A Comprehensive Failure Risk Assessment Method of Machining Center Component Based on Topology Analysis

The risk assessment of the failure mode of the traditional machining center component rarely considers the topological characteristics of the system and the influence of propagation risks, which makes the failure risk assessment results biased. Therefore, this paper proposes a comprehensive failure risk assessment method of a machining center component based on topology analysis. On the basis of failure mode and cause analysis, considering the correlation of failure modes, Analytic Network Process (ANP) is used to calculate the influence degree of failure modes, and it is combined with component failure mode frequency ratio and failure rate function to calculate independent failure risk. The ANP model of the machining center is transformed into a topological model, and the centrality measurement of network theory is used to analyze the topology of the machining center. The weight of the topological structure index is measured by subjective and objective weighting methods, and then the importance degree of the machining center component is calculated. In this paper, the coupling degree function is introduced to calculate the importance of the connection edge, which is combined with the failure probability to calculate the failure propagation influence degree, and the component propagation failure risk is calculated based on this. Finally, the independent failure risk and the propagation failure risk of the component are integrated to realize the failure risk assessment of the component. Taking a certain type of machining center as an example to illustrate the application, compared with the traditional assessment method, the effectiveness and advancement of the method proposed in this paper have been verified.

Frequency response function simulation and evaluating in boring

Finite element method of simulating frequency response function (FRF) for boring tool in LS-Dyna solver is investigated in this work. Nowadays, computer numerical simulation allows to obtain FRF using different materials model with high precision compared to real experiments with sensors like impact hammer testing. This function is used in construction of stability lobe diagrams that allows operator of machining center to avoid chatter self-excited vibrations. Such vibration is led to decreasing of productivity and quality in cutting of metals and other materials. Amplitude and phase angle for the model is obtained from LS-Dyna result interpreter, that reads binary files, created during simulation by the program. Amplitude and phase angle of frequency response function are depending on dynamic stiffness of machining system. Real and imaginary part of frequency response function have been obtained during simulation. With luck of dynamic stiffness amplitudes of response increases.

Effect of Mo addition on microstructure, mechanical and machinability properties of Cr-PM steels

In this work, it was investigated the effect of molybdenum (Mo) addition on machinability, mechanical properties, and microstructure of Cr steels produced by using powder metallurgy method. Tensile and hardness experiments were applied to define the mechanical properties of the produced Cr-PM steels. The machining experiments have been also performed without coolant on a CNC vertical machining center at three different cutting speeds (150, 210, and 270 m/min), two different feed rates (0.4 and 0.8 mm/tooth), and constant depth of cut (0.5 mm). The machinability of the alloys was evaluated in regard to surface roughness (Ra) and tool wear (Vb). The results indicated that that Cr-PM steel with 5% Mo addition by weight had the highest yield, tensile strength, and hardness, and the best surface quality was obtained in this sample in terms of surface roughness. However, according to Vb measurement results, the cutting performance of the cutting inserts wasnegative affected by MoC(N), CrC(N), and MoCrC(N) precipitates formed in the microstructure of PM steel.

Improving the methods for determining the index of quality of subsystem element interaction

It was proposed to improve the existing method of determining the quality of interaction of the elements of subsystems of the Machine Operator-Machining Center-Control Program for manufacturing parts (MO-MC-CP) system. This method combines estimates of social (machine operator), technical (machining center), and informational (control program for manufacturing parts) subsystems. Improvements were achieved through the use of four independent indices which are defined separately. One index takes into account single, double and triple interactions of integrated indicators where values of specific weight of weight coefficients depend on the sample size. The other three indices are a synergistic effect where the weight coefficients do not depend on the sample size. Therefore, the model of this index was modified at the expense of additional subsystems. Existing approaches to determining the indices are not focused on the assessment of the quality of interaction of the MO-MC-CP system, have software limitations, and work with limited sample sizes. With this in mind, it was decided to improve the existing tools of determining the quality indices of interaction to assess levels of interaction of the subsystem elements. The proposed software-implemented methods and the technology of index assessment improve the efficiency of the assessment of complex systems. Experimental verification has shown the superiority of interaction quality indices over those in the existing methods. A sign of efficiency is as follows: a smaller value of mean-square deviation of the proposed indices in comparison with the existing ones: S(ІQI1)=0.812; S(ІQI2)=0.271; S(ІQI3)=0.675; S(ІQI4)=0.57 and S(І)=0.947; S(І)=0.833; S(І)=0.594, respectively. The results obtained in the study of the interaction quality index are useful and important because they make it possible to better assess the interaction of subsystem elements and apply the proposed technology at industrial enterprises.

PREDICTION OF ENERGY CONSUMPTION IN THE LEADWELL V-40 IT CNC MACHINING CENTER THROUGH ARTIFICIAL NEURAL NETWORKS

Energy consumption in machining processes has become a problem for today's manufacturing industry. The use of neural networks and optimization algorithms for modeling and prediction of consumption as a function of the cut-off parameters in processes of this type has aroused the interest of research groups. The present work used artificial neural networks (ANN) to predict the energy consumption of a Leadwell V-40iT® five-axis CNC machining center, based on experimental data obtained through a factorial experimental design 53. ANN was programed in Matlab®. From the study was concluded that the depth per pass (Ap) is the variable that has the most influence on the prediction model of energy consumption with a 77% of relative importance, while the feed rate is the least relevant with 9% of importance.

Research on Availability Function Deployment for Motorized Spindle based on User Requirement

Users are the basis for the survival and development of enterprises. Only by understanding user requirement can products have sufficient competitiveness. In this paper, the machining center motorized spindle is taken as the research object. Based on the user requirement, this paper takes the motorized spindle as the research object, and combines the failure performance characteristics to expand the availability function. This method can transform the requirement information into the availability improvement strategy of the motorized spindle weak links, which is an effective method to quickly improve the availability level of the machining center motorized spindle.