Multi-robot systems, owing to their parallel operation and cooperative capabilities, have become an important means of improving the efficiency of complex workpiece machining. However, task allocation methods directly determine the overall system performance, which is particularly critical in scenarios with high curvature and stringent stiffness requirements. This study focuses on a Dual-Robot Carving System (DRCS) and proposes a task allocation method that incorporates stiffness performance constraints, using stereoscopic stone carving as a representative application. First, a workstation optimization model is developed based on the average normal stiffness as the evaluation metric, enabling the selection and allocation of high-complexity tasks. This approach not only ensures machining stiffness but also effectively decouples the task allocation problem. Subsequently, two allocation strategies are designed for low-complexity tasks: one based on machinability and the other on machining time balancing. Comparative simulations and physical experiments are conducted to evaluate the efficiency differences between the proposed methods and the single-robot machining mode. The results show that the machining time balancing strategy improves efficiency by 14.33% compared with the machinability-based strategy, and by 84.78% compared with the single-robot mode. These findings demonstrate the effectiveness of the proposed method in enhancing dual-robot collaborative efficiency and provide a novel modeling perspective and technical support for multi-robot task allocation under stiffness constraints in complex workpiece machining.
It has been more than a decade since the introduction of the Industry 4.0 paradigm. Since then, many issues have been raised in the world: the COVID-19 pandemic, sustainable development goals, and recent dramatic changes in global politics. The global value chains were broken during the pandemic, and the importance of humans as the most important element of the production system was highlighted. It caused rethinking about current industrial paradigms, including the brand new paradigm of Industry 4.0. More focus has been put on human workers, sustainability, and the resilience of the value chain, so the Industry 4.0 update was presented as Industry 5.0. A specific methodology to evaluate the maturity level of the manufacturing industry with regard to Industry 4.0/5.0 is presented and tested in the Croatian manufacturing industry. The developed methodology is unique since it puts Industry 5.0 in the right context with Industry 4.0. Therefore, the Industry 4.0 index remains the main indicator; however, alignment with three Industry 5.0 aims (human-centricity, sustainability, and resilience) represents three additional indicators. The results of the current state analysis are presented as a case study with a discussion about the results and methodology itself.
In the field of new energy vehicles and related sectors, treating butyl acetate (BA), a typical oxygen-containing volatile organic compound (VOC), is becoming increasingly important. The surface structure and physicochemical properties of a CeO_2-U catalyst were adjusted by introducing 0.5% Pd, and compared with Al_2O_3 and TiO_2 catalysts containing the same Pd loading. Characterizations using SEM, XPS, in-situ DRIFTS, and other methods were conducted to explore the synergistic effect of Pd and Ce active components on catalytic oxidation of BA. The results showed that the introduction of Pd increased CeO_2-U′s CO_2 yield from 77.8% to 90.7% at 220 ℃, significantly promoting the deep oxidation process of BA and alleviating the issue of CO_2 selectivity delay. The introduction of Pd enhanced the mobility and reactivity of lattice oxygen in CeO_2, increased the proportion of surface Ce^3+, and boosted surface oxygen vacancy concentration. Additionally, the catalytic oxidation mechanism of BA over Pd/CeO_2-U was confirmed through in-situ DRIFTS analysis, indicating that the L-H mechanism was followed at low temperatures (T<200 ℃), while the MvK reaction mechaism occured followed at high temperature (T>200 ℃). It was found that the decomposition of intermediate carboxylate served as the rate-controlling step. These findings have implications for controlling BA in the field of related sectors.
Renewable energy sources, Environmental protection
Learning is a key function in the brain to be able to achieve the activity patterns required to perform various activities. While specific behaviors are determined by activity in localized regions, the interconnections throughout the entire brain play a key role in enabling its ability to exhibit desired activity. To mimic this setup, this paper examines the use of reservoir computing to control a linear-threshold network brain model to a desired trajectory. We first formally design open- and closed-loop controllers that achieve reference tracking under suitable conditions on the synaptic connectivity. Given the impracticality of evaluating closed-form control signals, particularly with growing network complexity, we provide a framework where a reservoir of a larger size than the network is trained to drive the activity to the desired pattern. We illustrate the versatility of this setup in two applications: selective recruitment and inhibition of neuronal populations for goal-driven selective attention, and network intervention for the prevention of epileptic seizures.
