Actual Experiments Research Articles

Data-driven excavation trajectory planning for unmanned mining excavator

In autonomous mining scenarios, excavation trajectory planning plays a significant role since it considerably influences the working performance of the unmanned mining excavator (UME). Aiming at the limited dynamical characterization of traditional theoretical methods that yield unsatisfactory performance in trajectory planning, herein we propose a data-driven excavation trajectory planning framework based on deep learning for improving the operation performance in autonomous excavation scenarios. First, the actual sensing data is collected, and a temporal convolutional recurrent neural network (TCRNN) combining stacked dilated convolutions with an attention-based sequence to sequence (Seq2Seq) module is proposed to predict the digging force accurately. Then, the surrounding material profile is perceived based on Lidar, and a polynomial response surface is performed to point cloud reconstruction. Considering mining efficiency, energy consumption and stability in the excavation process, a multi-objective function is constructed, and a data-driven excavation trajectory planning framework based on TCRNN is established. To obtain the accurate solution of the planning framework, a discretization strategy based on Radau pseudospectral method is developed to model solving to ensure the working performance in autonomous mining. An actual unmanned prototype experiment is performed to investigate the performance of the proposed framework. The results demonstrate the effectiveness and superiority of the proposed framework.

Effects of alkali pretreatment on the flotation of spodumene and feldspar

The surface chemistry and morphology of minerals produced by alkali pretreatment affects their flotation behaviors. This study systematically investigated the impact on the mineral surface dissolution using NaOH as a modifier, which could modify the surface properties of minerals and improve the floatability difference between spodumene and feldspar. Various characterizations including flotation experiments, flotation kinetics analysis, SEM analysis, microcalorimetry analysis, zeta potential measurements, XPS analysis and ICP-MS analysis were conducted. Single minerals and actual ore flotation experiment results were in good agreement, and the flotation grade and recovery of Li2O in spodumene concentrates increased by 1.11 % and 6.99 %, respectively, after NaOH pretreatment. The flotation kinetics results demonstrated the cumulative recoveries of spodumene exhibited the best agreement with the first-order kinetic model and the flotation kinetic constant was improved after alkali pretreatment. Microcalorimetric results indicated the absolute values of the reaction heat for spodumene were higher than that of feldspar reacted with collectors after the alkali pretreatment. XPS and ICP results demonstrated the amounts of Al active sites on the spodumene surface was significantly increased after alkali pretreatment compared to that on the feldspar surface, improving the selective adsorption of NaOL.

Folding State within a Hysteresis Loop: Hidden Multistability in Nonlinear Physical Systems.

Identifying hidden states in nonlinear physical systems that evade direct experimental detection is important as disturbances and noises can place the system in a hidden state with detrimental consequences. We study a cavity magnonic system whose main physics is photon and magnon Kerr effects. Sweeping a bifurcation parameter in numerical experiments (as would be done in actual experiments) leads to a hysteresis loop with two distinct stable steady states, but analytic calculation gives a third folded steady state "hidden" in the loop, which gives rise to the phenomenon of hidden multistability. We propose an experimentally feasible control method to drive the system into the folded hidden state. We demonstrate, through a ternary cavity magnonic system and a gene regulatory network, that such hidden multistability is in fact quite common. Our findings shed light on hidden dynamical states in nonlinear physical systems which are not directly observable but can present challenges and opportunities in applications.

Effect of Different Time Step Sizes on Pedestrian Evacuation Time under Emergencies Such as Fires Using an Extended Cellular Automata Model

The cellular automata (CA) model has been a meaningful way to study pedestrian evacuation during emergencies, such as fires, for many years. Although the time step used in the CA model is one of the most essential elements, there is a lack of research on its impact on evacuation time. In this paper, we set different time step sizes in an extended cellular automaton model and discuss the effect of time step size on the overall evacuation time under different emergency types and levels. For a fixed step time mode, the larger the time step, the longer the evacuation time. In each time step size, the evacuation time gradually increases with the increase of emergency level, and there is a sharp increase when the time for pedestrians to move one step is exactly an integer multiple of the time step. When there is no friction between pedestrians, the evacuation time at each time step first decreases slightly with the increase of emergency level and then remains unchanged; the larger the time step, when the evacuation time remains unchanged, the lower the emergency level and the greater the evacuation time. For the variable time step model, when the friction between pedestrians approaches infinity, the total evacuation time does not change with the emergency level; when the friction between pedestrians is reduced, the total evacuation time slightly decreases with the increase of the emergency level. The less friction there is, the more significant the reduction. The results of previous actual experiments are also reflected in the simulation at a lower emergency level. The result shows that the time step size significantly impacts the evacuation simulation results of the CA model, and researchers should choose carefully to obtain more realistic simulation results.

