Modelling Of Tool Life When Milling Nickel Base Alloys With Two Different Coated Carbide Insert.

This paper discusses the development of the first-order model for predicting the tool life in end-milling operation of Hastelloy C-22HS nickel base superalloy when employing two differently coated carbide cutting tools. The first-order equations of tool life are developed using response surface methodology (RSM). The cutting variables used in this study are the cutting speed, feed rate, and axial depth of cut. The analysis of the obtained models is supported using a statistical software package. From this study, it is found that the models are able to predict values of tool life close to those readings recorded experimentally with a 95%-confidence interval. The tool life first order equations show that feed rate is the most dominant factor, followed by axial depth of cut, and then cutting speed. In addition, the study indicates that inserts coated with a single TiAlN layer outperform the other type of inserts,which are with multiple-layered coating

Prediction of Torque in Milling by Response Surface Method and Neural Network

The present paper discusses the development of the first-order model for predicting the cutting torque in the milling operation of ASSAB 618 stainless steel using coated carbide cutting tools. The first-order equation was developed using response surface method (RSM). The input cutting parameters were the cutting speed, feed rate, radial depth and axial depth of cut. The study found that the predictive model was able to predict torque values close to those readings recorded experimentally with a 95% confident interval. The results obtained from the predictive model were also compared by using multilayer perceptron with back-propagation learning rule artificial neural network. The first-order equation revealed that the feed rate was the most dominant factor which was followed by axial depth, radial depth and cutting speed. The cutting torque value predicted by using Neural Network was in good agreement with that obtained by RSM. This observation indicates the potential use of RSM in predicting cutting parameters thus eliminating the need for exhaustive cutting experiments to obtain the optimum cutting conditions in terms of torque

Development of a high pressure compressed natural gas mixer for a 1.5 litre CNG-diesel dual engine

[Abstract]: The Computational Fluid Dynamics (CFD) analysis software was used to study the flow behaviour of compressed natural gas (CNG) and air in a CNG-air mixer to be introduced through the air inlet of a CNG-Diesel dual fuel stationary engine. The results of the simulation show that the Venturi mixer with more holes gives superior engine performance compared to the 4-hole Venturi mixer. Further analysis is done on the different holes mixer to investigate the effect of engine speed on the mass flow rate of CNG and the equivalence ratio Lambda. The second part of the paper represents a comparison results between the performances of a single cylinder research Compression Ignition CI engine fuelled with CNG-diesel system and conventional CI engine fuelled by conventional diesel. The engine was equipped with the simulated Venturi mixer, the result showed significant reduction in the exhaust gas emission compared to the conventional diesel engine. The average power output generated by dual fuel engine was slightly higher than that diesel one at different engine speeds

Neural Network Modeling Of Grinding Parameters Of Ductile Cast Iron Using Minimum Quantity Lubrication

This paper presents the optimization of the grinding parameters of ductile cast iron in wet conditions and with the minimum quantity lubrication (MQL) technique. The objective of this project is to investigate the performance of ductile cast iron during the grinding process using the MQL technique and to develop artificial neural network modeling. In this project we used the DOE method to perform the experiments. Analysis of variance with the artificial neural network method is used to investigate significant effects on the performance characteristics and the optimal cutting parameters of the grinding process. Ductile cast iron was used in this experiment and the ethanol glycol was applied in the conventional method and compared with the MQL method. During conventional grinding, a dense and hard slurry layer was formed on the wheel surface and the performance of the ductile cast iron was very low, threatening the ecology and health of the workers. In order to combat the negative effects of conventional cutting fluids, the MQL method was used in the process to formulate modern cutting fluids endowed with user- and eco-friendly properties. Aluminum oxide was used as the grinding wheel (PSA-60JBV). This model has been validated by the experimental results of ductile cast iron grinding. Each method uses two passes - single-pass and multiple-pass. The prediction model shows that depth of cut and table speed have the greatest effect on the surface roughness and material removal rate for the MQL technique with multiple-passes by showing improved surface roughness, preventing workpiece burning and enabling a more friendly environment. Thus, various other parameters need to be added for further experiments, such as the wheel speed, distance from the wheel to the workpiece zone contact, and the geometry of the nozzle

