The key technology of vibration machine to finish deaggregation and powder refinement is both high vibration Intensity with a certain frequency and steady vibration of frequency conversion control. A frequency conversion control for vibration mill system based on multi-wave variable sinusoidal frequency ascending and descending cycle curve was developed. The frequency conversion control test of vibration mill was finished through sensing signal amplification, analog digital conversion and on line detection. The results show that the high and super-high vibration intensity with certain action time is produced and the effect of multi-amplitude and multi-frequency is acquired. The experimental data show that the super micro grinding of superfine particles is achieved, energy consumption is reduced, the useful life of wearing parts such as bearing is extended, and engineering effects of deaggregation and refinement of super-hard superfine powder are obtained by frequency conversion control.
The effectiveness of status monitoring in cutting process is contingent on the effectiveness of the sensor location optimization. A novel adaptive evolutionary algorithm (AEA) based on genetic algorithm (GA) to achieve an optimal sensor configuration for monitoring the cutting status closely related to the workpiece quality is proposed. Based on an understanding of the cutting principle, the minimal failure probability at minimum cost is the optimal target and the detectability and the false alarm rate are defined as the constraint conditions. The feasible region of solution is presented in decimal coding . The crossover operator and population size are adapted relative to the iterations estimated by a Markov model,which improve the convergence. The results of a case study have been found to agree well with the optimized results by AEA .The optimal scheme of AEA yields the minimal sensing stations while simultaneously attaining 99.99% system detectability and renders nearly 60% decrease in sensor cost in comparison to the scheme of saturated sensing.
Some special operations of chaotic vibration mill require not only has a certain frequency high vibration intensity but also can effectively control overtime and over-limited of vibration intensity. The validity of advanced control decision is key technology for chaotic vibration mill to achieve some special operations such as super-hard and superfine grinding. Through the application based on the intelligent frequency conversion of PLC advanced control system, the high vibration intensity exceeds 15 presents discontinuously and the transient super-high vibration intensity exceeds 20 presents 3 times shortly. By comparing with conventional vibration mill, it shows that the advance control system can control high vibration intensity effectively, and the engineering effects of the deaggregation and refinement of super-hard and superfine grinding is obtained.
Thepresent paper presents a design procedure and prototype of detection system, which uses high-precision grating displacement sensor as the main test instrument for detecting bogie wheelset alignment parameters in horizontal plane. Wheelset rim inside distance, wheelbase difference and diagonal difference of newly completed bogie are obtained automatically and accurately in once measurement, moreover, detection is not limited to different bogie wheelbase and different size of wheels. Furthermore, measurement and control system and software are designed; detection results should be saved, queried and printed. The precision of the detecting system could reach to 0.02mm. Compared with foreign similar equipments, the advantages of this detection system is improving the detection accuracy and reducing costs.
The surface roughness and roundness(SRR) are widely used indexes of mechanical product quality. How to implement the SRR monitoring is a crucial task. In this study, the hidden Markov models (HMMs) and the cutting vibration signals are applied to monitor the SRR in variant cutting conditions. Unlike most of the prior work only to reveal one element of the geometric specifications, based on the theoretical analysis of the influence of tool vibration displacement on the SRR, the vibration energy characteristic(VEC) is determined to serve as the characteristic for monitoring surface roughness(Ra) and roundness(Rd) synchronously.Which make up the insufficiency of the comprehensive monitoring of workpiece quality. Moreover, although classical hidden Markov models (HMMs) have been successfully used for fault diagnostics of mechanical systems, this method based on recognition rate is becoming unreliable to monitor the accuracy of the workpiece. Hence, the HMM-based judgment matrix method is proposed and it is tested and validated successfully using for SRR monitoring through a series of experiments.
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