Control engineering systems. Automatic machinery (General), Technology
Abstract Variational mode decomposition (VMD) is a suitable tool for processing cavitation-induced vibration signals and is greatly affected by two parameters: the decomposed number K and penalty factor α under strong noise interference. To solve this issue, this study proposed self-tuning VMD (SVMD) for cavitation diagnostics in fluid machinery, with a special focus on low signal-to-noise ratio conditions. A two-stage progressive refinement of the coarsely located target penalty factor for SVMD was conducted to narrow down the search space for accelerated decomposition. A hybrid optimized sparrow search algorithm (HOSSA) was developed for optimal α fine-tuning in a refined space based on fault-type-guided objective functions. Based on the submodes obtained using exclusive penalty factors in each iteration, the cavitation-related characteristic frequencies (CCFs) were extracted for diagnostics. The power spectrum correlation coefficient between the SVMD reconstruction and original signals was employed as a stop criterion to determine whether to stop further decomposition. The proposed SVMD overcomes the blindness of setting the mode number K in advance and the drawback of sharing penalty factors for all submodes in fixed-parameter and parameter-optimized VMDs. Comparisons with other existing methods in simulation signal decomposition and in-lab experimental data demonstrated the advantages of the proposed method in accurately extracting CCFs with lower computational cost. SVMD especially enhances the denoising capability of the VMD-based method.
Stochastic computing (SC) is widely known for its high error tolerance and efficient computing ability of complex functions with remarkably simple logic gates. The noise of electronic devices is widely used to be the entropy source due to its randomness. Compared with thermal noise and random telegraph noise (RTN), flicker noise is favored by researchers because of its high noise density. Meanwhile, unlike using RRAM, PCRAM and other emerging memory devices as the entropy source, using logic devices does not require any additional process steps, which is significant for industrialization. In this work, we systematically and statistically studied the <i>1/f</i> noise characteristics of 14 nm FinFET, and found that miniaturizing the channel area of the device or lowering the ambient temperature can effectively increase the <i>1/f</i> noise density of the device. This is of great importance to improve the accuracy of the SC system and simplify the complexity of the stochastic number generator (SNG) circuit. At the same time, these rules of <i>1/f</i> noise characteristics in FinFET devices can provide good guidance for our device selection in circuit design.
The detection and tracking of small and weak maneuvering radar targets in complex electromagnetic environments is still a difficult problem to effectively solve. To address this problem, this paper proposes a dynamic programming tracking-before-detection method based on long short-term memory (LSTM) network value function(VL-DP-TBD). With the help of the estimated posterior probability provided by the designed LSTM network, the calculation of the posterior value function of the traditional DP-TBD algorithm can be more accurate, and the detection and tracking effect achieved for maneuvering small and weak targets is improved. Utilizing the LSTM network to model the posterior probability estimation of the target motion state, the posterior probability moving features of the maneuvering target can be learned from the noisy input data. By incorporating these posterior probability estimation values into the traditional DP-TBD algorithm, the accuracy and robustness of the calculation of the posterior value function can be enhanced, so that the improved architecture is capable of effectively recursively accumulating the movement trend of the target. Simulation results show that the improved architecture is able to effectively reduce the aggregation effect of a posterior value function and improve the detection and tracking ability for non-cooperative nonlinear maneuvering dim small target.AbbreviationsLSTM: Long short-term memory; DP-TBD: Dynamic programming-based tracking before detection; DBT: Detection before tracking; TBD: Tracking before detection; HT-TBD: Tracking-before-detection algorithm based on the Hough transform; PF-TBD: Tracking-before-detection algorithm based on particle filtering; RFS-TBD: Tracking-before-detection algorithm based on random finite sets; SNR: Signal-to-noise ratio; DP: Dynamic programming; EVT: Extreme value theory; EVT: Generalized extreme value theory; GLRT: Generalized likelihood ratio detection; KT: Keystone transformation; PGA: Phase gradient autofocusing; CFAR: Constant false-alarm rate; J-CA-CFAR: Joint intensity-spatial CFAR; MF: Merit function; CP-DP-TBD: Candidate plot-based DP-TBD; CIT: Coherent integration time; RNN: Recurrent neural network; CS: Current statistical; Pd: Detection probability; Pt: Tracking probability.
