PROTOTYPE SURFACE MICRO- PRECISION IN FUSED DEPOSITION MODELING PROCESS

To aim at prototype parts fabricated with fused deposition modeling （FDM） process, the problems how to improve and enhance their surface micro-precision are studied. The producing mechanism of surface roughness is explained with three aspects concretely including the principle error of rapid prototyping （RP） process, the inherent characteristics of FDM process, and some mi- cro-scratches on the surface of the extruded fiber. Based on the micro-characters of section shape of the FDM prototype, a physical model reflecting the outer shape characters is abstracted. With the physical simplified and deduced, the evaluating equations of surface roughness are acquired. According to the FDM sample parts with special design for experimental measurement, the real surface roughness values of different inclined planes are obtained. And the measuring values of surface roughness are compared with the calculation values. Furthermore, the causes of surface roughness deviation between measuring values and calculation values are respectively analyzed and studied. With the references of analytic conclusions, the measuring values of the experimental part surface are revised, and the revised values nearly accord with the calculation values. Based on the influencing principles of FDM process parameters and special post processing of FDM prototype parts, some concrete measures are proposed to reduce the surface roughness of FDM parts, and the applying effects are better.

Effect of Surface Roughness in Micro-nano Scale on Slotted Waveguide Arrays in Ku-band

Modeling of the roughness in micro-nano scale and its influence have not been fully investigated, however the roughness will cause amplitude and phase errors of the radiating slot, and decrease the precision and efficiency of the SWA in Ku-band. Firstly, the roughness is simulated using the electromechanical coupled（EC） model. The relationship between roughness and the antenna＇s radiation properties is obtained. For verification, an antenna proto- type is manufactured and tested, and the simulation method is introduced. According to the prototype, a contrasting experiment dealing with the flatness of the radiating plane is conducted to test the simulation method. The advantage of the EC model is validated by comparisons of the EC model and two classical roughness models （sine wave and fractal function）, which shows that the EC model gives a more accurate description model for roughness, the maxi- mum error is 13%. The existence of roughness strongly broadens the beamwidth and raises the side-lobe level of SWA, which is 1.2 times greater than the ideal antenna. In addition, effect of the EC model＇s evaluation indices is investigated, the most affected scale of the roughness is found, which is 1/10 of the working wavelength. The proposed research provides the instruction for antenna designing and manufacturing.

Density-based rough set model for hesitant node clustering in overlapping community detection

Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.

Multi-factor influence of cross-sectional airflow distribution in roadway with rough roof

The wall surface roughness renders a significant impact on ventilation of roadways and cross-sectional wind speed distribution.Herein,the wall roughness(Ra)in the roadway has been defined theoretically.Moreover,three-center arched roadway models for different situations are established based on the normal distribution of roof roughness.The influence of inlet velocity,roof roughness and roadway height on wind speed distribution is systematically studied by using Fluent software.At Ra=0.1 m,the simulation results reveal that the wind speed is negatively related to the distance from the wall to the point where 80%of the central wind speed is reached(DA).Also,the wind speed distribution is significantly influenced by increasing the roof roughness.However,the wind speed distribution becomes asymmetric at Ra=0.2 m and 0.3 m.Furthermore,the low-speed area(v≤1 m/s)started to concentrate on the roof with the increase of roadway height.Overall,an Ra value of<0.1 m can reduce the influence of wall roughness on wind speed distribution of the roadway,which is suggested in practical applications.

Rough function model and rough membership function

Two pairs of approximation operators, which are the scale lower and upper approximations as well as the real line lower and upper approximations, are defined. Their properties and antithesis characteristics are analyzed. The rough function model is generalized based on rough set theory, and the scheme of rough function theory is made more distinct and complete. Therefore, the transformation of the real function analysis from real line to scale is achieved. A series of basic concepts in rough function model including rough numbers, rough intervals, and rough membership functions are defined in the new scheme of the rough function model. Operating properties of rough intervals similar to rough sets are obtained. The relationship of rough inclusion and rough equality of rough intervals is defined by two kinds of tools, known as the lower （upper） approximation operator in real numbers domain and rough membership functions. Their relative properties are analyzed and proved strictly, which provides necessary theoretical foundation and technical support for the further discussion of properties and practical application of the rough function model.