Optimization Method of Sheet Metal Laser Cutting Process Parameters under Heat Influence

To address the issues of workpiece distortion and excessive material melting caused by heat accumulation during laser cutting of thin-walled sheet metal components, this paper proposes a segmented optimization method for process parameters in sheet metal laser cutting considering thermal effects. The method focuses on predetermined perforation points and machining paths. Firstly, an innovative temperature prediction model Tpr,t is established for the nth perforation point during the cutting process, with a prediction error of less than 10%. Secondly, using the PSO-BP-constructed prediction model for laser cutting quality features and an empirical model for processing efficiency features, a multi-objective model for quality and efficiency is generated. The NSGA II algorithm is employed to solve the objective optimization model and obtain the Pareto front. Next, based on the predicted temperature at the perforation point using the model Tpr,t, the TOPSIS decision-making method is applied. Different weights for quality and efficiency are set during the cutting stages where the temperature is below the lower threshold and above the upper threshold. Various combinations of machining parameters are selected, and by switching the parameters during the cutting process, the thermal accumulation (i.e., temperature) during processing is controlled within a given range. Finally, the effectiveness of the proposed approach is verified through actual machining experiments.

An Adapted NURBS Interpolator with a Switched Optimized Method of Feed-Rate Scheduling

With the increasing demand for processing precision in the manufacturing industry, feed-rate scheduling is a crucial component in achieving the processing quality of complex surfaces. A smooth feed-rate profile not only guarantees machining quality but also improves machining efficiency. Although the typical offline feed-rate scheduling method possesses good processing efficiency, it may not provide an optimal solution due to the NP-hard problem caused by the feed-rate scheduling of continuous curve segments, which easily results in excess kinetic limitations and feed-rate fluctuations in a real-time interpolation. Instead, the FIR (Finite Impulse Response) method is widely used to realize interpolation in real-time processing. However, the FIR method will filter out a large number of high-frequency signals, leading to a low-processing efficiency. Further, greater acceleration or deceleration is required to ensure the interpolation passes through the segment end at a predefined feed rate and the deceleration in the feed rate profile appears earlier, which allows the interpolation to easily exceed the kinetic limitation. At present, a simple offline or online method cannot realize the global optimization of the feed-rate profile and guarantee the machining efficiency. Moreover, the current feed-rate scheduling that considers both offline and online methods does not consider the situation that the call of offline data and online prediction data will lead to a decrease in the real-time performance of the CNC system. Further, real-time feed-rate scheduling data tend to dominate the whole interpolation process, thus reducing the effect of the offline feed-rate scheduling data. Hence, based on the tool path with C3 continuity (Cubic Continuously Differentiable), this paper first presents a basic interpolation unit relevant to the S-type interpolation feed-rate profile. Then, an offline local smooth strategy is proposed to smooth the feed-rate profile and reduce the exceeding of kinetic limitations and feed-rate fluctuations caused by frequent acceleration and deceleration. Further, a global online smoothing strategy based on the data generated by offline pre-interpolation is presented. What is more, FIR login and logout conditions are proposed to further smooth the feed-rate profile and improve the real-time performance and machining efficiency. The case study validates that the proposed method performs better in kinetic results compared with the typical offline and FIR methods in both the simulation experiment and actual machining experiments. Especially, in actual processing experiments, the proposed method obtains a 28% reduction in contour errors. Further, the proposed method compared with the FIR method obtains a 15% increase in machining efficiency but only a 4% decrease compared with the typical offline method.

