The condition of mechanical equipment during machining is closely related to the accuracy and roughness of the workpiece. In an intelligent sensing environment, a large amount of multi-source data reflecting status information are generated during processing, and a number of studies have been conducted for machining equipment reliability analysis. In this paper, the reliability analysis method of machining equipment based on condition monitoring technology is taken as the main line. And an up-to-date comprehensive survey of multi-source information during the cutting process, failure physical analysis for signal selection and reliability assessment based on condition information will be provided. Finally, the future challenges and trends will also be presented. It is a feasible and valuable research direction to evaluate the reliability of machining equipment for product quality characteristics.
During the numerical control manufacturing process, the operational path and process parameters were defined, however, the various kinds of manufacturing factors were still generating great uncertainties. In this paper, the quality entropy model of the manufacturing process is put for word to analyze the uncertainty factors on the products of NC machining. The entropy theory combined with information entropy and quality entropy concept was proposed to model the quality entropy of the manufacturing process. The manufacturing factors, including hardware equipment, operator, environment deviation and rejection rate which influence the product quality are given, and the criterion and method determining the corresponding uncertainty state are given, and the quality entropy algorithm of the manufacturing process and the influence degree of each uncertainty are given. And the case study is analyzed and the quality entropy of manufacturing process is calculated.
Machining is the process that a kind of mechanical device change the dimensions or the performance of the workpiece, which has a great influence on the quality of the component. Manufacturing and processing enterprises always want to improve the passing rate and the life of the machining workpiece and reduce unnecessary costs during processing. This must strictly control machining process based mechanical process systems, however, due to non-ideal conditions of the actual process, the process is unstable, resulting in the quality of the product cannot be controlled during processing. In this paper, we propose a kind of machine fault pre-warning and diagnosis method based online testing of the process to solve this problem that machine fault can cause the quality problems of the workpiece during machining, collecting real-time machine state parameters by the sensor signal, using signal analysis methods such as Fourier transform and wavelet analysis, and analyzing real-time process monitoring data, and classifying data By KNN algorithm, and judging the machine working status and it's fault occurrence, and using LabVIEW to construct of the entire monitoring and controlling environment and to applied to the actual data, and processing and analyzing real-time data in the machining process that can judge the state of the machine, so the faults of the machine can be early found, there, by reducing the failure rate of the workpiece. The research about online numerical control machine fault diagnosis not only real-time judged part that the machine may have faults, but also optimized machining processes of the parts.
Typical matching parts of servo valve have high precision requirement and strict fit tolerance, which reduces the matching quality and practical performance. The traditional fit quality control method of the matching parts is based on expert experience and tolerance analysis, which is subjective and indirect. In this paper, a novel method was proposed to determine the matching quality before precision grinding processing. Firstly, the Empirical Mode Decomposition (EMD) was used to decompose the frequency of radial surface tolerance, and the intrinsic mode functions (IMFs) of the tolerance were obtained. Secondly, the correlation coefficients of the IMFs and the matching gap were calculated. Finally, the mode features are classified by using decision tree. The experimental results showed that the method can effectively identify the matching quality, and improve the pass rate of the precision grinding processing.
There is a growing body of literature which recognizes the importance of mechanical equipment reliability during processing, and reliability assessment is important in guaranteeing the precision, function, and use life span of mechanical equipment. For products with a long lifetime and high reliability, it is difficult to assess lifetime and reliability using traditional statistical inference based on a large sample of data from the lifetime test. Therefore, this study contributed to this growing area of research, through a reliability evaluation method based on degradation path distribution related to signal characteristics. In this research, an effective method for reliability assessment was constructed, in which the signal features of the machining process were used to replace traditional time data and fit equipment degradation model. The pseudo failure characteristic (PFC) was obtained according to the failure threshold and the reliability curve was plotted by a PFC distribution model. Experimental investigation on tool reliability assessment was used to verify the effectiveness of this method, in which the trend that tool wear changes with the features was fitted by a Gaussian distribution function and Logarithmic distribution function, to obtain a better tool degradation model. The results illustrated the model could evaluate reliability of mechanical equipment effectively.
There is a growing body of literature that recognizes the importance of product safety and the quality problems during processing. The working status of cutting tools may lead to project delay and cost overrun if broken down accidentally, and tool wear is crucial to processing precision in mechanical manufacturing, therefore, this study contributes to this growing area of research by monitoring condition and estimating wear. In this research, an effective method for tool wear estimation was constructed, in which, the signal features of machining process were extracted by ensemble empirical mode decomposition (EEMD) and were used to estimate the tool wear. Based on signal analysis, vibration signals that had better linear relationship with tool wearing process were decomposed, then the intrinsic mode functions (IMFs), frequency spectrums of IMFs and the features relating to amplitude changes of frequency spectrum were obtained. The trend that tool wear changes with the features was fitted by Gaussian fitting function to estimate the tool wear. Experimental investigation was used to verify the effectiveness of this method and the results illustrated the correlation between tool wear and the modal features of monitored signals.
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