, owing to their superior mechanical properties and reduced friction. This research highlights the potential of tailored CrN coatings for advanced machining applications and emphasizes the importance of optimizing deposition parameters to achieve high-performance tool coatings.

Research on Predictive Speed Control Scheme for Surface-Mounted Permanent Magnet Servo Systems

In order to improve the dynamic response and disturbance rejection performance of electric machines, a deadbeat predictive speed control (DPSC) scheme for a permanent magnet synchronous motor (PMSM) is proposed. To begin with, a DPSC controller was proposed with the purpose of achieving precise control for the next control cycle, and the control parameters were determined based on the optimal parameter design method. For better application, performance comparisons were made with a conventional PI control, and the mismatch effects of inertia and torque were analyzed. In order to improve the disturbance rejection performance of the system, an extended sliding mode observer (ESMO) was constructed to compensate for disturbances. Experimental verification with a conventional PI control indicates that the proposed DPSC control can reduce the speed response time from 0.675 s to 0.650 s. When the electric machine operates stably and is applied to a torque disturbance of 0.4 Nm, the speed fluctuation and settling time can be reduced from 9 rpm and 1.7 s to 6 rpm and 0.5 s, respectively. This proposed method effectively enhances the speed control performance of PMSM and can be applied to high-performance electric machine applications.

Precisely regulating thermal deformation behavior of wire electrical discharge machining thin-wall fin via pulsed laser-induced shockwave

The thermal deformation in the process of thin-walled metal fin processed by wire electrical discharge machine (WEDM) is almost unavoidable, which restricts the application of wire electrical discharge machining in the precision machining of thin-walled parts. In this study, a new process of thermal bending deformation regulation by laser-induced shockwave of wire electrical discharge machining thin-wall fin was proposed to obtain low/no deformation thin-wall parts. The thermal deformation of thin-wall In718 metal processed with different wire electrical discharge machining parameters was calibrated, and the law between the thermal bending and the process parameters was obtained. The thermal ablation and laser shock thermal deformation regulation model was established to accurately describe the stress distribution and deformation behavior of thin walls. For In718 thin-wall fin with different thicknesses, the thermal bending deformation could be regulated by laser-induced shockwave. After adjustment, the warpage of all thin walls was only −0.174μm at the lowest, and the thermal deformation could be reduced by 98.35 % at the highest. Laser shock improved the surface integrity of thin walls, increased the surface hardness to 433.9HV, increased by 46.84 %; laser shock also reduced the surface roughness of thin walls to a maximum of 2.67μm, a decrease of 33.58 %. In addition, laser shock could reduce the number of large-size grains, increase the number of twins, refine the impact surface grains to a certain extent, and the particle size was reduced to 7.64μm, which was reduced by 45.62 %.