Hand-eye calibration of line structured-light sensor by scanning and reconstruction of a free-placed standard cylindrical target

This paper proposed a hand-eye calibration method for a line structured-light (LSL) scanner mounted on industrial robot. Unlike widely used spherical targets, the target used in our work is a cylinder, which is inexpensive and easy to be manufactured in larger size to cover a wider calibration space. In order to address the profile self-occlusion and drastically shape changing of conic section during calibration, an unconstrained dimensionality reduction method is proposed to fit the scanned profiles. Based on fitted ellipse centers under various robot poses, the hand-eye calibration model is developed based on the difference between cylinder radius and the distance from the profile point to the cylinder axis. Simulation and actual experiments have shown that the proposed ellipse fitting method is more accurate and robust, and the calibration method achieves higher reconstruction accuracy under the same robot positioning error, need less robot operations to obtain more accurate hand-eye parameters.

Electric shovel trajectory tracking with inversion sliding mode based on Lyapunov functions

The harsh actual working environment and heavy weight of the electric shovel pose a great challenge to its trajectory tracking performance. Taking into account the strong robustness and anti-interference performance of the sliding mode control method and the global stability of the inversion control method, a Lyapunov-based inversion sliding mode trajectory tracking method is proposed in this paper, and its effectiveness is verified through numerical simulation based on the electromechanical coupling dynamic model of the electric shovel and the actual electric shovel tracking experiment. The results show that the trajectory tracking method can effectively realize the trajectory tracking of the electric shovel accompanied by tracking deviation within 0.3 m. The existence of the track slip phenomenon makes the electric shovel shake when steering, which also makes the actual operating velocity of the electric shovel fluctuate greatly. Meanwhile, the steering process of the electric shovel is accompanied by a regenerative braking steering mode.

See farther and more: a master-slave UAVs based synthetic optical aperture imaging system with wide and dynamic baseline.

An open challenge remained in designing an optical system to capture the aerial image with a wide field of view (FoV) and high resolution. The optical system of one camera from a single unmanned aerial vehicle (UAV) can hardly promise the FoV and resolution. The conventional swarm UAVs can form the camera array with a short or fixed baseline. They can capture the images with a wide FoV and high resolution, but the cost is the requirement of many UAVs. We aim to design a camera array with a wide and dynamic baseline to reduce the demand for UAVs to organize a synthetic optical aperture. In this thought, we propose a master-slave UAVs-based synthetic optical aperture imaging system with a wide and dynamic baseline. The system consists of one master UAV and multiple slave UAVs. Master and slave UAVs provide the global and local FoVs, respectively, and improve the efficiency of image acquisition. In such a system, fusing UAV images becomes a new challenge due to two factors: (i) the small FoV overlap of slave UAVs and (ii) the gap in resolution scale from slave to master UAV images. To deal with it, a coarse-to-fine stitching method is proposed to stitch up the multi-view images into one to obtain a wide FoV with high resolution. A video stabilization method has also been designed for the proposed imaging system. Challenges caused by wide and dynamic baselines can thus be solved by the above methods. Actual data experiments demonstrate that the proposed imaging system achieves high-quality imaging results.

High authenticity steering feel control strategy for Steer-by-Wire system based on terminal sliding mode rack force observer

This article proposes a high authenticity steering feel control strategy for Steer-by-Wire (SBW) system based on terminal sliding mode rack force observer, in response to the issue of how to design the steering feel of the SBW system to reflect road conditions. Firstly, a terminal sliding mode rack force observer is designed to achieve real-time and accurate estimation of steering system rack force. Then, based on the vehicle motion state, design the feedback characteristic curve of the rack force and the compensation torque of the handwheel to obtain the expected steering feel, and use the automatic disturbance rejection controller (ADRC) method to control the voltage of the handwheel motor. In simulation testing, a SBW open loop control system model is established to verify the performance of the designed rack force observer. The objective evaluation indicators are used to verify that the designed feedback method can intuitively adjust and meet the driver's steering feel needs, and the impact of various compensation torques on the evaluation indicators is analyzed. Finally, the results of actual vehicle experiments indicate that the designed method can provide suitable steering feel, verifying the feasibility of the design technology.

Design and experiment of Panax notoginseng root orientation transplanting device based on YOLOv5s.

