Research On The Indirect Monitoring Technique Of Grinding Burn And Its Interrelated Problems In Pre-Cision Grinding Process

Process monitoring system is the bridge between equipments and management roles. It could bring considerable benefits such as automation and intelligence into modern manu-facturing process. However, due to the complexity and diversity of modern manufacturing equipments, applications of process monitoring system in real industries are rarely seen. The major reason of such phenomena is the lack of interpretation and understanding of manufacturing process, and the short of process monitoring methods. To alleviate such kind of problems, this thesis focuses on grinding burn and its interrelated scientific problems, to get to the bottom of the relationship between sensor features and grinding parameters, to extract signal features closely related with grinding burn and its interrelated problems, and to build a feasible indirect monitoring system for precision grinding. Several efforts have been achieved concentrating around these specific themes and list as follows:In Chapterl, scientific backgrounds and state-of-art concerning this field are compre-hensively elaborated. The structure and contents of this thesis are depicted at the end of this chapter.Chapter2constructs mathematical models for AE RMS and cutting strain energy under single-grit-workpiece condition. Relations between grinding parameters and AE sensor are also studied both in theory and experiment. In order to make a thorough inquiry into thermal damage of metallic materials, AE features in laser-induced burn are extracted by virtue of preliminary time domain and frequency domain methods.In Chapter3, focus is shifted from laser-induced burn to practical grinding. Grinding burn features, i.e., spectral centroid of PSD and maximum power, are extracted, by means of AE sensor, current transducers, voltage transducers and accelerator.In order to go deep into grinding burn feature extraction methods, Chapter4keeps on studying issues raised in Chapter3. Discrete Wavelet Tranform(DWT) and Hilbert-Huang Transform(HHT) are utilized in grinding burn feature extraction. Results indicate that RMS value of the first5details yielded from DWT and marginal spectra of HHT can successfully reflect the occurrence of grinding burn.Chapter5focuses on interrelated problems of grinding burn, e.g., grinding wheel wear and wheel-workpiece initial contact detection. Wheel-workpiece contact detection method based on Empirical Mode Decomposition is proposed in this chapter. Solutions of wheel wear detection and prediction are also given in Chapters.A grinding process monitoring system is constructed in Chapter6, based on Support Vector Machine(SVM) and features extracted in previous chapters.Conclusions and prospects are briefly depicted at the end of this thesis.