Search

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]

Different shaped K 0.3 WO 3 blue pigments were prepared by a simple solvothermal method. The as-synthesized K 0.3 WO 3 pigments were characterized by X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, transmission electron microscopy, and spectrophotometer. It's found that the concentration of WCl 6 had a great influence on the morphology of K 0.3 WO 3 pigments. Spindlelike K 0.3 WO 3 composed of nanorods, microsphere structures composed of nanorods and microflower structures composed of nanosheets could be obtained with an increasing WCl 6 concentration. The obtained spindle shaped K 0.3 WO 3 showed higher near infrared shielding ability than that of K 0.3 WO 3 microspheres and microflowers. The reason could be attributed to the enhanced local surface plasmon resonance absorption induced by higher aspect ratio and higher free carrier concentrations of spindleliked K 0.3 WO 3 . An approximately 8.5 °C decrease in inner temperature was obtained for the heat box covered with K 0.3 WO 3 coated glass. Therefore, the synthesized K 0.3 WO 3 blue pigments with high visible light transparency and high near infrared shielding ability have the potential to be applied in smart window coatings to reduce the heat build-up. [ABSTRACT FROM AUTHOR]

Thrust of track shoes on soft ground is affected by soil moisture content, shear rate and structure parameters of track shoes. A lack of comprehensive consideration of these factors exists for normal calculation methods. A method to predict thrust for track shoes on soft ground with splayed grouser was established based on experimental results and theoretical derivations. Model track shoe traction experiments were conducted for verification and correction of the thrust formula. It was observed that the thrust for the track shoes decreased with the increase in moisture content of the soil. Increases in shear rate, grouser height, and grouser splayed angle resulted in greater tractions. Effect of grouser thickness and grouser draft angle on tractions was not obvious. A corrected thrust formula allowed accurate prediction of thrust for a single track shoe on soft ground. [ABSTRACT FROM AUTHOR]

Working accuracy and service life of large structural parts such as lathe beds, columns, etc. of a heavy-duty machine tool with large self-weight is directly influenced by the foundation-soil system. Therefore, simulation of the interaction of the heavy-duty machine tool and its foundation-soil system is a key technological requirement from a manufacturing perspective. However, owing to the size of the structural parts of most heavy-duty machine tools, and the complex and oversized structure of their foundation-soil system, typical simulation environments fail to meet the computational requirements. With an aim to carry out simulations and meet the computational demand for the interaction of heavy-duty machine tools and their foundation-soil system; a constitutive model with a transversely isotropic composite material (the reinforced concrete foundation) was developed to reduce computation load. Then, based on fractal theory, equations for calculating stiffness of the joint surface and damping matrix were deduced to overcome common technical difficulty facing the heavy-duty machine tool industry. Meanwhile, establishing a finite element model (FEM) framework in a cloud-computing environment, rapid solution of the FE model for heavy-duty machine tool foundation-soil system interactions was realized. Finally, a comparison of experimental results can be used to validate accuracy of the analysis. Furthermore, cloud-computing simulation of a heavy-duty machine tool foundation-soil system was used to evaluate efficacy of different types of isolation trenches. The results provide a basis for the construction of the foundations of a heavy-duty machine tool and the selection of an optimisation scheme. [ABSTRACT FROM AUTHOR]

Machining accuracy retainability is considered to be one of the most important aspects in the process of performance evaluation and optimization design of the machine tools. Reliability based design optimization (RBDO) and robust design optimization (RDO) are the tools that search for safe structural systems with minimal variability of response when subjected to uncertainties in design parameters. The aim of this paper is to propose an approach that simultaneously considers reliability and robustness to allocate geometric accuracy of components for improving machining accuracy retainability under certain design requirements. Based on homogenous transformation matrices (HTMs), a comprehensive volumetric model explaining how individual errors in the components of a machine affect its volumetric accuracy (the coupling relationship) was established. By applying high-order moment standardization technique (HOMST), reliability and sensitivity with single failure mode were obtained and then the reliability model and the sensitivity model with multiple failure modes were developed and common methods such as Narrow bounds, AFOSM and Monte Carlo were used for verification and comparison. Meanwhile, a cost model taking manufacturing cost and present worth of expected quality loss into consideration was introduced. By taking the minimum possibility of failure and cost of machine tools as criterions and taking the reliability of machine tools as constraint of optimizing the basic parameters of machine tools, an approach to optimize the machining accuracy retainability of multi-axis NC machine tool based on robust design was developed and an example of improving the machining accuracy retainability for a five-axis NC machine tool was used to demonstrate the effectiveness of this approach. This study implies that it is possible to obtain the relationships between geometric errors and specify the accuracy grades of main feeding components of mechanical assemblies. [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]

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]

