Adaptive neuro-fuzzy inference system for deburring stage classification and prediction for indirect quality monitoring

Abstract

Manufacturing of aerospace components consists of combination of different types of machining, finishing, and measuring processes. One of the finishing processes is deburring, i.e. a finishing process to remove burrs from work coupons after a boring hole process. Deburring is conducted to achieve required surface finish quality prior to further processes in assembly line. This paper introduces sensor data analysis as a tool to quantify and correlate the deburring stage with the features extracted from sensors data. This study covers signal processing, feature extraction and analytical method to determine its relevancy to the surface finish quality from deburring process. Wavelet decomposition and Welch spectrum estimate is used as a signal processing and feature extraction method. Consequently, the features are used as the basis for analysis by adaptive neuro-fuzzy inference system (ANFIS). The ANFIS yields the output corresponding to the predicted surface finish quality in terms of boss hole chamfer length and the stage classification of deburring process. The results show a decreasing trend in measured vibration signal, which is qualitatively well correlated the deburring stage and the development of chamfer length during deburring.

Introduction to Special Issue on Symmetry in Mechanical Engineering

“…The presented solutions, methods, and approaches can be improved and used in the future. Moreover, mechanical engineering is essential for fault diagnosis of machines [10][11][12][13][14][15][16][17][18][19][20][21][22] and the analysis of temperature [23][24][25]. The mechanical properties of materials are also investigated in the literature [26][27][28].…”

Acoustic-Based Fault Diagnosis of Commutator Motor

“…, where FS-A1, FS-A2, FS-A3, FS-A4-denoted 4 frequency spectra of state A, FS-B1, FS-B2, FS-B3, FS-B4-denoted 4 frequency spectra of state B, FS-C1, FS-C2, FS-C3, FS-C4-denoted 4 frequency spectra of state C, FS-D1, FS-D2, FS-D3, FS-D4-denoted 4 frequency spectra of state D, FS-E1, FS-E2, FS-E3, FS-E4-denoted 4 frequency spectra of state E. Next, 40 differences between frequency spectra are computed: The MSAF-15-MULTIEXPANDED-8-GROUPS found 28 essential frequency components: 48,50,79,81,97,101,128,157,159,1469,1471,1672,1926,1927,1934,1935,1939,1942,1953,1957,1958,1961,1978,2038,2039,2042 Found essential frequency components were classified by the NN classifier [35,36], NM classifier, SOM [37], BNN [38][39][40][41][42][43][44]. There was possibility to use another classifier such as naive Bayes, support vector machine [45][46][47], linear discriminant analysis [48], fuzzy classifiers [49,50], and fuzzy c-means clustering [51].…”

Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis

“…It keeps changing on demand, which follows the nonlinear and time-varying properties of the given problem. PANFIS is also capable of crafting a classical-transparent fuzzy rule from a high-dimensional ellipsoidal rule through fuzzy rule transformation (14). An example of PANFIS's rule is exemplified in variance prediction:…”

“…This fuzzy rule is rather vague and is unable to be associated directly with linguistic labels because of the absence of atomic clauses. This issue can be addressed using the fuzzy set transformation strategy (14) and this leads to the traditional expression of the fuzzy rule as follows: R 1 :If Variance n−1 is close to c 11 = 0.29, σ 11 = 0.11 and Skewness n−2 is close to c 12 = 0.292, σ 12 = 0.11 Then y = 0.03 + 0.17variance n−1 + 0.04variance n−2 (21) This rule is more readable than (20) because each fuzzy set corresponds to a specific linguistic label. This fuzzy rule is generated under the time-series mode prediction.…”

“…The parameter learning strategy is based on the extended recursive least square method [13], which appends a binary function to enhance the convergence and stability of the tuning process.The objective of the proposed method is to predict the future state of the slew bearing condition based on the vibration features on a timely basis. The Fuzzy-based method is selected as a prognosis method due to its capability to capture the nonlinear time-varying nature of the process [14]. Some recent literature has also proved that the fuzzy-based method is a potential method for prediction [15][16][17].…”