1. Ph.D Theses
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/1/11
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Item Investigations on Machinability Characteristics of EN47 Spring Steel using Optimization Techniques(National Institute of Technology Karnataka, Surathkal, 2019) Vasu, M.; Nayaka, H Shivananda.Challenge of any manufacturing industry to give a better quality of products to society with minimum manufacturing cost, low manufacturing time and less consumption of raw material. Manufacturing involves various processes to convert raw material into finished products and hence meet demands with high-quality products. Selection of process parameters plays a significant role to satisfy all demands to ensure the quality of the product, increased production rate, and reduced operating cost. For such cases, optimization is essential to represent manufacturing process. Process parameters have been optimized by chosen best possible optimization techniques. Before conducting any experiments, selection of workpiece and tools it is necessary, to explore the literature to know, what has happened in earlier days. A literature survey has been done thoroughly existing statistical techniques are understood and implemented to optimize speed, feed, and depth of cut. EN47 spring steel has been chosen as work material which has a hardness of 45-48HRC. Hard turning process eliminates grinding process, and EN47 steel possesses low thermal conductivity and suitably oil hardened and tempered. Hardened spring steel offers excellent toughness and shock resistance, and are considered as suitable material for automobile applications. Other applications involve such as manufacturing of die, leaf spring for a heavy vehicle, crankshaft, spindles, pumps and steering knuckles and many general engineering applications. Experiments were performed using two different techniques, namely, one factor at a time (OFAT) approach and Design of Experiments (DOE). Cutting tool inserts are commercially available in the form of PVD coated TiAlN German make and are used during machining. Cutting forces, surface roughness, tool tip temperature, and material removal rate are estimated experimentally. From the experimental work, it is known that with an increase in nose radius, cutting forces, tool tip temperature, and material removal rate are increased, but surface roughness is decreased. Further, a tool with 0.8mm nose radius exhibits nominal performance in all output performances. 0.8mm nose radius tools are used to work in three different cutting environments, namely dry, wet and cryogenic. From the analysis, cryogenic machining showed better quality of the machined surface, tool wear also reduced and tool tip temperature decreased.viii Experiments were performed and analyzed using design of experiments (DOE) technique L27 full factorial design. A second order regression model was developed to know the interaction effect of output responses. Tool wear was analyzed by confocal microscope and SEM, with varying cutting time. ANOVA was used to identify the significant factor and percentage contribution for a particular output. Results from machining reveal that cutting force is mainly influenced by feed rate and depth of cut. Surface roughness was influenced by cutting speed and feed rate. Tool tip temperature was influenced by cutting speed and depth of cut. Material removal rate was influenced by speed, feed, and depth of cut. 3D response surface plots show interaction effect on each output response. Main effects plots show optimum condition for each output performance. Normal probability plots showed that the developed models are adequate by observing normal error distribution. Determination coefficient (R2) value should be in between 1 or 100% in the model. Multi-objective optimization was identified by Desirability Approach (DA) and Particle Swarm Optimization (PSO). Also, Artificial Neural Network (ANN) is used to predict experimental results and compared with RSM model, as well as, experimental value. Statistical analysis was done by Minitab and Design Expert Software. Validation was performed by ANN. MATLAB is used to develop artificial neural network model, as well as; codes are developed for PSO. From the experimental analysis, the developed model showed a significant and good agreement between the experimental value and predicted value.Item Falut Diagnosis of Single Point Cutting Tool Through Online and Offline Monitoring Techniques(National Institute of Technology Karnataka, Surathkal, 2016) N, Gangadhar.; S, Narendranath; Kumar, HemanthaTool condition monitoring plays a crucial role in automated industry to monitor the state of cutting tool. It prevents any hazards occurring to the machine, avoid deterioration of the surface finish on end product and it helps to introduce a new tool in an instant at which the existing tool has worn out toensure safety, productivity and optimum performance of the metal cutting process. In the present research work,fault diagnosis of single point cutting tool is investigated based on the vibration signals and cutting force signals on an engine lathe. Vibration signals and cutting force signals corresponding to a healthy insert (baseline) anddifferent types of industrial practical worn out insertswere recorded. The research work is carried out in three phases. The first phase investigates fault diagnosis of cutting tool using signal processing techniquessuch astime domain, spectrum, cepstrum, continuous wavelet transform (CWT), recurrence plots (RPs) and recurrence quantification analysis (RQA). The result shows that recurrence plots and recurrence quantification analysis were useful for revealing post fault detection and diagnosis of worn states of the inserts. The second phase of research workpresents fault diagnosis of cutting tool using machine learning approach based on vibration signals. From the vibration signals, statisticalfeatures, histogram features, discrete wavelet transform (DWT) features and empirical mode decomposition (EMD) featureswere extracted. Principle component analysis (PCA) and J48 algorithm (decision tree) were used for important feature selection/reduction. Artificial neural network (ANN), Naïve Bayes, Bayes net, support vector machine (SVM), K-star and J48 algorithm classifiers have been used to classify the different fault conditions. Classification accuracy is found to be reasonably good with J48 algorithm feature selection compared to PCA. The third phase presents the results of investigations undertaken to find suitability of vibration signals and cutting forces to detect the condition of tungsten carbide cutting tool insert, surface roughness and type of chip formation. The results show that there is an increase in the level of acceleration and cutting force at faulty tool condition ascompared with the healthy condition of the tool. Based on this finding, cutting tool acceleration and cutting forces can be used to predict the cutting tool condition, surface roughness and chip formation type. Qualitative comparisons of the computational predicted forces are drawn by plotting the trends of the predicted forces together with the measured forces. The Deform-3D has correctly predicted this trend which is consistent with the experimental trends of the cutting forces components. Tool wear analysis has been carried out on the worn tungsten carbide insert cutting tools to find the tool wear mechanisms.Based on SEM micrographsof worn surface of the cutting tool,micro-abrasion, micro-attrition, adhesion and micro-fatigue behaviors are identified as the dominant kinds of wear mechanisms.