Search

Instead improving the accuracy of machine tool by increasing the precision of key components level blindly in the production process, the method of combination of SNR quality loss function and machine tool geometric error correlation analysis to optimize five-axis machine tool geometric errors will be adopted. Firstly, the homogeneous transformation matrix method will be used to build five-axis machine tool geometric error modeling. Secondly, the SNR quality loss function will be used for cost modeling. And then, machine tool accuracy optimal objective function will be established based on the correlation analysis. Finally, ISIGHT combined with MATLAB will be applied to optimize each error. The results show that this method is reasonable and appropriate to relax the range of tolerance values, so as to reduce the manufacturing cost of machine tools.

Thermal extension error of boring bar in z-axis is one of the key factors that have a bad influence on the machining accuracy of boring machine, so how to exactly establish the relationship between the thermal extension length and temperature and predict the changing rule of thermal error are the premise of thermal extension error compensation. In this paper, a prediction method of thermal extension length of boring bar in boring machine is proposed based on principal component analysis (PCA) and least squares support vector machine (LS-SVM) model. In order to avoid the multiple correlation and coupling among the great amount temperature input variables, firstly, PCA is introduced to extract the principal components of temperature data samples. Then, LS-SVM is used to predict the changing tendency of the thermally induced thermal extension error of boring bar. Finally, experiments are conducted on a boring machine, the application results show that Boring bar axial thermal elongation error residual value dropped below 5 μm and minimum residual error is only 0.5 μm. This method not only effectively improve the efficiency of the temperature data acquisition and analysis, and improve the modeling accuracy and robustness.

Copyright of Transactions of Nanjing University of Aeronautics & Astronautics is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

S -shape acceleration and deceleration are the most widely used flexible acceleration and deceleration method in the current CNC system, but its velocity solution equation contains irrational terms, which create a more complicated solution process. When analyzing the solution process of the S -shape acceleration and deceleration directly, using a traditional numerical solution method, the phenomenon of "solving the interval jump" arises, which is the main reason for low efficiency and poor stability of the solution. According to the S -curve profile and solution, the concept of separating the curve profile recognition from the velocity solution was proposed, and a method of quickly identifying the interval of the solution location was introduced. Through the method mentioned above, the complete acceleration and deceleration curve parameters can be obtained through a one-time plan and a one-time solution, and the solution efficiency and stability are guaranteed; solving the Newton problem depends too much on the initial value of Newton velocity, which not only retains the speed advantage of the Newton method but also uses the downhill factor to ensure its convergence. Through the simulation comparison and analysis, the efficiency, stability, and universality of the method are verified. [ABSTRACT FROM AUTHOR]

Cancer therapy that utilizes oncolytic virus may offer an exciting alternative, and coxsackievirus B3 (CVB3) is a potent oncolytic virus. This study was to assess the oncolytic activities of novel recombinant CVB3 with genetically inserted basic peptides in lung cancer. Recombinant CVB3 was produced in Vero cells, with or without genetically inserted basic peptides. In vitro and in vivo experiments with nude mouse models bearing human lung carcinoma xenografts were performed to examine the antitumor activities. Cytokines and immune responses to the recombinant CVB3 were determined in cynomolgus monkeys. Recombinant CVB3 with genetically inserted basic peptides was associated with significantly higher pH values within tumors. Mice treated with recombinant CVB3 showed significantly less tumor progression, and recombinant CVB3 with genetically inserted basic peptides appeared to enhance tumor suppression. Recombinant CVB3 was associated with significantly less proliferation of various lung cancer cells without affecting proliferation of normal lung fibroblasts. The cytokine profiles of the cynomolgus monkeys were comparable among control group (normal saline solution) and those given recombinant CVB3 with or without fused basic peptides, with no induction of excessive cytokine or immune responses. In conclusions, recombinant CVB3, especially those with fused basic peptides, possess strong antitumor activities without eliciting excessive immune responses. [ABSTRACT FROM AUTHOR]

The goal of this study was to recognize human standing postures in human-robot collaborations such that the robot can serve the human operator better. An intelligent sensing floor was developed based on a thin-film pressure sensor and a human standing posture dataset was obtained by transforming the pressure data into a pressure image. A human standing posture recognition method based on an improved convolutional neural network is proposed. The results of the experiments demonstrate that a convolutional neural network can be used in the field of pressure images. The proposed method returned a recognition rate of 96.6%. Compared to the traditional neural network, the improved convolutional neural network model has better performance. The study results are expected to be used in standing posture monitoring to provide additional data for a robot in a human-robot collaboration system. [ABSTRACT FROM AUTHOR]