Control engineering systems. Automatic machinery (General), Systems engineering
With the continuous improvement of the requirements of spiral bevel gear transmission for low noise and high precision, the optimization of high-efficiency tooth surface accuracy considering the motion axis error of machining machine tool has become a key point in the process of designing and manufacturing spiral bevel gear. Based on the theory of the multi-body dynamics system and the principle of gear meshing, a new tooth surface error model of spiral bevel gear considering the error of machine-tool moving axis is proposed and optimized, so as to obtain higher tooth surface accuracy and meshing efficiency. Firstly, according to the motion and machining principle of spiral bevel gear NC grinding machine, the corresponding relationship between the motion error of each axis of the NC gear grinding machine and the variation of cutting parameters of spiral bevel gear is analyzed, and the geometric motion error model of spiral bevel gear grinding machine is given; secondly, the tooth surface equation of the spiral bevel gear considering the machine-tool motion axis error is established; then, in view of Powell's dog leg optimization algorithm, the tooth surface considering the motion axis error of the machine tool is optimized, and the optimal cutting parameters of spiral bevel gear tooth surface machining are obtained. Finally, the simulation results show that the difference between the peak and peak values of transmission error and the maximum contact stress of the tooth surface is reduced by 72.6% and 1.62%, respectively, and the meshing efficiency is improved from 0.9798 to 0.9803 after optimization; by comparing the rolling experiment and simulation results, the maximum error between the quantitative parameters of the two groups of tooth surface contact marks is no more than 9%, which verifies the effectiveness and feasibility of this method, and provides a certain theoretical basis for practical production and processing.
Light detection and ranging (LiDAR), combining traditional radar technology with modern laser technology, has much potential for applications in navigation, mapping, and so on. Benefiting from the superior performance, an all-solid-state beam steering realized by integrated optical phased array (OPA) is one of the key components in the LiDAR system. In this review, we first introduce the basic principle of OPA for beam steering. Then, we briefly review the detailed advances of different solutions such as micro-electromechanical system OPA, liquid crystal OPA, and metasurface OPA, where our main focus was on the recent progress of OPA in photonic integrated chips. Finally, we summarize the different solutions and discuss the challenges and perspectives of all-solid-state beam steering for LiDAR.
Abstract Perennial grasses can reduce soil erosion, restore carbon stocks, and provide feedstocks for biofuels and bioproducts. Here, we show an additional benefit, amelioration of regional climate warming, and drying. Growing Miscanthus × giganteus, an example of perennial biomass crops, on US marginal land cools the Midwest Heartland summer by up to 1°C as predicted by a new coupled climate‐crop modeling system. This cooling is mainly caused by the increased duration and size of the Miscanthus × giganteus leaf canopy when compared with the existing vegetations on marginal land, resulting in larger solar reflection, more evapotranspiration, and decreased sensible heat transfer. Summer rainfall is increased through mesoscale circulation responses by 23–29 mm (14%–15%) and water vapor pressure deficit reduced by 5%–13%, lowering potential transpiration for all Midwest crops. Similar but weaker effects are simulated in the Southern Heartland. This positive feedback through the climate–crop interaction and teleconnection leads to 4%–8% more biomass production and potentially 12% higher corn and soybean yields, with greater yield stability. Growing perennials on marginal land could be a feasible solution to climate change mitigation and adaptation by strengthening food security and providing sustainable alternatives to fossil‐based products.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
Rotor dynamic balancing is essential in rotor industrial. The conventional balancing methods, including the influence coefficients method and modal balancing method, are effective, but lack economy and sufficient usage of the data. To overcome the disadvantages of the conventional balancing methods, a balancing method using unsupervised deep learning without weight trails had been proposed. The proposed network could identify the unbalanced forces from the data observed from just one run of the rotor and without labels. To validate the novel balancing method, an experimental rig is well-designed and established. Experimental validation and comparison with influence coefficients method are conducted. The experimental results show that the proposed balancing method gives consideration to both cost and accuracy. Compared with influence coefficients method, no extra weight trail process is needed and balancing performances are comparative. The experimental rig can be used for proving the scheme and for further same kind of research.