Consistent root orientation is one of the important requirements of Panax notoginseng transplanting agronomy. In this paper, a Panax notoginseng orientation transplanting method based on machine vision technology and negative pressure adsorption principle was proposed. With the cut-main root of Panax notoginseng roots as the detection object, the YOLOv5s was used to establish a root feature detection model. A Panax notoginseng root orientation transplanting device was designed. The orientation control system identifies the root posture according to the detection results and controls the orientation actuator to adjust the root posture. The detection results show that the precision rate of the model was 94.2%, the recall rate was 92.0%, and the average detection precision was 94.9%. The Box-Behnken experiments were performed to investigate the effects of suction plate rotation speed, servo rotation speed and the angle between the camera and the orientation actuator(ACOA) on the orientation qualification rate and root drop rate. Response surface method and objective optimisation algorithm were used to analyse the experimental results. The optimal working parameters were suction plate rotation speed of 5.73 r/min, servo rotation speed of 0.86 r/s and ACOA of 35°. Under this condition, the orientation qualification rate and root drop rate of the actual experiment were 89.87% and 6.57%, respectively, which met the requirements of orientation transplanting for Panax notoginseng roots. The research method of this paper is helpful to solve the problem of orientation transplanting of other root crops.

A novel subset assignment and matching method for DIC measurement of complex deformation in aircraft braking process

This paper proposes a measurement method related to the braking deformation of a complex motion. During the braking process, the deformation of the wheel includes large amounts of movement, vibration, warping, and distortion. A novel subset assignment and correlation method is proposed to measure the complex deformation. The proposed method can greatly improve the accuracy and stability of the calculation of complex deformations by simplifying the complex deformation into translational deformations in logarithmic coordinate system. According to the simulation and actual experiments, the proposed method can be utilized to measure the deformations of up to 100% tensile strain under complex deformation. According to the accuracy verification experiment, the error of the proposed method is less than 50 με. The results show that the proposed method can effectively carry out structural deformation measurement in the complex motion and deformation process. The proposed method has great significance for structural performance analysis and optimization design considering complex motion and deformation.

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

AbstractThe surface‐roughness machine vision measurement method requires training numerous samples with uniform roughness values to construct a prediction model. Insufficient actual sample size will make the model lack generalization ability. So, a grinding surface‐roughness measurement method combined with simulation data and transfer learning is proposed. First, through surface simulation, inverse fitting, and three‐dimensional (3D) modeling, more realistic simulation samples are obtained, real samples are prepared, and a simulation imaging system and an actual imaging experiment to acquire surface images are designed. Second, the data distribution difference between the simulation domain and the actual domain through transfer component analysis and the use of the adjusted simulation‐domain data and its label to train a support vector regression model is adjusted. Finally, the model predicts the surface roughness of the uncalibrated actual‐domain sample. For shared features of transferable simulation images and actual images, using the high sensitivity of color information, image features are enhanced and a proportion of pure color region index is designed, strongly correlated within the domain but less differentiated between domains. The results show that the transfer learning prediction accuracy based on this index exceeds 90%, verifying the feasibility of the proposed method and providing a new improvement strategy for sample expansion in visual roughness measurement.

Application of user preference mining algorithms based on data mining and social behavior in brand building

Small and medium-sized enterprises currently suffer from a lack of branding. Therefore, to further promote their active branding, this study proposes a user preference mining algorithm based on data mining and social behavior. Employing this algorithm to study the degree of users’ brand preference can provide data support for enterprises’ brand building. The experimental results showed that the proposed algorithm outperforms previous algorithms in terms of performance, convergence, and accuracy. The area under the curve reached 0.953, indicating highly authentic output results with extremely high realism. In actual simulation experiments, its prediction results for the user’s brand preference index are accurate, with an error of only 0.11, and the algorithm has extremely high ratings among industry insiders. In conclusion, the user-preference mining algorithm based on data mining and social behaviors suggested in this study plays a better role in promoting an enterprise’s brand building. It can help the enterprise know the level of consumer preference for its brand; accordingly, it can determine the shortcomings in, provide effective and accurate data support for, and thereby promote its brand building.

Automation of etch pit analyses on solid-state nuclear track detectors with machine learning for laser-driven ion acceleration.