Grinding force is an important factor to consider in the field of precision manufacturing. In this study, a model of the thread grinding force in inner thread grinding that takes into account the thread helix angle and effect of grains overlapping is presented. The average undeformed chip thickness of a single abrasive grain can be obtained based on the Rayleigh probability density function. The contact length is assumed to be the arc contact length and is calculated based on the movement of an active grain. In addition, the effective grain overlap coefficient can be defined as the ratio of the effective area removed by a single grain to the cutting area without overlapping grains. The normal force is generated by material deformation and friction. Similarly, the tangential force is produced by material deformation and tangential friction. The normal and tangential forces can be calculated by considering a micro abrasive grain. Experiments were conducted to calculate the coefficients of the grinding force model in order to validate the model. Scanning electron microscopy was performed to determine the relationship between the normal force and the largest pit on the inner thread surface. Normal and tangential forces were analyzed under different spindle speeds, workpiece speeds, grinding depths, and thread helix angles. The numerical results suggest that grinding force is affected by the thread helix angle and friction forces; moreover, grain overlap cannot be ignored. Finally, a linear relationship between the normal force and the largest pit diameter was derived and used to determine the optimal normal force for generating improved inner thread surfaces. The proposed model provides a theoretical basis for the optimization of high-speed high-precision inner thread grinding. [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]

Purpose This paper aims to study the nonlinear supporting performance of hydrostatic ram under the impact of cutting force and search for an optimal solution to improve its stiffness.Design/methodology/approach The Reynolds equation was applied to resolve the carrying capability of a single oil pad numerically, and an iteration method was used to analyze the nonlinear supporting force and stiffness of a pair of oil pads placed face-to-face. The total offset of ram could be obtained after the displacement of aspectant oil pads was solved by the bisection method. From the comparison of the offset values of ram evaluated under different support conditions, the optimal solution was determined.Findings In this study, an optimized oil supply allocation, concluded as 1.16:0.84, is proposed to improve the performance of hydrostatic ram supporting structure.Originality/value The supporting performance of hydrostatic ram could be improved by appropriate allocation of oil supply without extra energy consumption. [ABSTRACT FROM AUTHOR]

Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. An experiment was conducted on a five-axis machine tool, and the results confirmed the proposed approach being able to display the accuracy design of the machine tool. [ABSTRACT FROM AUTHOR]

Hydrostatic systems are considered as essential supporting structures in heavy machine tools. The calculations and analyses for hydrostatic bearings are always laborious because of the involvement of several disciplines such as elastic mechanics, hydromechanics, thermodynamics, and other factors in the design. It is well known that large data and cloud technology are capable of processing and transmission of hydrostatic system studies. In this work, a cloud manufacture model is presented to provide big data storage, transmission, and processing platform for designers, manufacturers, and users of hydrostatic bearings. All participants involved in the manufacturing were linked together by timely information communication in order to ensure that the customized products met with the actual conditions in the cloud server. Based on the actual requirements of the user, the carrying capacity of hydrostatic system was analyzed by a designer using finite difference method and the results were sent to the manufacturer for machining of components. The monitored data from the consumer could be fed back to the designer for performance evaluation through the cloud server. It was found that customized manufacturing tasks could be finished more reliably and efficiently if all participants exchanged the data through cloud platform in real time. [ABSTRACT FROM AUTHOR]

The components of machine tools are mainly fixed and connected by bolts. The performance of the assembly can be affected by the dynamic characteristics of the bolted joints. This paper presents a nonlinear virtual material method based on surface contact stress to describe the bolted joint for accurate dynamic performance analysis of the bolted assembly. Fractal geometry theory is used to describe the surface topography. The elastic modulus and shear modulus of one micro-contact are derived based on fractal contact theory. The equivalent elastic modulus, Poisson ratio, and density of the bolted joint can be obtained through the weighted mean method. In order to obtain the stress distribution, the contact surface is assumed flat in the macro-scale, and the uneven distribution of contact stress can be obtained by the finite element method (FEM). The contact surface can be divided into several sections, and the parameters of a virtual material layer can be determined based on the mean contact stress. Both theoretical and experimental results for a bolted joint are obtained for a box-shaped specimen under equal pre-tightening force and bending moment effect. 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 4.41%, which indicates that the present nonlinear virtual material method is appropriate for the bolted joint in modeling CNC machine tools. [ABSTRACT FROM AUTHOR]

