Advanced Materials Research Vol. 576

Abstract: This paper presents the experimental result of grinding performance with an internal through coolant supply. Using this method, the coolant is delivered through the grinding wheel and this concept will allowed the coolant supplied directly to the contact zone. The experiment has been conducted on thin plate of AISI1020 mild steel, to ensure the effectiveness when using internal through coolant supply and the grinding result using internal through. The result of internal through coolant supply was compared with an external coolant supply. It was revealed that the temperature was lower and surface roughness was smaller when the proposed coolant supply concept used.

Abstract: Machining of hardened steel at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality and cutting tool life. Thus, predicting the temperature in early stage becomes utmost importance. This research presents a neural network model for predicting the cutting temperature in the CNC end milling process. The Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the cutting temperature. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the cutting temperature. The artificial neural network (ANN) was applied to predict the cutting temperature. Twenty hidden layer has been used with feed forward back propagation hierarchical neural networks were designed with Matlab2009b Neural Network Toolbox. The results show a high correlation between the predicted and the observed temperature which indicates the validity of the models.

Abstract: Power consumption cost is one of the main integral parts of the total machining cost, but it has not given the proper attention when minimizing the machining cost. In this paper, the optimal machining parameters for continuous machining are determined with respect to the minimum power consumption cost with maintaining the surface roughness in the range of acceptance. The constraints considered in this research are cutting speed, feed rate, depth of cut and rake angle. Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) was applied to resolve the problem, and the results have been analyzed.

Abstract: Surface roughness affects the functional attributes of finished parts. Therefore, predicting the finish surface is important to select the cutting levels in order to reach the required quality. In this research an experimental investigation was conducted to predict the surface roughness in the finish end milling process with higher cutting speed. Twenty sets of data for finish end milling on AISI H13 at hardness of 48 HRC have been collected based on five-level of Central Composite Design (CCD). All the experiments done by using indexable tool holder Sandvick Coromill R490 and the insert was PVD coated TiAlN carbide. The experimental work performed to predict four different roughness parameters; arithmetic mean roughness (Ra), total roughness (Rt), mean depth of roughness (Rz) and the root mean square (Rq).

Abstract: In finishing end milling, not only good accuracy but also good roughness levels must be achieved. Therefore, determining the optimum cutting levels to achieve the minimum surface roughness is important for it is economical and mechanical issues. This paper presents the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and minimum Root-mean-square roughness (Rq). The mathematical models for the surface roughness parameters have been developed, in terms of cutting speed, feed rate, and axial depth of cut by using Response Methodology Method (RSM). Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) has been applied to resolve the problem, and the results have been analyzed.

Abstract: Glass materials play a vital role in advancement of science and technology. They have found wide spread application in the industry, in laboratory equipment and in micro-gas turbines. Due to their low fracture toughness they are very difficult to machine, moreover there are the chip depositions on the machined surface which affects surface finish under ductile mode cutting conditions. In this research, high speed end milling of soda lime glass is performed on CNC vertical milling machine to investigate the effects of machining parameters i.e. spindle speed, depth of cut, and feed rate on machined surface roughness. Design of experiments was performed following Central Composite Design (CCD) of Response Surface Methodology (RSM). Design Expert Software was used for generating the empirical mathematical model for average surface roughness. The model’s validity was tested to 95% confidence level by Analysis of Variance (ANOVA). Subsequent experimental results showed that the developed mathematical model could successfully describe the performance indicators, i.e. surface roughness, within the controlled limits of the factors that were considered.

Abstract: An experimental study of high speed machining of soda lime glass using directional compressed air blowing for removal of the ductile chips from the machined surface, is presented. High speed end milling of soda lime glass is performed on a vertical CNC milling machine to observe the effects of machining parameters i.e. spindle speed, depth of cut and feed rate on the resultant surface roughness. The design of the experiments was performed following the Central Composite Design (CCD) of the Response Surface methodology (RSM) using the Design Expert Software. Optimization of machining parameters was conducted using desirability function of the Design Expert software based on minimum surface roughness criterion. Finally, experimental verification tests were conducted to validate the predicted optimized value.

Abstract: One of the major technological parameters in metal cutting is surface roughness. It is an unavoidable, and often, unwanted by-product of all machining operations, especially end milling. Surface roughness is directly related to product quality and performance, operation cost, machining accuracy, and chatter. Today’s stringent customer requirements demand machined parts with minimum (mirror finished) products. Hence, the prediction, modeling, and optimization of surface roughness is a quintessential concern in machining research and industrial endeavor. This research was undertaken in order to determine whether end milling of medium carbon steel performed using chosen ranges of cutting parameter and under magnetic field generated by permanent magnets could effectively improve surface roughness. A small central composite design with five levels and an alpha value of 1.4142, in Response Surface Methodology, was used in developing the relationship between cutting speed, feed, and depth of cut, with surface roughness. Design-Expert 6.0 software was utilized to develop the quadratic empirical mathematical model for surface roughness. The experiments were performed under two different conditions. The first condition was cutting under normal conditions, while the other one was cutting under the application of magnetic fields from two permanent magnets. Medium carbon steel was used as the work material. The resultant average surface roughness was found to be reduced by a maximum of 59% due to magnet application.

Abstract: Chatter phenomenon is a major issue as it greatly affects the topography of machined parts. Due to the inconsistent character of chatter, it is extremely difficult to predict resultant surface roughness in a machining process, such as end milling. Also, recent studies have shown that chatter can be suitably damped using magnetic fields. This paper, thus, focuses on a novel approach of minimizing surface roughness in end milling of Mild (Low Carbon) Steel using uncoated WC-Co inserts under magnetic field from permanent magnets. In this experiment, Response Surface Methodology (RSM) approach using DESIGN EXPERT 6.0 (DOE) software was used to design the experiments. The experiments were performed under two different cutting conditions. The first one was cutting under normal conditions, while the other was cutting under the application of magnetic fields from two permanent magnets positioned on opposite sides of the cutter. Surface roughness was measured using Mitutoyo SURFTEST SV-500 profilometer. The subsequent analysis showed that surface roughness was significantly reduced (by as much as 67.21%) when machining was done under the influence of magnetic field. The experimental results were then used to develop a second order empirical mathematical model equation for surface roughness and validated to 95% confidence level by using ANOVA. Finally, desirability function approach was used to optimize the surface roughness within the limiting values attainable in end milling.

Abstract: Cryogenic cooling is an environmental friendly and effective cooling technology. The present work investigates the role of cryogenic cooling on tool wear at various cutting conditions. An indigenous method of applying cryogenic coolant was developed. Different methods of circulating the liquid nitrogen and vaporizing it inside the tool holder were tried with the objective of lowering down the temperature at the cutting zone. This was achieved through more efficient heat transfer. As-rolled AISI 4340 steel shafts were turned using coated cemented carbide inserts. Results of tool wear show improvement of tool life during cryo-machining as compared to dry machining especially when heavy depths of cut, high feed values and high cutting speeds are employed. The effect of cryogenic cooling was not pronounced at low speeds, small feed and depths of cut.