Solid-state nuclear track detectors (SSNTDs) are often used as ion detectors in laser-driven ion acceleration experiments and are considered to be the most reliable ion diagnostics since they are sensitive only to ions and measure ions one by one. However, ion pit analyses require tremendous time and effort in chemical etching, microscope scanning, and ion pit identification by eyes. From a laser-driven ion acceleration experiment, there are typically millions of microscopic images, and it is practically impossible to analyze all of them by hand. This research aims to improve the efficiency and automation of SSNTD analyses for laser-driven ion acceleration. We use two sets of data obtained from calibration experiments with a conventional accelerator where ions with known nuclides and energies are generated and from actual laser experiments using SSNTDs. After chemical etching and scanning the SSNTDs with an optical microscope, we use machine learning to distinguish the ion etch pits from noises. From the results of the calibration experiment, we confirm highly accurate etch-pit detection with machine learning. We are also able to detect etch pits with machine learning from the laser-driven ion acceleration experiment, which is much noisier than calibration experiments. By using machine learning, we successfully identify ion etch pits ∼105 from more than 10 000 microscopic images with a precision of ≳95%. A million microscopic images can be examined with a recent entry-level computer within a day with high precision. Machine learning tremendously reduces the time consumption on ion etch pit analyses detected on SSNTDs.

Point-to-Point-Based Optimization Method of Ballast Water Allocation for Revolving Floating Cranes with Experimental Verification

The Revolving Floating Crane (RFC) is a specialized engineering vessel crucial for offshore lifting operations, such as offshore platform construction and deep-water salvaging. It boasts impressive lifting capacity, good adaptability to various environmental conditions, and high operational efficiency. Conventionally, the safety and stability of RFC operations heavily depend on manual ballast water allocation, which is directly influenced by factors such as personnel status and sea conditions. These manual operations often result in reduced lifting efficiency, higher energy consumption, and compromised operational safety. In response, this paper introduces a ballast water-allocation approach based on the Point-to-Point (PTP) theory for the intelligent operation process of the RFC. The fundamental principles of the PTP theory are analyzed, and a method tailored to optimize ballast water allocation for RFC is proposed. Considering the unique characteristics of the ballast system and the specific requirements of lifting operations, an optimization model for PTP-based ballast water allocation is established. Numerical experiments are conducted to verify the efficacy and reliability of the proposed method. Comparing it to the conventional approaches, the results demonstrate a notable 17.75% reduction in energy consumption and an impressive 73.49% decrease in decision-making time, showcasing the superiority of the proposed approach. Finally, the engineering feasibility of the PTP-based optimization method for ballast water allocation is validated through actual lifting experiments, underscoring its potential to enhance RFC operations.

Prediction of Specific Fuel Consumption of a Tractor during the Tillage Process Using an Artificial Neural Network Method

In mechanized agricultural activities, fuel is particularly important for tillage operations. In this study, the impact of seven distinct parameters on fuel usage per unit of draft power was examined. The parameters are tractor power, soil texture index, plowing speed, plowing depth, width of implement, and both initial soil moisture content and soil bulk density. This study investigated the construction of an artificial neural network (ANN) model for tractor-specific fuel consumption predictions for two tillage implements: chisel and moldboard plows. The ANN model was created based on the collection of related data from previous research studies, and the validation was performed using actual field experiments in clay soil using a chisel plow. The developed ANN model (9-22-1) was confirmed by graphical assessment; additionally, the root-mean-square error (RMSE) was computed. Based on the RMSE, the results demonstrated a good agreement for specific fuel consumption per draft power between the observed and predicted values, with corresponding RMSE values of 0.08 L/kWh and 0.075 L/kWh for the training and testing datasets, respectively. The novelty of the work presented in this paper is that, for the first time, a farm machinery manager can optimize tractor fuel consumption per draft power by carefully controlling certain parameters, such as initial soil moisture content, tractor power, plowing speed, implement width, and depth of plowing. The results show that the input parameters make a significant contribution to the output over the used data with different percentages. Accordingly, the contribution analysis showed that the implement width had a high impact on tractor-specific fuel consumption for both plows at 30.13%; additionally, the chisel and moldboard plows contributed 4.19% and 4.25% in predicting tractor fuel consumption per draft power. This study concluded that practical useful advice for agricultural production can be achieved through optimizing fuel consumption rate by selecting the proper levels of affecting parameters to reduce fuel costs. Moreover, an ANN model could be used to develop future tractor fuel-planning schemes for tillage operations.

