A study of using back-propagation neural model in automatic lubrication installation for the feeding system of computer numerical control machine tool

Nowadays, the feeding systems of computer numerical control machine tools are lubricated by periodic oil supply or fixed stroke, the lubrication is insufficient in the case of high load and high-speed movement, and the lubrication is excessive during finishing and low feed rate. This study discusses the optimum lubrication timing of the feeding system. When the feeding system is moving, the servomotor torque value and current, accuracy, and oil film thickness are measured by sensors. Moreover, the lubrication characteristic model is validated and built by using the sensor values, and the optimal lubrication state estimation is obtained by using the back-propagation neural model. Then analyzed and feedback to the machine tool controller, to intelligent the lubrication system. According to the test, when the feed rate is increased by 5 m/min, the friction coefficient increases with rate, increasing the output of the frictional value of the work table by 6.90%. When the load is increased by 175 kg, the friction coefficient decreases with load, reducing the frictional value output of table movement by 6.71%. In the relationship between oil film thickness and current, the accuracy difference between the prediction and actual test results is less than 10%; in the case of the same accuracy, the oil supply frequency is reduced by 80%, and environmentally friendly machine tool has been achieved.

Variation of the entropy analysis method in information flows of IBS in the concept of safe navigation development

The creation of a configuration by a bridge integrated system in the form of the control machine variation - artificial intelligence, becomes relevant in the concept of E-navigation development with the vector of the vessel’s directional control on the course, both in a small crew and unmanned way. It is possible to consider a large number of mathematical ideas about the basis for creating an intelligent control machine: an artificial neuron and fuzzy sets, Petri nets and Markov chains, Kolmogorov’s differential equations, which are difficult to configure and cumbersome to compute. Multiagent systems seem to be feasible. Integrated bridge systems with embedded supplementary reality assist management personnel in solving navigation issues. Saturation of the navigation control information complex of the bridge system with an increasing number of hardware and instrument units leads to a decrease in information entropy but does not bring to the full automatic control. In ergatic systems, object control is still reduced to declining the uncertainty to a minimum value by means of the operator’s control actions. The methodology for analyzing the entropy capacity of the system and elements of the augmented virtual assessment of the situation in the configuration of multifunctional machine units can be applied in the efficiency criteria development for vessel’s control systems with small crew or unmanned navigation in the concept of E-navigation.

Urban rail transit signal and control based on Internet of things

At present, China’s traffic signal control machine has a low level of intelligence and a single control strategy. It cannot make corresponding control according to the actual traffic situation, and its ability to direct traffic flow in a reasonable and orderly manner is low. In order to understand the urban rail transit signal and control system, we analyzed the requirements of the train dispatching subsystem, designed the overall architecture of the system from the perspective of function realization and architecture, and constructed the wireless sensor network of the system, which is the best for other experts. In this paper, combined with the research of related technologies of the Internet of Things (IoT), an intelligent traffic signal control machine is designed, and the traffic signal control effects under different algorithms are compared, and the relevant rail transit conditions are statistically studied. Studies have proved that sensors based on IoT technology can effectively improve the intensity and control effect of urban rail transit signals. Compared with other algorithm technologies, the overall score of the sensor algorithm is higher than other algorithms, and the ratio is about 30% higher. This article realizes the maintenance function of various data on the system simulation operation terminal, and builds the overall framework of the system; realize the main functions of the train dispatching operation terminal, including the realization of train dispatching functions such as station map, manual route arrangement, automatic route triggering, station deduction and station jump settings, and log report generation. This shows that the sensor algorithm under the IoT has a great promotion effect on the urban rail transit signal and control system.

Mathematical Approach in Complex Surfaces Toolpaths

This paper represents the focus on developing efficient algorithms that reduce the operations required to be employed in order to obtain complex surfaces milling finishing toolpaths for the three axis NC (Numerical Control) machine within the reverse engineering chain of processes. Direct machining is the process of generating efficient toolpaths directly from the digitized data, meaning the point cloud. The entire research is focused on determining the mathematical calculus able to interpret the data collected through the contact/noncontact 3D scanning process. In this direction, two algorithms were developed to generate ball-end mill finishing toolpaths for freeform surfaces using ordered/unordered point clouds. Practical work that validates author’s employed algorithms of obtaining finishing milling toolpaths uses the point cloud stored from the 3D scanning process in matrix found in ASCII files, which makes data interpreting easy.