The interference of the complex background and less information of the small targets are two major problems in vehicle attribute recognition. In this paper, two cascaded networks of vehicle attribute recognition are established to solve the two problems. For vehicle targets with normal size, the multitask cascaded convolution neural network MC-CNN-NT uses the improved Faster R-CNN as the location subnetwork. The vehicle targets in the complex background are extracted by the location subnetwork to the classification subnetwork CNN for the classification. The implementation of this task decomposition strategy effectively eliminates the interference of the complex background in target detection. For vehicle targets with small size, the multitask cascaded convolution neural network MC-CNN-ST applies the network compression strategy and the multilayer feature fusion strategy to extract the feature maps. These strategies enrich the location information and semantic information of the feature maps. In order to optimize the nonlinear mapping ability and the hard-to-detect samples mining ability of the networks, the activation function and the loss function in the two cascaded networks are improved. The experimental results show that MC-CNN-NT for the normal targets and MC-CNN-ST for the small targets achieve the state-of-the-art performance compared with other attribute recognition networks. [ABSTRACT FROM AUTHOR]

The motion accuracy of hydrostatic turntable is the key in improving the machining accuracy of heavy-duty machine tool. However, the motion accuracy of hydrostatic turntable depends not only on the offset load but also on the rotating speed of the turntable as well as the profile errors of the guide rails. In this paper, a simulation model is developed to analyze the effect of guide rail profile errors on the motion accuracy of hydrostatic turntable. The reaction forces of preload thrust bearing and hydrostatic circular oil pads are obtained based on the Reynolds equation of the lubricant film. The motion equations of hydrostatic turntable are derived in which the profile errors of two guide rails are considered. The results show that the motion accuracy of hydrostatic turntable can be affected by wavelength, amplitude of profile errors and speed, and offset load of turntable. Finally, the motion accuracy of heavy-duty hydrostatic turntable used in XCKA28105 type turning and milling composite machine tool is obtained by using the presented method. Comparing with the experimental results, the proposed model can be used to predict the machining accuracy caused by the profile errors of guide rails for any heavy-duty hydrostatic turntable. [ABSTRACT FROM AUTHOR]

Storage reliability is an important index of ammunition product quality. It is the core guarantee for the safe use of ammunition and the completion of tasks. In this paper, we develop a prediction model of ammunition storage reliability in the natural storage state where the main affecting factors of ammunition reliability include temperature, humidity, and storage period. A new improved algorithm based on three-stage ant colony optimization (IACO) and BP neural network algorithm is proposed to predict ammunition failure numbers. The reliability of ammunition storage is obtained indirectly by failure numbers. The improved three-stage pheromone updating strategies solve two problems of ant colony algorithm: local minimum and slow convergence. Aiming at the incompleteness of field data, "zero failure" data pretreatment, "inverted hanging" data pretreatment, normalization of data, and small sample data augmentation are carried out. A homogenization sampling method is proposed to extract training and testing samples. Experimental results show that IACO-BP algorithm has better accuracy and stability in ammunition storage reliability prediction than BP network, PSO-BP, and ACO-BP algorithm. [ABSTRACT FROM AUTHOR]

The thermal contact conductance is an important problem in the field of heat transfer. In this research, a three-dimensional fractal theory based on the thermal contact conductance model is presented. The topography of the contact surfaces was fractal featured and determined by fractal parameters. The asperities in the microscale were considered as elastic, elastic-plastic, or plastic deformations. The real contact area of the asperities could be obtained based on the Hertz contact theory. It was assumed that the rough contact surface was composed of numerous discrete and parallel microcontact cylinders. Consequently, the thermal contact conductance of the surface roughness was composed of the thermal constriction conductance of microcontacts and the air medium thermal conductance of microgaps. The thermal contact conductance of rough surfaces could be calculated by the microasperities integration. An experimental set-up with annular interface was designed to verify the presented thermal contact conductance model. Three materials were used for the thermal contact conductance analysis with different fractal dimensions D and fractal roughness parameters G. The numerical results demonstrated that the thermal contact conductance could be affected by the elastic-plastic deformation of the asperities and the gap thermal conductance should not be ignored under the lower contact load. The presented model would provide a theoretical basis for thermal transfer engineering application. [ABSTRACT FROM AUTHOR]

