Search Results (92)

To address the challenges of short allowable motion windows and complex motion planning inherent in dual forging manipulator systems, this study proposes a coordinated motion pattern tailored to dual-manipulator operations, focusing on forging deformation behavior and press control characteristics. First, six representative long-shaft forging materials were classified based on typical industrial applications. Using DEFORM-3D (V11.0) software, the deformation process during the elongation operation was analyzed, and the velocity and displacement characteristics at both ends of the forgings were extracted to clarify the compliant motion requirements of the grippers. Next, a segmented computation method for manipulator allowable motion time was developed based on the motion–time curve of the hydraulic press, significantly improving the time utilization efficiency for coordinated control. Furthermore, experimental tests were carried out to verify the dynamic response performance and motion accuracy of the dual-manipulator system. Finally, the dual-manipulator forging cycle was systematically divided into four stages—pre-forging adjustment, inter-pass compliance, execution phase, and forging completion—resulting in a structured and implementable coordination control framework. This research provides both a theoretical foundation and practical pathway for achieving efficient and precise coordinated motion control in dual forging manipulator systems, offering strong potential for engineering application and industrial deployment. Full article

Climate change is increasing global temperatures and imposing new constraints on tree regeneration, especially in late-successional species exposed to simultaneous drought and low-light conditions. To disentangle the effects of warming from those of atmospheric drought, we conducted a multifactorial growth chamber experiment on Fagus sylvatica seedlings, manipulating temperature (25 °C and +7.5 °C above optimum), soil moisture (well-watered vs. water-stressed), and light intensity (high vs. low), while maintaining constant vapor pressure deficit (VPD). We assessed growth, biomass allocation, leaf gas exchange, water relations, and xylem hydraulic traits. Warming significantly reduced total biomass, leaf area, and water-use efficiency, while increasing transpiration and residual conductance, especially under high light. Under combined warming and drought, seedlings exhibited impaired osmotic adjustment, reduced leaf safety margins, and diminished hydraulic performance. Unexpectedly, warming under shade promoted a resource-acquisitive growth strategy through the production of low-cost leaves. These results demonstrate that elevated temperature, even in the absence of increased VPD, can compromise drought tolerance in beech seedlings and shift their ecological strategies depending on light availability. The findings underscore the need to consider multiple, interacting stressors when evaluating tree regeneration under future climate conditions. Full article

Ensuring the overall efficiency of hydraulic fracturing treatment depends on the ability to forecast bottomhole pressure. It has a direct impact on fracture geometry, production efficiency, and cost control. Since the complications present in contemporary operations have proven insufficient to overcome inherent uncertainty, the precision of bottomhole pressure predictions is of great importance. Achieving this objective is possible by employing machine learning algorithms that enable real-time forecasting of bottomhole pressure. The primary objective of this study is to produce sophisticated machine learning algorithms that can accurately predict bottomhole pressure while injecting guar cross-linked fluids into the fracture string. Using a large body of work, including 42 vertical wells, an extensive dataset was constructed and meticulously packed using processes such as feature selection and data manipulation. Eleven machine learning models were then developed using parameters typically available during hydraulic fracturing operations as input variables, including surface pressure, slurry flow rate, surface proppant concentration, tubing inside diameter, pressure gauge depth, gel load, proppant size, and specific gravity. These models were trained using actual bottomhole pressure data (measured) from deployed memory gauges. For this study, we carefully developed machine learning algorithms such as gradient boosting, AdaBoost, random forest, support vector machines, decision trees, k-nearest neighbor, linear regression, neural networks, and stochastic gradient descent. The MSE and R² values of the best-performing machine learning predictors, primarily gradient boosting, decision trees, and neural network (L-BFGS) models, demonstrate a very low MSE value and high R² correlation coefficients when mapping the predictions of bottomhole pressure to actual downhole gauge measurements. R² values are reported as 0.931, 0.903, and 0.901, and MSE values are reported at 0.003, 0.004, and 0.004, respectively. Such low MSE values together with high R² values demonstrate the exceptionally high accuracy of the developed models. By illustrating how machine learning models for predicting pressure can act as a viable alternative to expensive downhole pressure gauges and the inaccuracy of conventional models and correlations, this work provides novel insight. Additionally, machine learning models excel over traditional models because they can accommodate a diverse set of cross-linked fracture fluid systems, proppant specifications, and tubing configurations that have previously been intractable within a single conventional correlation or model. Full article