Control engineering systems. Automatic machinery (General), Technology (General)
Multi-disciplinary analysis was performed to analyze and investigate the thermal performance during transient operation of a 2 L diesel engine system with two different cooling systems. The multi-disciplinary model consisted of the engine thermal management system (ETMS) comprising a zero-dimensional engine model that can simulate the engine performance, and a one-dimensional flow model for cooling and lubrication systems with a controller. By deploying this approach, we were able to model different physical domains, including mechanical for the engine and the dynamometer and thermodynamic for the heat exchangers. Therefore, the thermal performance of the ETMS could be numerically predicted and analyzed. To develop the ETMS model, the physical properties, the heat transfer model, and the pressure drop were modeled. The base fluid, a 50/50 mixture of water and ethylene glycol (EG), and an Al<sub>2</sub>O<sub>3</sub> nanofluid with a 1.5% volume ratio were modeled based on the thermodynamic properties such as density, dynamic viscosity, thermal conductivity, and specific heat. Nanofluid, with its higher thermal conductivity and higher heat transfer coefficient, absorbed more heat from the combustion chamber through the water-jacket in the engine block. Therefore, the oil temperature for the nanofluid was effectively 2.5 °C less than for the base fluid following the step-load condition. Simulation results showed the better effect of nanofluid on thermal performance. The total flow rate of nanofluid decreased by 2.2 L/min, although the flow rate through the radiator with nanofluid increased by 0.81 L/min to obtain greater heat dissipation. Eventually, the piston and the liner temperatures with the nanofluid were drastically reduced by 7.55 and 8 °C, respectively, compared to those of the base fluid. Finally, when nanofluids was applied in automotive cooling systems, the temperature of the piston decreased by 7.3 °C due to the reduced overall thermal resistance from combustion chambers to outside air. The effect of working fluid on the diesel engine system could be predicted through the multi-disciplinary model.
Abderrahmane Khechekhouche, A. Muthu Manokar, Ravishankar Sathyamurthy
et al.
Researches in many laboratories on solar still desalination are concerned with increasing efficiency using only solar energy. One of the techniques is the difference in the thickness of the glass cover of the distiller. In order to see the influence of this parameter on efficiency, three similar stills with three different glass coverings were investigated. The flow of heat goes through the cover, and higher glass temperature leads to solar still productivity becoming lower. This paper presents an optimization of glass thickness (Gt) of a conventional solar still (CSS) in El Oued climate, Algeria. Based on the experimental results, the distilled water production rate, energy, and energy efficiency of the CSS have been discussed. The results showed that the suitable Gt of the CSS was 3 mm. The distilled water of around 3.15, 2.02, and 1.13 kg was produced by the CSS at energy efficiency of 30.71, 19.02, and 11.44% with the Gt of 3, 5, and 6 mm, respectively. The daily average exergy efficiency of 2.46, 1.38, and 0.84% was calculated for the CSS at Gt of 3, 5, and 6 mm, respectively.
