Improved nonconvex optimization model for low-rank matrix recovery

Low-rank matrix recovery is an important problem extensively studied in machine learning, data mining and computer vision communities. A novel method is proposed for low-rank matrix recovery, targeting at higher recovery accuracy and stronger theoretical guarantee. Specifically, the proposed method is based on a nonconvex optimization model, by solving the low-rank matrix which can be recovered from the noisy observation. To solve the model, an effective algorithm is derived by minimizing over the variables alternately. It is proved theoretically that this algorithm has stronger theoretical guarantee than the existing work. In natural image denoising experiments, the proposed method achieves lower recovery error than the two compared methods. The proposed low-rank matrix recovery method is also applied to solve two real-world problems, i.e., removing noise from verification code and removing watermark from images, in which the images recovered by the proposed method are less noisy than those of the two compared methods.

Improved Quality Prediction Model for Multistage Machining Process Based on Geometric Constraint Equation

Product variation reduction is critical to improve process efficiency and product quality, especially for multistage machining process（MMP）. However, due to the variation accumulation and propagation, it becomes quite difficult to predict and reduce product variation for MMP. While the method of statistical process control can be used to control product quality, it is used mainly to monitor the process change rather than to analyze the cause of product variation. In this paper, based on a differential description of the contact kinematics of locators and part surfaces, and the geometric constraints equation defined by the locating scheme, an improved analytical variation propagation model for MMP is presented. In which the influence of both locator position and machining error on part quality is considered while, in traditional model, it usually focuses on datum error and fixture error. Coordinate transformation theory is used to reflect the generation and transmission laws of error in the establishment of the model. The concept of deviation matrix is heavily applied to establish an explicit mapping between the geometric deviation of part and the process error sources. In each machining stage, the part deviation is formulized as three separated components corresponding to three different kinds of error sources, which can be further applied to fault identification and design optimization for complicated machining process. An example part for MMP is given out to validate the effectiveness of the methodology. The experiment results show that the model prediction and the actual measurement match well. This paper provides a method to predict part deviation under the influence of fixture error, datum error and machining error, and it enriches the way of quality prediction for MMP.

A New Method of Global Path Planning for AGV

Path planning is important in the research of a mobile robot （MR）. Methods for it have been used in different applications. An automated guided vehicle（ AGV） , which is a kind of MR, is used in a flexible manufacturing system （FMS）. Path planning for it is essential to improve the efficiency of FMS. A new method was proposed with known obstacle space FMS in this paper. FMS is described by the Augmented Pos Matrix of a Machine （ APMM ） and Relative Pos Matrix of Machines （ RPMM）, which is smaller. The optimum path can be obtained according to the probability of the path and the maximal probability path. The suggested algorithm of path planning was good performance through simulation result： simplicity, saving time and reliability.

Simultaneous optimization of multiple performance characteristics in WEDM for machining ZC63/SiC_p MMC

Abstract The compliance of an integrated approach, principal component analysis （PCA）, coupled with Tagu chi＇s robust theory for simultaneous optimization of cor related multiple responses of wire electrical discharge machining （WEDM） process for machining SiCp rein forced ZC63 metal matrix composites （MMCs） is investi gated in this work. The WEDM is proven better for its efficiency to machine MMCs among others, while the particulate size and volume percentage of SiCp with the composite are the utmost important factors. These improve the mechanical properties enormously, however reduce the machining performance. Hence the WEDM experiments are conducted by varying the particulate size, volume fraction, pulseon time, pulseoff time and wire tension. In the view of quality cut, the most important performance indicators of WEDM as surface roughness （Ra）, metal removal rate （MRR）, wire wear ratio （WWR）, kerf （Kw） and white layer thickness （WLT） are measured as respon ses. PCA is used as multiresponse optimization technique to derive the composite principal component （CPC） which acts as the overall quality index in the process. Consequently, Taguchi＇s S/N ratio analysis is applied to optimize the CPC. The derived optimal process responses are confirmed by the experimental validation tests results. The analysis of vari ance is conducted to find the effects of choosing process variables on the overall quality of the machined component.The practical possibility of the derived optimal process conditions is also presented using SEM.

Electrical Discharge Machining of Al2024-65 vol%SiC Composites

In the present work, the wire electrical discharge machining（WEDM） process of the 65 vol% SiCp/2024 Al composite prepared by pressure infiltration methods has been investigated. The microstructure of the machined composite was characterized by scanning electron microscope, the average surface roughness（Ra）, X-ray diffraction, X-ray photoelectron spectroscopy and transmission electron microscopy（TEM） techniques. Three zones from the surface to the interior（melting zone, heat affected zone and un-affected zone） were found in the machined composites, while the face of SiC particles on the surface toward the outside was ‘‘cut＇＇ to be flat. Increase in Al and Si but decrease in C and O were observed in the core areas of the removed particles. Si phase, which was generated due to the decomposition of SiC, was detected after the WEDM process. The irregular and spherical particles were further observed by TEM. Based on the microstructure observation, it is suggested that the machining mechanism of 65 vol% SiCp/2024 Al composite was the combination of the melting of Al matrix and the decomposition of SiC particles.