The heavy load mechanical spindle is an important functional component in a 5-axis computer numerical control (CNC) machine tool, which is used to process large and complex free-form surfaces. It is necessary to obtain the natural frequency and analyze the spindle stability for improving the machining precision. In this paper, Timoshenko beam theory is introduced to model the mechanical spindle shaft, where the centrifugal force and gyroscopic effects are considered. Stability of the heavy load mechanical spindle shaft is analyzed, and the buckling load of the spindle shaft is obtained under different rotational speeds. The natural frequency of spindle is investigated in a freedom and restraint state, respectively. Comparing the proposed method with the simplified hollow cylinder and shaft prototype in the freedom state, the results show that they are highly correlated with experimental results. For the restraint state, the axial load, rotational speed, gyroscopic effect, and centrifugal force are discussed, and all of these parameters affect the natural frequency. The proposed modeling approach can be used for spindle design and optimization in a given machining process and can be easily extended to other spindle design. [ABSTRACT FROM AUTHOR]

Computer-aided process planning is an important interface between computer-aided design and computer-aided manufacturing in computer-integrated manufacturing environments. In this paper, the complicated process planning is modeled as a combinatorial optimization problem with constraints, and a hybrid graph and genetic algorithm (GA) approach has been developed. The approach deals with process planning problems in a concurrent manner by simultaneously considering activities such as sequencing operations, selecting manufacturing resources, and determining setup plans to achieve the global optimal objective. Graph theory accompanied with matrix theory, as the basic mathematical tool for operation sequencing, is embedded into the main frame of GA. The precedence constraints between operations are formulated in an operation precedence graph (OPG). The initial population composed of all feasible solutions is generated by an elaborately designed topologic sort algorithm to the OPG. A modified crossover operator guaranteeing only feasible offspring generated is used, two types of mutation strategies are adopted, and a heuristic algorithm is applied to adjust the infeasible plan generated by the mutation operator to the feasible domain. A case study has been carried out to demonstrate the feasibility and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Bolted joints often have a significant effect on the dynamical behavior of assembled mechanical structures. An accurate model of an assembled structure depends on correctly determining and identifying the dynamic parameters of bolted joints. This paper presents an alternative method for identifying these dynamic parameters using structure's natural frequency and damping. A novel experiment is designed with a test piece consisting of only bolted joints, with the governing equations of the test piece established using the analytical method. The relationships between the equivalent dynamic parameters of the bolted joints and the natural frequencies and damping ratios of the test piece are determined for both the normal and tangential directions. The parameter identification problem for bolted joints is thus transformed into a test of the natural frequency and the damping ratio of the test piece. In order to check the accuracy of the proposed identification method, the test piece and bolted joints are modeled using the finite element method (FEM) and the dynamic properties of the test piece are analyzed. The maximum error between the natural frequencies of the FEM result and the experimental values in the normal and tangential models are 4.73% and 0.34%, respectively. The result indicates that the proposed method is valid for the dynamic parameter identification of bolted joints. [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]

Ag nanoparticles were uniformly attached to the surface of K 0 · 3 WO 3 nanorods by the chemical reduction method. The photocatalytic performance and NIR shielding property of the nanocomposites could be regulated by changing the mass ratio of Ag to K 0 · 3 WO 3. The NIR shielding ability of the nanocomposites was as high as 49.8%. The prepared K 0 · 3 WO 3 /Ag nanocomposites exhibited improved visible photocatalytic performance, resulting in an enhanced self-cleaning property of K 0 · 3 WO 3 /Ag films. The improvement in visible photocatalytic activity of the K 0 · 3 WO 3 /Ag nanocomposites could be attributed to the improved separation efficiency of photo-generated carriers. Furthermore, K 0 · 3 WO 3 /Ag coated glass can reduce the interior temperature of the heat box by 2.4 °C. Therefore, the integration of good visible light transmittance, superior visible photocatalytic self-cleaning property, and strong NIR shielding performance make K 0 · 3 WO 3 /Ag nanocomposites good candidates for smart window coatings. Image 1 • K 0 · 3 WO 3 /Ag nanocomposites were prepared. • The nanocomposites exhibit good visible light transmittance. • The nanocomposites can shield nearly 50% of near-infrared light. • The nanocomposite coatings show superior thermal insulation performance. • The nanocomposite coatings exhibit enhanced self-cleaning property. [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]

Preload relaxation, caused by elastic interaction between tightened bolts, is one of the major reasons for non-uniformity of pretension force. But how to accurately assess the residual tension of a bolt is still extremely difficult. This paper considers two types of multiple bolted joint and six kinds of tightening sequence. Based on the deformation compatibility equation and elastic interaction stiffness, the generalized mathematical model for evaluating residual preload of bolts is established. Certain model parameters are obtained through numerical simulation. To analyze the influence of initial tightening torque, spacing between bolt holes, bolt material and gasket material on the elastic interaction stiffness, experimental devices and test procedures are developed. Furtherly, preload relaxation models are validated by tests conducted on six types of multi-bolted joint with the same surface roughness. Results of the proposed mathematical model of pretension force show a good consistency with those of corresponding experiment. • A mathematical model for estimating preload relaxation of bolted joint subjected to elastic interaction was established. • The elastic interaction stiffness between bolts was analyzed based on developed Experimental devices and test procedures. • The proposed mathematical model of pretension force shows a good consistency with the corresponding experiment. [ABSTRACT FROM AUTHOR]