This paper outlines the outcomes of a multidisciplinary initiative aimed at creating flexible arms that leverage key aspects of soft-bodied sea animal anatomy. We designed and prototyped a flexible arm inspired by nature while focusing on integrating practical engineering technologies from a system perspective. The mechanical structure was developed by studying soft-bodied marine animals from the cephalopod order. Simultaneously, we carefully addressed engineering challenges and limitations, including material flexibility, inherent safety, energy efficiency, cost-effectiveness, and manufacturing feasibility. The design process is demonstrated through two successive generations of prototypes utilizing fluidic actuators. The first one exhibited both radial and longitudinal actuators, the second one only longitudinal actuators, thus trading off between bio-inspiration and engineering constraints. Full article

This study investigates the onset and decay mechanisms of sheet cavitation within a chamfered orifice under turbulent conditions, using high-speed backlight imaging for detailed frame-by-frame analysis. A distinctive metastable sheet cavitation regime was identified, distinguished by its unique hysteresis behavior during onset conditions, with the ability to control periodicity through variations in cavitation numbers. This new sheet cavitation regime appears at high cavitation numbers, contrary to typical expectations of cavitation inception, highlighting a new potential risk within the range of safe operation for hydraulic systems equipped with control valves. Furthermore, linear growth and rapid collapse of the bubble sheet were observed, which differs from the conventional periodic behavior of sheet cavitation on hydrofoils. The new mechanism to intentionally initiate and control this sheet cavitation regime by manipulating the pressure drop across the orifice could potentially be adopted for industrial applications, particularly in the generation of controlled and dispersed bubbles. Future research should include quantifying bubble dynamics within this regime and assessing the effects of fluid properties and orifice geometries on cavitation characteristics. In summary, this study introduces a new perspective on metastable sheet cavitation, emphasizing its potential applications and importance in the design and operation of fluid systems. Full article

As operational scenarios become more complex and task demands intensify, the requirements for the intelligence and automation of manipulators in industry are increasing. This work investigates the challenge of posture tracking control for hydraulic flexible manipulators by proposing a discrete-time integral terminal sliding mode predictive control (DITSMPC) method. First, the proposed method develops a second-order dynamic model of the manipulator using the Lagrangian dynamic strategy. Second, a discrete-time sliding mode control (SMC) law based on an adaptive switching term is designed to achieve high-precision tracking control of the system. Finally, to weaken the influence of SMC buffeting on the manipulator system, the predictive time domain function is integrated into the proposed SMC law, and the delay estimation of the unknown term in the manipulator system is carried out. The DITSMPC scheme is derived and its convergence is proven. Simulation experiments comparing the DITSMPC scheme with the classical discrete-time SMC method demonstrate that the proposed scheme results in smooth torque changes in each joint of the manipulator, with the integral of torque variations being $5.22\times {10}^3$. The trajectory tracking errors for each joint remain within ±0.0025 rad, all of which are smaller than those of the classical scheme. Full article

In order to further improve the tracking performance of multiple-degree-of-freedom serial electro-hydraulic robotic manipulators, a high-performance multilayer neurocontroller will be proposed. In detail, multilayer neural networks will be employed to approximate the smooth and non-smooth state-dependent modeling uncertainties. Meanwhile, extended state observers will be utilized to estimate matched and unmatched time-varying disturbances. Moreover, these estimated values will be incorporated into the synthesized controller to compensate for the modeling uncertainties. Significantly, the proposed controller without “explosion of complexity” is suitable for the scene where the joint angular velocities are not measurable. Additionally, the sensor measurement noises can be reduced and input saturation nonlinearity will be handled. Full article

