1. Ph.D Theses

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Subject: search.filters.subject.Fault diagnosis

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    Design and Development of Magnetorheological Fluid Damper to Suppress the Tool Vibration In Hard Turning Operation
    (National Institute Of Technology Karnataka Surathkal, 2023) Aralikatti, Suhas S.; Kumar, Hemantha
    The state of the cutting tool determines the quality of the surface finish produced on the machined parts. A faulty tool produces poor surface and inaccurate geometry leading to the rejection of parts. It is necessary to monitor tool conditions to have consistent quality and economic production. Condition monitoring is ineffective without the implementation of a real-time corrective strategy. In the present study, fault classification of single-point cutting tools for hard turning has been carried out by employing signal processing and machine learning technique using cutting force signals and vibration signals. A comparison of the performance of classifiers was made between cutting force and vibration signal to choose the best signal acquisition method in classifying the tool fault conditions using the machine learning technique. A set of four tool conditions, namely healthy, worn flank, broken insert and extended tool overhang, have been considered for the study. These faulty tools produce undesired vibration that reduces machine quality and production rate. The adverse effect of tool vibration leads to loss of geometric tolerance, accelerated tool wear, poor surface finish and machine instability. The author designed a current- controlled compact magnetorheological fluid (MRF) damper consisting of an electromagnetic coil in the piston as a corrective measure. The damper is fitted onto the lathe machine with the optimal fluid composition to evaluate its performance in controlling the tool vibration. The optimal composition of MRF is identified by a genetic algorithm through the central composite design of the experiment. To cross- verify the algorithm's output values, a validation study is done. A comparison between optimal in-house MR fluid and commercial MR fluid is conducted. The comparison demonstrates that in-house prepared MR fluid performs equally well compared to commercial fluid. The MR damper effectively damps high-amplitude vibration at aggressive cutting conditions. The L9 Taguchi design of the experiment opted to arrive at minimal machining parameters to evaluate the performance of the damper in machining two workpiece materials, namely oil-hardened nickel steel (OHNS) and high carbon high chromium (HCHCR) die steel. The surface roughness and tool vibration iiiare reduced with the damper. It is noted that in-house MR fluid performed equally well as commercial MR fluid. The tool wear study is also carried out to monitor the influence of external damping over tool life. The stability lobe diagram is obtained analytically with experimental validation to mark the stability limit of the machining condition. The stability boundary increases with the damper enabling aggressive cutting conditions. The designed MR damper is controlled by a real-time controller considering the vibration-limiting feedback approach. The Bouc-Wen model is used to estimate the damping force based on the vibration feedback of the tool. The tool wear, surface roughness, and amplitude of tool vibration are evaluated with and without a semi-active MR damper. The above-developed MR damper forms an external adaptor to control the tool vibration that can be installed on the lathe. To improve the design configuration of the MR damper, an internally damped novel tool holder is designed that houses MR fluid in its axial hollow section. The MR fluid is activated by the internal electromagnet coil wound around the inverse beam supported at the inner wall of the hollow portion. The developed MR tool damper reduces the tool vibration with the electric current supply.
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    Condition Monitoring of Face Milling Tool Using Vibration and Sound Signals
    (National Institute of Technology Karnataka, Surathkal, 2018) C. K, Madhusudana; Kumar, Hemantha; S, Narendranath
    Fault diagnosis of the cutting tool is very essential for improving the quality and maintaining the accurate dimension of the products during machining process. The milling is a multi-toothed metal removing process. In face milling, because of dynamic variation of cutting forces, thermo-mechanical shocks and vibration, which results in catastrophic tool failure along with gradual wear of the tool inserts. Wear development during machining can reach up to unacceptable level, resulting in inaccurate dimension and poor surface finish of the components. Monitoring the condition of the cutting tool during face milling operation is a vital role before the tool causes any damage on the machined surface which becomes highly valuable in order to avoid loss of products, damage to the machine tool and associated loss in productivity. Keeping in view of the automation, it is necessary to choose an effective and efficient method for monitoring the cutting tool condition without affecting the machining setup and the work material. This study mainly deals with the fault diagnosis of the face milling tool using vibration and sound signals through signal processing techniques and machine learning approach. The face milling is a machining process with an intermittent cutting action. The milling tool will undergo different types of faults such as flank wear, breakage and chipping which occurs predominantly during milling. The vibration and sound signals under these faulty and healthy milling tool conditions are acquired and these signals are further analyzed. Current research work is mainly categorized into two phases. The first phase is to detect/diagnose the face milling tool conditions by analyzing the vibration and sound signals using signal processing techniques. The signal processing techniques such as time-domain analysis, spectrum analysis, cepstrum analysis and continuous wavelet transform (CWT) method are applied to recognize the face milling tool conditions. The cepstrum analysis has been applied for the first time in fault detection of the face milling tool and has provided the sufficient information about the face milling tool condition using both vibration and sound signals. Generally conventional data processing is computed in time or frequency domain which is not suitable for analyzing non-stationary signals. In order to overcome the lack of a global view on how to develop machining monitoring systems based on artificial intelligent models, machine learning approach is one of the best methods for developing an effective tool condition monitoring (TCM) system.In the second phase, fault diagnosis studies of the face milling tool using vibration and sound signals based on artificial intelligence techniques are conducted. Fault diagnosis of the different tool conditions based on machine learning technique is basically comprised of three steps; feature extraction, feature selection and feature classification. Different features such as, statistical features, histogram features, discrete wavelet transform (DWT) features and empirical mode decomposition (EMD) features are extracted from the acquired vibration and sound signals. For example, features such as skewness, mode, standard error, maximum, minimum, range, sum, mean, standard deviation, median, sample variance and kurtosis are computed from each acquired vibration and sound signals will serve as statistical features. The important features out of all extracted features are to be selected using induction based on decision tree technique (ID3 algorithm or J48 algorithm). The artificial intelligence techniques such as support vector machine (SVM), Naïve Bayes algorithm, artificial neural network (ANN), decision tree algorithm and K-star algorithm are used to classify the data using selected features. Fault diagnosis analysis with acquired vibration and sound signals are carried out by making use of different combinations of feature extraction methods and different classifiers with selected features based on decision tree technique. Overall results have shown that the vibration signal based fault diagnosis has given better classification accuracy than the sound signal based fault diagnosis. The current research work has demonstrated that the statistical features served as best features among all other features extracted such as, EMD features, Histogram features and DWT features. It is also found that the Naïve Bayes algorithm provides best classification accuracy in comparison with other classifiers used such as SVM, ANN, decision tree and K-star algorithm. Based on research work, it is proposed that the combination of statistical features and the Naïve Bayes algorithm as classifier is the best feature-classifier pair using vibration signals in tool condition monitoring system for the face milling process.