Optimization and perfection of the roughing model for ferritic SUS430

Based on the characteristics of ferritic SUS430 heating and deformation,and combined with the features of the 1780 mm hot-rolling mill,a roughing model was introduced in two aspects:optimizing the rough rolling passes and improving the width control precision.Through reducing the rough rolling passes,the rough rolling time can be shortened,the precision rolling startup temperature can be raised and the yield of the hot-rolled products can be increased.Moreover,on the premise that the slab width fluctuation was great,the precision of the width control can be improved through optimizing the parameters of the hot-rolling width control model.The result shows that the optimization and perfection of the original rolling process of the stainless steel 430 series further improved its capacity and product quality.

Granularity of Knowledge Computed by Genetic Algorithms Based on Rough Sets Theory

Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. Genetic Algorithms provides a general frame to optimize problem solution of complex system without depending on the domain of problem.It is robust to many kinds of problems.The paper combines Genetic Algorithms and rough sets theory to compute granular of knowledge through an example of information table. The combination enable us to compute granular of knowledge effectively.It is also useful for computer auto-computing and information processing.

Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis

Classification model has received great attention in any domain of research and also a reliable tool for medical disease diagnosis. The domain of classification model is used in disease diagnosis, disease prediction, bio informatics, crime prediction and so on. However, an efficient disease diagnosis model was compromised the disease prediction. In this paper, a Rough Set Rule-based Multitude Classifier (RS-RMC) is developed to improve the disease prediction rate and enhance the class accuracy of disease being diagnosed. The RS-RMC involves two steps. Initially, a Rough Set model is used for Feature Selection aiming at minimizing the execution time for obtaining the disease feature set. A Multitude Classifier model is presented in second step for detection of heart disease and for efficient classification. The Na?ve Bayes Classifier algorithm is designed for efficient identification of classes to measure the relationship between disease features and improving disease prediction rate. Experimental analysis shows that RS-RMC is used to reduce the execution time for extracting the disease feature with minimum false positive rate compared to the state-of-the-art works.

LS-SVM-based surface roughness prediction model for a reflective fiber optic sensor

Reflective fiber optic sensors have advantages for surface roughness measurements of some special workpieces,but their measuring precision and efficiency need to be improved further. A least-squares support vector machine（LS-SVM）-based surface roughness prediction model is proposed to estimate the surface roughness, Ra, and the coupled simulated annealing（CSA） and standard simplex（SS） methods are combined for the parameter optimization of the mode. Experiments are conducted to test the performance of the proposed model, and the results show that the range of average relative errors is-4.232%–2.5709%. In comparison with the existing models, the LS-SVM-based model has the best performance in prediction precision, stability, and timesaving.

Rough Heston Models with Variable Vol-of-Vol and Option Pricing

In this paper,a rough Heston model with variable volatility of volatility(vol-of-vol)is derived by modifying the generalized nonlinear Hawkes process and extending the scaling techniques.Then the nonlinear fractional Ric-cati equation for the characteristic function of the asset log-price is derived.The existence,uniqueness and regularity of the solution to the nonlinear fractional Riccati equation are proved and the equation is solved by the Adams methods.Finally the Fourier-cosine methods are combined with the Adams methods to price the options.

Micro ball-end milling of freeform titanium parts

Micro machining has growing number of applications in various industries such as biomedical, automotive, aerospace, micro-sensor, micro-actuator and jewelry industries. Small-sized freeform titanium parts are frequently needed in the biomedical applications, especially in the implantations such as mini-blood pumps and mini left-ventricular assist devices, finger joint replacements and small bone implants. Most of the small-sized titanium parts with freeform geometries are machined using micro ball-end milling before polishing and other surface treatments. Decreasing the cycle time of the machining parts is important for the productivity. In order to reduce the cycle time of the roughing process in the micro ball-end milling, this paper investigates the imple- mentation of a previously developed force-based feedrate scheduling （FFS） technique on micro milling of freeform titanium parts. After briefly introducing the instantaneous micro milling forces in micro ball-end milling of titanium parts with freeform surfaces, the FFS technique is implemented in the rough machining of a freeform titanium surface to demonstrate the cycle time reduction potentials via virtual micro milling simulations.

Text Classification Using Sentential Frequent Itemsets

Text classification techniques mostly rely on single term analysis of the document data set, while more concepts, especially the specific ones, are usually conveyed by set of terms. To achieve more accurate text classifier, more informative feature including frequent co-occurring words in the same sentence and their weights are particularly important in such scenarios. In this paper, we propose a novel approach using sentential frequent itemset, a concept comes from association rule mining, for text classification, which views a sentence rather than a document as a transaction, and uses a variable precision rough set based method to evaluate each sentential frequent itemset＇s contribution to the classification. Experiments over the Reuters and newsgroup corpus are carried out, which validate the practicability of the proposed system.