This paper presents the modeling and control of a flexible single-boom crane manipulator using a high-fidelity real-time simulation model. The model incorporates both electro-hydraulic actuation and flexible-body dynamics, with the flexible boom represented via the lumped parameter method. A systematic tuning and validation procedure ensures that the model accurately replicates the physical system’s dynamics, achieving an eigenfrequency accuracy of approximately 97% and a piston-position deviation within 1.2% of the overall stroke length in final tests. The real-time simulation model is utilized in both open-loop and closed-loop control schemes to investigate whether simulated data can reduce dependency on sensor feedback compared to a benchmark controller. While the simulation-based controller alone does not match the fully sensor-based closed-loop accuracy, the simulation-based feedforward improves performance by 83% compared to the standard model-based velocity feedforward. Additionally, integrating the real-time simulation with sensor feedback enhances the benchmark controller’s performance by approximately 16%. These findings highlight the potential of combining real-time, nonlinear simulation with conventional sensor feedback to enhance the control of electro-hydraulic flexible manipulators. Full article

Segment assembly is one of the principal processes during tunnel construction using a tunnel boring machine (TBM). The segment erector is a robotic manipulator powered by a hydraulic system that assembles prefabricated concrete segments onto the excavated tunnel surface. In the case of a larger diameter, while the segment assembly has a more extensive range of motion, it also demands more control accuracy. However, the single-pump-based hydraulic system fails to meet the dual requirements. Therefore, this paper proposes a novel dual parallel-connected-pump hydraulic system consisting of a small displacement pump and a large displacement pump. On this basis, taking advantage of both the quick response and low dead zone of the small pump and the high flow range of the large pump, a two-level error allocation strategy is constructed to coordinate the two pumps and keep the motion error of segment assembly within a small range. Finally, comparative experiments were conducted, and the results show that the proposed scheme achieves the simultaneous high-level synchronization of the two pumps and high-precision and high-speed motion-tracking performance. Full article

This study addresses the challenges of traditional date palm harvesting, which is often labor-intensive and hazardous, by introducing an innovative solution utilizing multirotor flying vehicles (MRFVs). Unlike conventional methods such as hydraulic lifts and ground-based robotic manipulators, the proposed system integrates a quadrotor equipped with a winch and a suspended robotic arm with a precision saw. Controlled remotely via a mobile application, the quadrotor navigates to targeted branches on the date palm tree, where the robotic arm, guided by live video feedback from integrated cameras, accurately severs the branches. Extensive testing in a controlled environment demonstrates the system’s potential to significantly improve harvesting efficiency, safety, and cost-effectiveness. This approach offers a promising alternative to traditional harvesting methods, providing a scalable solution for date palm cultivation, particularly in regions with large-scale plantations. This work marks a significant advancement in the field of agricultural automation, offering a safer, more efficient method for harvesting date palms and contributing to the growing body of knowledge in automated farming technologies. Full article

Large-scale explosive ordnance disposal (EOD) robotic manipulators can replace manual EOD tasks, offering higher efficiency and better safety. This study focuses on the control strategies and response speeds of EOD robotic manipulators. Using Adams to establish the dynamic model of an EOD robotic manipulator and constructing a hydraulic system model in AMEsim, a co-simulation model is integrated. This study proposes a PID control strategy optimized by the particle swarm optimization (PSO) algorithm for a backpropagation (BP) neural network and simulates the system’s step response for analysis. To address the vibration issues arising during the manipulator’s motion, B-spline curves are used for trajectory optimization to reduce vibrations. The PSO algorithm optimizes the connection weight matrix of the BP neural network, solving the potential problem of local minima during the training process of the BP neural network, thereby enhancing the global search capability, learning efficiency, and network performance. Simulation results indicate that compared to traditional BP+PID control, genetic algorithm (GA)+PID control, and whale optimization algorithm (WOA)-BP+PID control, the PSO-BP+PID algorithm control rapidly tunes the PID control parameters K_p, K_i, and K_d. Under the same step function conditions, the overshoot is only 1.37%, significantly lower than other methods, and the settling time is only 14 s. After stabilization, there is almost no error, demonstrating faster response speed, higher control accuracy, and stronger robustness. This research has theoretical value and reference significance for the control methods and improvements in EOD robotic manipulators. Full article