Evaluating the Tribological Behaviour in Cutting Operations Using a Modified Ball-on-Disc Open Tribotester

Predicting the tribological behaviour in the secondary shear zone in the metal-cutting processes is considered a significant challenge in contemporary research. This work investigated the frictional performance in the secondary shear zone of a planing process using a modified ball-on-disc open tribometer. The values of the coefficient of friction (COF) were tracked between an AISI4140 + QT disc (chip) and a cemented carbide ball (cutting tool) coated with TiAlN under three contact pressures of 0.5, 1, and 2 GPa at a range of sliding speeds starting from 0.2 m/s to 1.6 m/s. The tests were conducted under both dry and lubricated conditions using three commercial cutting fluids of CSF 35 straight oil, Vasco 6000, and Zubora 67H emulsions. Also, the MWFs were tested for their rheological properties and wettability. The tribometer results validated the same COF trend as that in the actual metal-cutting experiments, particularly at 0.5 and 1 GPa in dry conditions. Moreover, Zubora 67H emulsion is proven to be the optimal choice due to it reducing the COF between the rubbing contacts by up to 78%. Furthermore, it showed the lowest contact angle and viscosity index, revealing its ability to easily penetrate, especially at higher temperatures, within the secondary cutting zone.

UCST-Type Soluble Immobilized Cellulase: A New Strategy for the Efficient Degradation and Improved Recycling Performance of Wastepaper Cellulose.

This paper reports an innovative study that aims to address key issues in the efficient recycling of wastepaper cellulose. The research team utilized the temperature-responsive upper critical solution temperature (UCST) polymer P(NAGA-b-DMA) in combination with the LytA label's affinity for choline analogs. This innovative approach enabled them to successfully develop a novel soluble immobilized enzyme, P(NAGA-b-DMA)-cellulase. This new enzyme has proven highly effective, significantly enhancing the degradation of wastepaper cellulose while demonstrating exceptional stability. Compared with the traditional insoluble immobilized cellulase, the enzyme showed a significant improvement in the pH, temperature stability, recycling ability, and storage stability. A kinetic parameter calculation showed that the enzymatic effectiveness of the soluble immobilized enzyme was much better than that of the traditional insoluble immobilized cellulase. After the immobilization reaction, the Michaelis constant of the immobilized enzyme was only increased by 11.5%. In the actual wastepaper degradation experiment, the immobilized enzyme was effectively used, and it was found that the degradation efficiency of wastepaper cellulose reached 80% of that observed in laboratory conditions. This novel, thermosensitive soluble immobilized cellulase can efficiently catalyze the conversion of wastepaper cellulose into glucose under suitable conditions, so as to further ferment into environmentally friendly biofuel ethanol, which provides a solution to solve the shortage of raw materials and environmental protection problems in the paper products industry.

The Impact of Assimilating Cirrus‐Effected Infrared Satellite Radiance From the FY‐4A AGRI on Water Vapor Analysis and Rainstorm Forecasting

AbstractIn this study, a method for assimilating FY4A advanced geostationary radiance imager (AGRI) cirrus‐effected radiances (CER) is investigated, and the impact of this method on water vapor analysis and rainstorm forecasting is examined through observing system simulation experiments and actual case experiments. The high proportion of inverted humidity profiles in the cirrus‐effected pixels is the main reason for the negative effect of assimilation in the mid‐to‐lower troposphere. To address this, relevant constraint conditions are incorporated into the cost function. The statistical results reveal that the addition of a CER assimilation improves the analysis increment of water vapor, with pattern correlation coefficients of 0.33, 0.35, and 0.20 at 200, 300, and 400 hPa, respectively, which are greater than those of a clear‐sky radiance assimilation (0.28, 0.33, and 0.17, respectively). Moreover, the inclusion of a CER assimilation greatly improves data utilization, and has a neutral to positive effect on precipitation forecasting.