Graphene as an alternative additive in automotive cooling system

The project represents graphene can be used as an alternative additive in the automotive cooling system. Thus, graphene nanofluids have been prepared at 0.1, 0.3 and 0.5% volume concentrations. Afterward, measurement of various thermophysical properties of nanofluid such as thermal conductivity, density, viscosity, and specific has been done. The obtaining data has been analyzed and compared with graphene oxide, titanium oxide, aluminium oxide, silicon carbide, and copper oxide nanofluid to figure out the best nanofluid that can absorb more heat to protect the car engine from overheating. In, summary, the overall best nanofluid among these six would be graphene oxide, with the best thermal conductivity, specific heat capacity, and one of the lowest viscosities. As for comparison among graphene all volume concentrations, the 0.1% graphene nanofluid demonstrated the best with high thermal conductivity and low viscosity

Mamdani-Fuzzy Modeling Approach for Quality Prediction of Non-Linear Laser Lathing Process

Lathing is a process to fashioning stock materials into desired cylindrical shapes which usually performed by traditional lathe machine. But, the recent rapid advancements in engineering materials and precision demand gives a great challenge to the traditional method. The main drawback of conventional lathe is its mechanical contact which brings to the undesirable tool wear, heat affected zone, finishing, and dimensional accuracy especially taper quality in machining of stock with high length to diameter ratio. Therefore, a novel approach has been devised to investigate in transforming a 2D flatbed CO2 laser cutting machine into 3D laser lathing capability as an alternative solution. Three significant design parameters were selected for this experiment, namely cutting speed, spinning speed, and depth of cut. Total of 24 experiments were performed with eight (8) sequential runs where they were then replicated three (3) times. The experimental results were then used to establish Mamdani - Fuzzy predictive model where it yields the accuracy of more than 95%. Thus, the proposed Mamdani-Fuzzy modeling approach is found very much suitable and practical for quality prediction of non-linear laser lathing process for cylindrical stocks of 10mm diameter

Artificial Neural Network Optimization Modeling On Engine Performance Of Diesel Engine Using Biodiesel Fuel

This paper presents a study of engine performance using a mixture of palm oil methyl ester blends with diesel oil as biodiesel in a diesel engine, and optimizes the engine performance using artificial neural network (ANN) modeling. To acquire data for training and testing of the proposed ANN, a four-cylinder, four-stroke diesel engine was fuelled with different palm oil methyl ester blends as biodiesel, operated at different engine loads. The properties of biodiesel produced from waste vegetable oil were measured based on ASTM standards. The experimental results revealed that blends of palm oil methyl ester with diesel fuel provided better engine performance. An ANN model was developed based on the Levenberg-Marquardt algorithm for the engine. Logistic activation was used for mapping between the input and output parameters. It was observed that the ANN model could predict the engine performance quite well with correlation coefficients (R) of 0.996684, 0.999, 0.98964 and 0.998923 for the incylinder pressure, heat release, thermal efficiency, and volume, respectively. The predicted MSE (mean square error) error was between the desired outputs, as the measured and simulated values were obtained as 0.0001 by the model. Long-term effects on engine performance when running on biodiesel fuel can be further studied and improved

Finite Element Analysis of Hastelloy C-22HS in End Milling

This paper presents a finite element analysis of the stress distribution in the end milling operation of nickel-based superalloy HASTELLOY C-2000. Commercially available finite element software was used to develop the model and analyze the distribution of stress components in the machined surface of HASTELLOY C-22HS following end milling with coated carbide tools. The friction interaction along the tool-chip interface was modeled using the Coulomb friction law. It was found that the stress had lower values under the cut surface and that it increased gradually near the cutting edge

Analysis of Connecting Rod Based on Finite Eliment Approach

The connecting rod is one of the most important pats of an automotive engine. The connecting rod is subjected to a complex state of loading. High compressive and tensile loads are due to the combustion and connecting rod’s mass of inertia respectively. The connecting rod fails during the operation of the engine is the critical situation. Therefore the connecting rod should be able to withstand tremendous load and transmit a great deal of power smoothly. The objective of this paper is to investigate the failure analysis of the connecting rod of the automotive engine. The materials including carbon steel, mild steel, bass and aluminum are considered in this study. The linear static analysis was carried out utilizing the finite element analysis codes. The numerical results were verified with the experimental results. It can be seen from the acquired results that the carbon steel gives good results in terms of hardness and endurance limit compared with the other materials