Due to the influence of centrifugal force, accurate contact stiffness model of spindle–toolholder joint at high speeds is crucial in predicting the dynamic behavior and chatter vibration of spindle–toolholder system. In this paper, a macro–micro scale hybrid model is presented to obtain the contact stiffness of spindle–toolholder joint in high speeds. The hybrid model refers to the finite element model in macro-scale and three-dimensional fractal model in micro-scale. The taper contact surface of spindle–toolholder joint is assumed flat in macro-scale and the finite element method is used to obtain the pressure distribution at different speeds. In micro-scale, the topography of contact surfaces is fractal featured and determined by fractal parameters. Asperities in micro-scale are considered as elastic and plastic deformation. Then, the contact ratio, radial and torsional contact stiffness of spindle–toolholder joint can be calculated by integrating the micro asperities. Experiments with BT40 type toolholder–spindle assembly are conducted to verify the proposed model in the case of no speed. The reasonable intervals of spindle speed and drawbar force can be obtained based on the presented hybrid model, which will provide theoretical basis for the application and optimization of the spindle–toolholder system. [ABSTRACT FROM AUTHOR]

The machine tools are consisted of many parts and most of them are connected by the bolts. Accurate modeling of contact stiffness and damping for bolted joint is crucial in predicting the dynamic performance of machine tools. This paper presents a modified three-dimensional fractal contact model to obtain the stiffness and damping of bolted joint. Topography of the contact surface of bolted joint is fractal featured and determined by fractal parameters. Asperities in microscale are considered as elastic, elastic–plastic, and full plastic deformation. The expand coefficient ψ is introduced to the size-distribution function of asperities. The real contact area, contact stiffness, and damping of the contact surface can be calculated by integrating the microasperities. The relationship of contact stiffness, damping, fractal dimension D, and fractal roughness parameter G can be obtained. Experiments are conducted to verify the efficiency of the proposed model. The results show that the theoretical mode shapes are in good agreement with the experimental mode shapes. The relative errors between the theoretical and experimental natural frequencies are less than 3.33%, which is less than those of the W-K model and L-L model. The presented model can be used to accurately predict the dynamic characteristic of bolted assembly in the machine tools. [ABSTRACT FROM AUTHOR]

The stability of the tribological properties of polymer coatings is vital to ensure their long term use. The superlubricity of the poly(vinylphosphonic acid) (PVPA)-modified Ti6Al4V/polytetrafluoroethylene (PTFE) interface can be obtained when lubricated by phosphate-buffered saline (PBS, pH = 7.2), but not when lubricated by deionized water and ethanol. Therefore, the mechanisms for the superlubricity of PVPA coatings affected by lubricant were investigated in detail. The stability of the PVPA coatings and their compatibility with the lubricant are critical factors in realizing ideal tribological properties of PVPA coatings. Robust PVPA coatings are stable under a wide range of pH values (6-10) using PBS as the basic solution, and are also characterized by superlubricity. The hydrolysis kinetics of phosphate anhydride is the main reason for the pH responses. In addition, along with stability, PVPA coatings exhibit different friction coefficients in salt solutions which are composed of various ions, which indicates that the compatibility between PVPA coatings and the lubricant can be used to regulate the superlubricity properties. Based on a fundamental understanding of the mechanism of surperlubricity by considering the effects of the lubricant, PVPA coatings with stability and perfect tribological performance are expected to be applied in more aspects. [ABSTRACT FROM AUTHOR]

Accurate modeling of contact stiffness is crucial in predicting the dynamic behavior and chatter vibration of spindle–toolholder system for high-speed machining centers. This paper presents a fractal theory-based contact model of spindle–toolholder joint to obtain the contact stiffness and its real contact area. Topography of the contact surfaces of spindle–toolholder joint is fractal featured and determined by fractal parameters. Asperities in micro-scale are considered as elastic or plastic deformation. Then, the contact stiffness, the real contact area, the elastic contact force, and the plastic contact force of the whole contact surface are calculated by integrating the micro asperities. The relationship of the contact stiffness and the drawbar force follows a power law, in which the power index is determined by the fractal parameters. Experiments are conducted to verify the efficiency of the proposed model. The results from the fractal contact model of spindle–toolholder joint have good agreement with those of experiments. [ABSTRACT FROM AUTHOR]

