Papers by Keyword: Machining Force

Abstract: In the present work hard turning of AISI 52100 steel has been performed using Polycrystalline cubic boron nitride (PCBN) tools. The input parameters considered are cutting speed, feed, depth of cut, nose radius and negative rake angle and the measured responses are machining force and workpiece surface temperature. Experiments are planned as per Central Composite Design (CCD) of Response Surface Methodology (RSM). The effect of input parameters and their interactions are discussed with main effects plot. Further, the multi-objective optimization scheme is proposed by adopting Grey Relational Analysis (GRA) coupled with Principle Component Analysis (PCA). Results demonstrated that speed is the most significant factor affecting the responses followed by negative rake angle, feed, depth of cut, and nose radius. The optimum cutting parameters obtained are cutting speed 1000 rpm, feed 0.02 mm/rev, depth of cut 0.5 mm, Nose radius 1 mm and Negative rake angle 5^o.

Abstract: The discrete element method (DEM) is applied to glass micromachining in this study. By three standard tests the discrete element model is established to match the main mechanical properties of glass. Then, indentating, cutting, micro milling process are simulated. Results show that the vertical damage depth is prevented from reaching the final machined surface in cutting process. Tool rake angle is the most remarkable factor influencing on the chip deformation and cutting force. The final machined surface is determined by the minimum cutting thickness per edge. Different cutting thickness, cutter shape and spindle speed largely effect on the mechanism of glass.

Abstract: Analysis of the cutting force and specific cutting pressure play vital roles in machining of the composite materials. The present experimental work describes the modeling of machining parameters using one of the soft computing techniques i.e. fuzzy logic for machining force and specific cutting pressure. The basic idea is to machine the carbon fiber reinforced plastic (CFRP) composite materials and measuring the cutting forces and then determining the machining force and specific cutting pressure. 27 experiments based on Taguchis L₂₇ orthogonal array were carried out involving three machining parameters, namely, cutting speed, feed and depth of cut, each defined at three levels. Subsequently the prediction models were developed using three different fuzzy logic membership functions, namely, triangular, trapezoidal and bell shape. It is found that the predicted values of proposed responses such as machining force and specific cutting pressure are very close to the experimental values within the chosen ranges of the process parameters. The statistical analysis using ANOVA on machining parameters are also presented and discussed. The machined surface analyzed through SEM images revealed the damages encountered during turning process.

Abstract: This work deals with optimization of multiple characteristics in CNC turning of reinforced Poly Ether Ether Ketone (PEEK CF30) with TiN coated tools. The considered criteria included surface roughness, machining force and cutting power. Three controllable factors of the turning process consisting of cutting speed, depth of cut and feed rate were incorporated. Taguchi design of experiments method was used to arrange the experimentation task. Artificial Neural Network approach was used for modeling the obtained responses. Then, desirability function approach was used to conduct single and multiple response optimizations.

Abstract: The deflection of thin-walled components during machining will seriously affect their machining accuracy and surface quality. This paper presents a state of art review for the research in error prediction and error compensation during machining of these components. Some key techniques, such as the overall scheme, the cutting stability, the machining force modeling methodologies, the machining deformation analysis methodologies based on FEA and NC compensation methodologies, are discussed. The problems which have been highlighted in the research papers are pointed out and some problems which should be further investigated are given.

Abstract: In this study, basic experiments involving machining using a rotational tool were conducted with the aim of increasing the volume of material removed rate in ductile-mode machining of Si wafers. The machining surface and machining force was compared to experimentally clarify the material removal process for a single cutting edge, the critical cutting thickness tc at which occurs of cracks was set as the machining condition. Then, the three machining modes were experimentally revealed. As the result, the ductile-mode machining surface was obtained that the total depth of cut was under less than 78.5μm on ductile-brittle-mode machining.

Abstract: In present study, neural networks have been employed for studying the ultrasonic vibration-assisted turning (UAT) process and for predicting the machining force and workpiece's surface roughness. Extensive experiments were carried out using different values of UAT parameters such as vibration amplitude, depth of cut, feed rate and cutting speed. The tests were implemented on the basis of full factorial design of experiments for three different levels of each UAT parameter. The machining force and workpiece's surface roughness were measured as the responses of the experiments and were subsequently modeled with the aid of back propagation multilayer perceptron neural network for 1.1191 steel. The nonlinear relation existing between the aforementioned UAT parameters and the machining force and workpiec's surface roughness could effectively be modeled by the developed networks and the responses error could be kept less than ten percent. This was verified by further experiments different from those carried out for developing the network.

Abstract: The single point cutting tool in ultrasonic vibration-assisted turning (UAT) is made to vibrate under ultrasonic frequency. In present study, the influence of various parameters such as vibration amplitude, depth of cut, feed rate and cutting velocity on the machining force and workpiece's surface roughness in UAT of Al7075 has been investigated. Full factorial experiments were carried out with an ultrasonic frequency range of 20±0.5 kHz. ANOVA was conducted on the experimental results and regression models were obtained for predicting the machining force, surface roughness and cutting temperature. The proposed models were verified by further experiments. The robustness of the proposed models was then investigated whence the optimal parameters were estimated. Similar full factorial experiments were also carried out with conventional turning (CT) in order to compare the results with those of UAT.