The multi low speed,high-loaded bearing installed on same axis,different locations of the bearing fault pulse will appear aliasing coupling phenomenon,resulting in fault signal identification difficult. In this regard,based on the characteristic of corner-domain and full vector empirical mode decomposition to identify and diagnose the vibration source of the low speed,high-loaded bearing fault signal is proposed. Firstly,the spline interpolation algorithm is used to transform the acquired time domain nonstationary signal into the angular pseudo-stationary signal to realize the angular area resampling,extract the contact angle of rotation of bearing,and the vibration source recognition is completed. Then,the signal is decomposed and reconstructed by EMD,the dual-channel full-spectrum technique is used to fuse the homologous information. Finally,the fault is identified by after chamfering domain analysis of the reconstructed signal,extract the characteristic parameters of contact times of per circle to complete the signal fault type diagnosis. The experimental results show that the fault diagnosis method based on characteristic of corner-domain and full vector EMD can satisfy the requirement of low speed,high-loaded fault signal diagnosis to a certain extent,and it has certain practicability.
According to the problem that a certain type of automotive transmission case in the sale is easy to crack,the finite element modal analysis and operational modal tests of automotive transmission are carried out and the modal parameters of transmission are obtained. Then,the reliability of the results is verified by the modal correlation matrix. On this basis,through comparing the results between simulation and experiment,the correctness and reliability of the simulation analysis are proved. The mode shapes which prone to fatigue cracking are analyzed. Finally,from the perspective of the frequency shift,the installation type of the powertrain is optimized,the overall stiffness of the transmission is improved,the test results provide method and theoretical basis for avoiding the transmission case cracking.
Andrey Kolmogorov put forward in 1933 the five fundamental axioms of classical probability theory. The original idea in my complex probability paradigm is to add new imaginary dimensions to the experiment real dimensions which will make the work in the complex probability set totally predictable and with a probability permanently equal to one. Therefore, adding to the real set of probabilities $ {\sc{R}} $ the contributions of the imaginary set of probabilities $ \sc{M} $ will make the event in $ \sc{C}= \sc{R} + \sc{M} $ absolutely deterministic. It is of great importance that stochastic systems become totally predictable since we will be perfectly knowledgeable to foretell the outcome of all random events that occur in nature. Hence, my purpose here is to link my complex probability paradigm to Claude Shannon’s information theory that was originally proposed in 1948. Consequently, by calculating the parameters of the new prognostic model, we will be able to determine the magnitude of the chaotic factor, the degree of our knowledge, the complex probability, the self-information functions, the message entropies, and the channel capacities in the probability sets $ \sc{R} $ and $ \sc{{M}} $ and $ \sc{C} $ and which are all functions of the message real probability subject to chaos and random effects.
Control engineering systems. Automatic machinery (General), Systems engineering
A probabilistic methodology is proposed to evaluate fatigue damage accumulation and fatigue lives of specimens under variable amplitude loading. With probabilistic modifications in the present model, the calculative consistency is achieved between fatigue damage and fatigue life. The load sequence effects on fatigue damage accumulation are properly accounted for variable amplitude loading. The developed damage model overcomes the inherent deficiencies in the linear damage accumulation rule, but still preserves its simplicity for engineering application. Based on the Monte Carlo sampling method, numerical verification of this model is conducted under two kinds of spectrum loading. The predicted probabilistic distributions of fatigue lives are validated by fatigue tests on Al-alloy straight lugs.
Mechanical engineering and machinery, Structural engineering (General)
The unbalancing vibration monitoring and control of rotating machinery is an important engineering problem. In order to correct the unbalance of rotor system online, an active dynamic balancing head is designed based on the ratchet-pawl mechanis m and pneumat ic technology. The inner mass distribution state of balancing head can be changed to correct the unbalance when the rotor system is working in unbalancing condition. The mechanical p rinciple and pneumatic control system of the balancing head are introduced in th e paper. Based on a double-face online dynamic balancing experiment system, the balancing effect of the balancing head has been proved by many experiments. In order to improve the dynamic balancing accuracy of the balancing head in the pract ical application, the possible in fluencing factors of dynamic balancing accuracy are analyzed in the paper.