Geometric error has significant influence on the processing results and reduces machining accuracy. Machine tool geometric errors can be interpreted as a deterministic value with an uncertain fluctuation of probabilistic distribution. Although, the uncertain fluctuation can not be compensated, it has extremely profound significance on the precision and ultra-precision machining to reduce the fluctuation range of machining accuracy as far as possible. In this paper, a typical 3-axis machine tool with high precision is selected and the fluctuations in machining accuracy are studied. The volumetric error modeling of machine tool is established by multi-body system (MBS) theory, which describes the topological structure of MBS in a simple and convenient matrix form. Based on the volumetric error model, the equivalent components of the errors for the three axes are established by reducing error terms. Then, the fluctuations of equivalent errors and the machining accuracy in working planes are depicted and predicted using the theory of stochastic process, whose range should be controlled within a certain confidence interval. Furthermore, the critical geometric errors that have significant influence on the machining accuracy fluctuation are identified. Based on the analysis results, some improvement in the machine tool parts introduced and the results for the modified machine show that the prediction allow for reduction in errors for the precision and ultra-precision machining. [ABSTRACT FROM AUTHOR]

Machining accuracy of a machine tool is influenced by geometric errors produced by each part and component. Different errors have varying influence on the machining accuracy of a tool. The aim of this study is to optimize errors to get a desired performance for a numerical control machine tool. Applying multi-body system theory, a volumetric error model was constructed to track and compensate effects of errors during operation of the machine, and to relate the functional specifications on volumetric accuracy to the permissible errors on the joints and links of the machine. Error sensitivity analysis was used to identify the influence of different errors (especially the errors which have large influences) on volumetric error. Based on First Order and Second Moment theory, an error allocation approach was developed to optimize allocation of manufacturing and assembly tolerances along with specifying the operating conditions to determine the optimal level of these errors so that the cost of controlling them and the cost of failure to meet the specifications is minimized. The approach developed was implemented in software and an example of the geometric errors budgeting for a five-axis machine was discussed. It is identified that the different optimal standard deviations reflect the cost-weighted influences of the respective parameters in the equations of the functional requirements. This study suggests that it is possible to determine the coupling relationships between these errors and optimize the allowable error budgeting between these sources. [ABSTRACT FROM AUTHOR]

Robust design (RD) is an important method because it can reduce the variation of products or processes and improve their performances such as static and dynamic quality characteristics at a low cost. In order to obtain an efficient and universal RD solution, a novel analytical RD methodology is proposed based on axiomatic design principles. The covariance matrix of multiple functional requirements (FRs) that can reflect dependency relationship is obtained via the Talyor series expansion. Evaluation mechanism based on information axiom is introduced to select the better design scheme gained with different robust optimization models. The distinct characteristics of the proposed method are the implementation by matrix differential, and the ability that can deal with multiple FR optimization simultaneously. An RD optimization of a five roller coating head is illustrated. The results show that the proposed method can optimize the design parameters which meet the multiple FRs. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Complex nonlinear and nonstationary signals can be adaptively analyzed by the Hilbert–Huang transform through empirical mode decomposition and the Hilbert transform to generate the instantaneous energy. The instantaneous energy was able to display the local characteristics of the signals and had good time–frequency analysis capability, it is therefore widely applied to the analysis of vibration signals in the field of gear fault diagnosis. However, only a few extracted intrinsic mode functions through empirical mode decomposition can reflect fault feature or closely related to the faults but others are irrelevant. Therefore, the fault feature of the instantaneous energy for all intrinsic mode functions was not obvious and the accuracy of diagnosis was low. Aimed at solving this problem, a fault leading rate evaluation algorithm was proposed that can select those intrinsic mode functions, which reflect fault features (it was called the dominant intrinsic mode function) from all intrinsic mode functions. In the paper, this algorithm was applied to gear fault feature extraction. By calculating the instantaneous energy of the dominant intrinsic mode function the method could accurately extract gear fault feature and improve the accuracy of diagnosis. Both simulated signals and experimental signals of a Klingelnberg bevel gear were analyzed to verify the effectiveness and correctness of the algorithm. [ABSTRACT FROM AUTHOR]

An active tendon, consisting of a displacement actuator and a co-located force sensor, has been adopted by many studies to suppress the vibration of large space flexible structures. The damping, provided by the force feedback control algorithm in these studies, is small and can increase, especially for tendons with low axial stiffness. This study introduces an improved force feedback algorithm, which is based on the idea of velocity feedback. The algorithm provides a large damping ratio for space flexible structures and does not require a structure model. The effectiveness of the algorithm is demonstrated on a structure similar to JPL-MPI. The results show that large damping can be achieved for the vibration control of large space structures. [ABSTRACT FROM AUTHOR]

