Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding
operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation
of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or
tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study
is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning.
The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as
the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm
to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides
performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated
against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven
to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling
in this study, it is expected to be applicable to other tool wear modeling applications. 相似文献
Autonomous robots are complex systems that require the interaction or cooperation of numerous heterogeneous software components. Nowadays, robots are getting closer to humans and as such are becoming critical systems that must meet safety properties including logical, temporal, and real-time constraints. 相似文献
This paper presents theoretical and experimental investigations on electroosmotic control of stream width in hydrodynamic
focusing. In the experiments, three liquids (aqueous NaCl, aqueous glycerol and aqueous NaCl) are introduced by syringe pumps
to flow side by side in a straight rectangular microchannel. External electric fields are applied on the two aqueous NaCl
streams. Under the same inlet volumetric flow rates, the applied electric fields are varied to control the interface positions
and consequently the width of the focused aqueous glycerol stream. The electroosmotic effect on the width of the aqueous glycerol
is measured using fluorescence imaging technique. The electroosmotic effect under different flow rates, different viscosity,
and aspect ratio are investigated. The results indicate that the electroosmotic effect on the pressure-driven flow becomes
weaker with the increase in flow rates, viscosity ratio or aspect ratio of the channel. The measured results of the focused
width of the non-conducting fluid agree well with the analytical model. 相似文献
Membrane decorated with biocides is an effective way to suppress biofilm growth. However, their immediate biocidal effect usually suffers from a significant decline due to the irreversible consumption of the biocides. Here, a smart nanofiltration membrane is reported with rechargeable antibacterial capability that is fabricated by a facile interfacial polymerization via 3-aminophenylboronic acid and trimesoyl chloride on a polysulfone substrate. Biocides bearing diol groups can be grafted onto the membrane surface under neutral/alkaline condition and then released from the surface under acidic environment, due to the pH-responsive feature of boronate ester complexes. The resultant membrane exhibits integrated properties of fast bacterial inactivating efficiency, rechargeable antibacterial capability, and impressive stability. In addition, the achieved membrane shows remarkable separation efficiency to dye/monovalent salt system. The successful fabrication of the membrane with rechargeable anti-bacterial property provides new insights into the development of pH-responsive and sustainable antibacterial membranes. 相似文献
Development of multifunctional electrocatalysts with high efficiency and stability is of great interest in recent energy conversion technologies. Herein, a novel heteroelectrocatalyst of molecular iron complex (FeMC)-carbide MXene (Mo2TiC2Tx) uniformly embedded in a 3D graphene-based hierarchical network (GrH) is rationally designed. The coexistence of FeMC and MXene with their unique interactions triggers optimum electronic properties, rich multiple active sites, and favorite free adsorption energy for excellent trifunctional catalytic activities. Meanwhile, the highly porous GrH effectively promotes a multichannel architecture for charge transfer and gas/ion diffusion to improve stability. Therefore, the FeMC–MXene/GrH results in superb performances towards oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) in alkaline medium. The practical tests indicate that Zn/Al–air batteries derived from FeMC–MXene/GrH cathodic electrodes produce high power densities of 165.6 and 172.7 mW cm−2, respectively. Impressively, the liquid-state Zn–air battery delivers excellent cycling stability of over 1100 h. In addition, the alkaline water electrolyzer induces a low cell voltage of 1.55 V at 10 mA cm−2 and 1.86 V at 0.4 A cm−2 in 30 wt.% KOH at 80 °C, surpassing recent reports. The achievements suggest an exciting multifunctional electrocatalyst for electrochemical energy applications. 相似文献
In this work, real-time ultraviolet photodetectors are realized through metal–semiconductor–metal (MSM) structures. Amorphous indium gallium zinc oxide (a-IGZO) is used as semiconductor material and gold as metal electrodes. The readout of an individual sensor is implemented by a transimpedance amplifier (TIA) consisting of an all-enhancement a-IGZO thin-film transistor (TFT) operational amplifier and a switched capacitor (SC) as feedback resistance. The photosensor and the transimpedance amplifier are both manufactured on glass substrates. The measured photosensor possesses a high responsivity R , a low response time t R E S , and a good noise equivalent power value NEP . 相似文献
Artificial Life and Robotics - Honey bees (Apis mellifera L.) are social insects that makes frequent use of volatile pheromone signals to collectively navigate unpredictable and unknown... 相似文献
Engineering with Computers - Structural health monitoring (SHM) and Non-destructive Damage Identification (NDI) using responses of structures under dynamic excitation have an imperative role in the... 相似文献
Stochastic demand is an important factor that heavily affects production planning. It influences activities such as purchasing, manufacturing, and selling, and quick adaption is required. In production planning, for reasons such as reducing costs and obtaining supplier discounts, many decisions must be made in the initial stage when demand has not been realized. The effects of non-optimal decisions will propagate to later stages, which can lead to losses due to overstocks or out-of-stocks. To find the optimal solutions for the initial and later stage regarding demand realization, this study proposes a stochastic two-stage linear programming model for a multi-supplier, multi-material, and multi-product purchasing and production planning process. The objective function is the expected total cost after two stages, and the results include detailed plans for purchasing and production in each demand scenario. Small-scale problems are solved through a deterministic equivalent transformation technique. To solve the problems in the large scale, an algorithm combining metaheuristic and sample average approximation is suggested. This algorithm can be implemented in parallel to utilize the power of the solver. The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given, then the problems of the first and second stages can be decomposed. 相似文献
This study aims to propose a more efficient hybrid algorithm to achieve favorable control performance for uncertain nonlinear systems. The proposed algorithm comprises a dual function-link network-based multilayer wavelet fuzzy brain emotional controller and a sign(.) functional compensator. The proposed algorithm estimates the judgment and emotion of a brain that includes two fuzzy inference systems for the amygdala network and the prefrontal cortex network via using a dual-function-link network and three sub-structures. Three sub-structures are a dual-function-link network, an amygdala network, and a prefrontal cortex network. Particularly, the dual-function-link network is used to adjust the amygdala and orbitofrontal weights separately so that the proposed algorithm can efficiently reduce the tracking error, follow the reference signal well, and achieve good performance. A Lyapunov stability function is used to determine the adaptive laws, which are used to efficiently tune the system parameters online. Simulation and experimental studies for an antilock braking system and a magnetic levitation system are presented to verify the effectiveness and advantage of the proposed algorithm.