WEB-BASED VIRTUAL CNC MACHINE MODELING AND OPERATION

A CNC simulation system based on intemet for operation training of manufacturing facility and manufacturing process simulation is proposed. Firstly, the system framework and a rapid modeling method of CNC machine tool are studied under the virtual environment based on PolyTrans and CAD software. Then, a new method is proposed to enhance and expand the interactive ability of virtual reality modeling language（VRML） by attaining communication among VRML, JavaApplet, JavaScript and Html so as to realize the virtual operation for CNC machine tool. Moreover, the algorithm of material removed simulation based on VRML Z-map is presented. The advantages of this algorithm include less memory requirement and much higher computation. Lastly, the CNC milling machine is taken as an illustrative example for the prototype development in order to validate the feasibility of the proposed approach.

Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning

Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are generated,reflecting the interaction between the TBM system and surrounding rock,and these data can be used to evaluate the rock mass quality.This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data.Based on the Songhua River water conveyance project,a total of 7538 TBM tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing.Then,through the tree-based feature selection method,10 key TBM operation parameters are selected,and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers.The preprocessed data are randomly divided into the training set(90%)and test set(10%)using simple random sampling.Besides stacking ensemble classifier,seven individual classifiers are established as the comparison.These classifiers include support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),gradient boosting decision tree(GBDT),decision tree(DT),logistic regression(LR)and multilayer perceptron(MLP),where the hyper-parameters of each classifier are optimised using the grid search method.The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers,and it shows a more powerful learning and generalisation ability for small and imbalanced samples.Additionally,a relative balance training set is obtained by the synthetic minority oversampling technique(SMOTE),and the influence of sample imbalance on the prediction performance is discussed.

USE OF GENETIC ALGORITHMS TO SEQUENCE THE MACHINING OPERATIONS OF PARTS

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Soil moisture content as a predictor of soil disturbance caused by wheeled forest harvesting machines on soils of the Western Carpathians

Limiting surface soil disturbance caused by forest harvesting machines is an important task and is influenced by the selection of efficient and reliable predictors of such disturbance. Our objective was to determine whether soil moisture content affects soil load bearing capacity and the formation of ruts. Measurements were conducted in six forest stands where various machines operated. We measured the formation of ruts along skid trails in connection with varying soil moisture content. Soil moisture content was determined through the gravimetric sampling method. Our results showed that severe（rut depth16–25 cm） to very severe disturbance（rut depth [26 cm）occurred in forest stands where the instantaneous soil moisture exceeded its plasticity limits defined through Atterberg limits. Atterberg limits of soil plasticity ranged from 26 to 32 % in individual stands. Regression and correlation analysis confirmed a moderately strong relationship（R = 0.52; p ／ 0.05） between soil moisture content and average rut depth. This confirmed that soil moisture is a suitable and effective predictor of soil disturbance.

A MACHINE OPERATOR OR A DYNAMIC STIMULATOR:—MAKE YOUR VIDEO CLASS INTERACTIVE

An analysis of the different types of interaction taking place during a video-class shows thatcommunicative methods stimulate the students’ language learning.Thus video becomes a useful languagelearning tool.