A least square method based on data fitting is proposed to construct a new lifting wavelet, together with the nonlinear idea and redundant algorithm, the adaptive redundant lifting transform based on fitting is firstly stated in this paper. By variable combination selections of basis function, sample number and dimension of basis function, a total of nine wavelets with different characteristics are constructed, which are respectively adopted to perform redundant lifting wavelet transforms on low-frequency approximate signals at each layer. Then the normalized lP norms of the new node-signal obtained through decomposition are calculated to adaptively determine the optimal wavelet for the decomposed approximate signal. Next, the original signal is taken for subsection power spectrum analysis to choose the node-signal for single branch reconstruction and demodulation. Experiment signals and engineering signals are respectively used to verify the above method and the results show that bearing faults can be diagnosed more effectively by the method presented here than by both spectrum analysis and demodulation analysis. Meanwhile, compared with the symmetrical wavelets constructed with Lagrange interpolation algorithm, the asymmetrical wavelets constructed based on data fitting are more suitable in feature extraction of fault signal of roller bearings. [ABSTRACT FROM AUTHOR]

During human–robot collaborations (HRC), robot systems must accurately perceive the actions and intentions of humans. The present study proposes the classification of standing postures from standing-pressure images, by which a robot system can predict the intended actions of human workers in an HRC environment. To this end, it explores deep learning based on standing-posture recognition and a multi-recognition algorithm fusion method for HRC. To acquire the pressure-distribution data, ten experimental participants stood on a pressure-sensing floor embedded with thin-film pressure sensors. The pressure data of nine standing postures were obtained from each participant. The human standing postures were discriminated by seven classification algorithms. The results of the best three algorithms were fused using the Dempster–Shafer evidence theory to improve the accuracy and robustness. In a cross-validation test, the best method achieved an average accuracy of 99.96%. The convolutional neural network classifier and data-fusion algorithm can feasibly classify the standing postures of human workers. [ABSTRACT FROM AUTHOR]

Abstract Series of novel K 0.3 WO 3 /Ag 2 O nanocomposites were successfully prepared by simple two steps method. An exciting fact that near-infrared light shielding and visible photocatalytic self-cleaning effects are integrated into the K 0.3 WO 3 /Ag 2 O film (weight ratio of K 0.3 WO 3 to Ag 2 O = 30) could offer significant potential applications in smart windows. Furthermore, the optical properties and photocatalytic activity of the composites could be tuned by adjusting the weight ratio of K 0.3 WO 3 to Ag 2 O. The obtained K 0.3 WO 3 /Ag 2 O films presented enhanced photocatalytic self-cleaning performance compared with pure K 0.3 WO 3 under visible light irradiation. The improved self-cleaning performance of the K 0.3 WO 3 /Ag 2 O composites was ascribed to a combined contribution of the matched energy band structures and boosted separation efficiency of photo-generated carriers. An approximately 2.3°C-3.4 °C decrease in inner temperature was obtained for the heat box covered with K 0.3 WO 3 /Ag 2 O coated glass. Therefore, the proposed K 0.3 WO 3 /Ag 2 O (weight ratio of K 0.3 WO 3 to Ag 2 O = 30) smart coating is a promising material for self-cleaning energy-saving windows. Graphical abstract Image 1 Highlights • K 0.3 WO 3 /Ag 2 O nanocomposites were prepared by simple two steps method. • The nanocomposites show nice visible light transmittance. • The nanocomposites show strong NIR shielding performance. • The nanocomposite coatings showed good thermal insulation property. • The nanocomposite coatings enhanced self-cleaning property. [ABSTRACT FROM AUTHOR]

A nonlinear redundant lifting wavelet packet algorithm was put forward in this study. For the node signals to be decomposed in different layers, predicting operators and updating operators with different orders of vanishing moments were chosen to take norm l(p) of the scale coefficient and wavelet coefficient acquired from decomposition, the predicting operator and updating operator corresponding to the minimal norm value were used as the optimal operators to match the information characteristics of a node. With the problems of frequency alias and band interlacing in the analysis of redundant lifting wavelet packet being investigated, an improved algorithm for decomposition and node single-branch reconstruction was put forward. The normalized energy of the bottommost decomposition node coefficient was calculated, and the node signals with the maximal energy were extracted for demodulation. The roller bearing faults were detected successfully with the improved analysis on nonlinear redundant lifting wavelet packet being applied to the fault diagnosis of the roller bearings of the finishing mills in a plant. This application proved the validity and practicality of this method.