Abstract Reliable operating conditions of hydrostatic turntables are prerequisite to ensuring machine tool performance. The hydrostatic turntable is affected by multiple working conditions, therefore, methods for evaluating turntable load-carrying capacity has become research hotspot. In this paper, by analyzing bearing capacity, parameters closely related to the support performance of turntables are selected as recognition features and an artificial neural network (ANN) training method is proposed. The ANN method is based on numerical solutions of over-determined nonlinear equations (ODNE) to intelligently evaluate turntable performance. In this study, ANN and ODNE training are applied to evaluate the performance of hydrostatic turntables. Finally, to verify the feasibility of the method, an intelligent monitoring system is established to collect data on machine tools. Highlights • Factors influencing the performance of hydrostatic turntable were determined by analyzing the carrying ability of oil pads. • Based on the ANN structure for fast calculation of optimal weights, an ODNE training method was presented. • For intelligent monitoring of a hydrostatic turntable, BP and ODNE training are both recommended for training the ANN. [ABSTRACT FROM AUTHOR]

Machining accuracy is critical for the quality and performance of a mechanical product, and the reliability of a multi-axis NC machine tool reflects the ability to reach and maintain the required machining accuracy. The objective of this study is to propose a general methodology that will simultaneously consider geometric errors and thermal-induced errors to allocate the geometric accuracy of components, for improving machining accuracy reliability under certain design requirements. The multi-body system (MBS) theory was applied to develop a comprehensive volumetric error model, showing the coupling relationship between the individual errors of the components of this machine tool and their volumetric accuracy. Additionally, a thermal error model was established based on the neural fuzzy control theory and was compared to the common thermal error modeling method called BP neural network. Based on the traditional cost model and the reliability analysis model, a geometric error-cost model and a geometric error-reliability model were established, taking the weighted function principle into consideration. Then, an allocation approach of the geometric errors, for optimizing total cost (manufacture and QLF) and reliability, subject to the geometrical and operational constraints of the machine tool, was proposed and formulated into a mathematical model, in order to perform the optimization process of accuracy allocation by using the advanced NSGA-II algorithm. A case study was also performed in a five-axis machine tool, and the traditional NSGA algorithm was used for comparison. The optimization results for the five-axis machining center showed that the proposed approach is effective and able to perform the optimization of geometric accuracy and improve the machining accuracy and the reliability of the machine tool. [ABSTRACT FROM AUTHOR]

High stability and movement compatibility under friction is vital to guarantee long-term use of implants in the human body. In this study, the negligible wear of the poly (vinylphosphonic acid) (PVPA)-modified Ti6Al4V/polytetrafluoroethylene (PTFE) interface in phosphate-buffered saline (PBS, PH = 7.2) were confirmed. The depth of scratches on the PVPA-modified Ti6Al4V was no more than 20 nm, while there was slight wear on the PTFE ball, as indicated by the radius of curvature being almost the same as the initial value after sliding for 2 h. In addition, it should be noted that the novel velocity and load-independent tribological behaviors are exciting. The superlubricity of the PVPA-modified Ti6Al4V can be maintained over a wide velocity range, from 0.3 mm/s to 48 mm/s, even under a high pressure of 63.49 MPa, confirming its potential application in implants. The sufficient thickness of the firm PVPA coatings with specific lubricating state and the compatibility of the two tribo-pairs in this tribological system primarily account for the novel tribological stability. This study provides insights into the tribological mechanisms of the high-stability polymer coatings. [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]

Geometric errors directly affect the tool tip position, reduce machining accuracy, and are one of the most important errors of multi-axis machining tool. However, the geometric errors are intercoupling, and the measured values at different points vary and are stochastic. The identification of the most crucial geometric errors and the determination of a method to control them is a key problem to improve the machining accuracy of machine tool. To achieve this goal, a new analytical method, to identify crucial geometric errors for a multi-axis machine tool is proposed here based on multibody system (MBS) theory and global sensitivity analysis. The volumetric error modeling of multi-axis machine tool has been given by MBS theory, which describes the topological structure of multibody system simply and conveniently in a matrix. The stochastic characteristic of geometric errors is taken into consideration and Sobol global sensitivity analysis method is introduced to identify crucial geometric errors of machine tool. A vertical machining center is selected as an illustration example. The analysis results reveal that the analytical method presented in this paper can identify the crucial geometric errors and are helpful to improve the machining accuracy of multi-axis machine tool. [ABSTRACT FROM AUTHOR]