With the advancement of deep-sea exploration, the demand for underwater manipulators capable of long-duration heavy-duty operations has intensified. Water-hydraulic systems exhibit less viscosity variation with increasing depth than oil-based systems, offering better adaptability to deep-sea conditions. Using seawater as the driving medium inherently eliminates issues such as oil contamination by water, frequent maintenance limiting underwater operation time, and environmental pollution caused by oil leaks. This paper introduces a deep-sea manipulator directly driven by seawater from the deep-sea environment. To address the challenges of weak lubrication and high corrosion associated with water hydraulics, a reciprocating plunger seal was adopted, and a water-hydraulic actuator was developed. The installation positions of actuator hinges and maximum output force requirements were optimized using particle swarm optimization (PSO), effectively reducing the manipulator’s self-weight. Through kinematic and inverse kinematic analyses and joint performance tests, a six-degree-of-freedom water-hydraulic manipulator was designed with a maximum reach of 2.5 m, a lifting capacity of 5000 N, and end-effector positioning accuracy within 18 mm. Full article

The analysis of liquid-fueled molten-salt reactors (LFMSRs) during steady state, operational transients and accident scenarios requires addressing unique reactor multi-physics challenges with coupling between thermal hydraulics, neutronics, inventory control and species distribution phenomena. This work utilizes the General Nuclear Field Operation and Manipulation (GeN-Foam) code to perform coupled thermal-hydraulics and neutronics calculations of an LFMSR design. A framework is proposed as part of this study to perform modeling, design optimization, and uncertainty quantification. The framework aims to establish a protocol for the studies and analyses of LFMSR which can later be expanded to other advanced reactor concepts too. The Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) statistical analysis tool was successfully coupled with GeN-Foam to perform uncertainty quantification studies. The uncertainties were propagated through the input design parameters, and the output uncertainties were characterized using statistical analysis and Spearman rank correlation coefficients. Three analyses are performed (namely, scalar, functional, and three-dimensional analyses) to understand the impact of input uncertainty propagation on temperature and velocity predictions. Preliminary three-dimensional reactor analysis showed that the thermal expansion coefficient, heat transfer coefficient, and specific heat of the fuel salt are the crucial input parameters that influence the temperature and velocity predictions inside the LFMSR system. Full article

To protect critical computing systems from network attacks, modern enterprises typically employ physical isolation measures to disconnect them from open networks such as the Internet. However, attackers can still infiltrate these closed networks through internal employees or supply chain vulnerabilities. This presents the primary challenge that attackers face: how to effectively manage and manipulate infected devices that are isolated from the external network. In this paper, we propose a new covert communication technology called HydraulicBridge, which demonstrates how air gap networks can communicate through covert water pressure-fluctuation channels. Specifically, we demonstrate how water pressure from water pipes can be used to communicate with infected hosts within an air gap network. Additionally, we provide experimental results demonstrating the feasibility of covert channels and test the communication speed in the experimental environment. Finally, we offer a forensic analysis and propose various methods for detecting and blocking this channel. We believe that this study provides a comprehensive introduction to previously unseen attack vectors that security experts should be aware of. Full article

The abrasive waterjet machining process was introduced in the 1980s as a new cutting tool; the process has the ability to cut almost any material. Currently, the AWJ process is used in many world-class factories, producing parts for use in daily life. A description of this process and its influencing parameters are first presented in this paper, along with process models for the AWJ tool itself and also for the jet–material interaction. The AWJ material removal process occurs through the high-velocity impact of abrasive particles, whose tips micromachine the material at the microscopic scale, with no thermal or mechanical adverse effects. The macro-characteristics of the cut surface, such as its taper, trailback, and waviness, are discussed, along with methods of improving the geometrical accuracy of the cut parts using these attributes. For example, dynamic angular compensation is used to correct for the taper and undercut in shape cutting. The surface finish is controlled by the cutting speed, hydraulic, and abrasive parameters using software and process models built into the controllers of CNC machines. In addition to shape cutting, edge trimming is presented, with a focus on the carbon fiber composites used in aircraft and automotive structures, where special AWJ tools and manipulators are used. Examples of the precision cutting of microelectronic and solar cell parts are discussed to describe the special techniques that are used, such as machine vision and vacuum-assist, which have been found to be essential to the integrity and accuracy of cut parts. The use of the AWJ machining process was extended to other applications, such as drilling, boring, milling, turning, and surface modification, which are presented in this paper as actual industrial applications. To demonstrate the versatility of the AWJ machining process, the data in this paper were selected to cover a wide range of materials, such as metal, glass, composites, and ceramics, and also a wide range of thicknesses, from 1 mm to 600 mm. The trends of Industry 4.0 and 5.0, AI, and IoT are also presented. Full article