The Experts below are selected from a list of 28914 Experts worldwide ranked by ideXlab platform
Carmita Camposeconegrete - One of the best experts on this subject based on the ideXlab platform.
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optimization of Cutting parameters using response surface method for minimizing energy consumption and maximizing Cutting quality in turning of aisi 6061 t6 aluminum
Journal of Cleaner Production, 2015Co-Authors: Carmita CamposeconegreteAbstract:Abstract Modern production is faced with the challenge of reducing the environmental impacts related to machining processes. Machine tools consume large amounts of energy and, as a consequence, environmental impacts are generated owing to this consumption. Many studies have been carried out in order to minimize Cutting Power, Cutting energy or Power consumed by the machine tool. Nevertheless, the response variables mentioned before do not take into account the energy required by all the components inside the machine tool during the Cutting operation. This paper presents an experimental study related to the optimization of Cutting parameters in roughing turning of AISI 6061 T6 aluminum. Energy consumption and surface roughness were minimized, while the material removal rate of the process was maximized. A set of experimental runs was established using a Central Composite Design, and the Response Surface Method was employed to obtain the regression model for the energy consumed during machining, specific energy, surface roughness and material removal rate. The adequacy of the model was proved by Analysis of Variance analysis. The relationship between Cutting parameters and the response variables (energy consumption, surface roughness and material removal rate) was analyzed using contour plots. Moreover, the desirability method was used to define the values of the variables that achieved a minimum quantity of specific energy consumed and minimum surface roughness. Feed rate and depth of cut were the most significant factors for minimizing the total specific energy consumed, and for minimizing the surface roughness, feed rate was the most significant factor. Compared to the traditional objective optimization, the optimal turning parameters determined by the proposed optimization method reduced the energy consumption in 14.41%, and the surface roughness in 360.47%. Consequently, sustainability and quality of the machining process were achieved at the same time.
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optimization of Cutting parameters for minimizing energy consumption in turning of aisi 6061 t6 using taguchi methodology and anova
Journal of Cleaner Production, 2013Co-Authors: Carmita CamposeconegreteAbstract:Machine tools are responsible for environmental impacts owing to their energy consumption. Cutting parameters have been optimized to minimize Cutting Power, Power consumed or Cutting energy. However, these response variables do not consider the energy demand that ensures the readiness of the machine tool. The present paper outlines an experimental study to optimize Cutting parameters during turning of AISI 6061 T6 under roughing conditions in order to get the minimum energy consumption. An orthogonal array, signal to noise (S/N) ratio and analysis of variance (ANOVA) were employed to analyze the effects and contributions of depth of cut, feed rate and Cutting speed on the response variable. A comparison was done to highlight the importance of correctly selecting the response variable to be analyzed, due to the difference of the values of Cutting parameters needed to optimize Cutting Power, Cutting energy, Power consumed and energy consumed during the machining process. Additional, the relationship between Cutting parameters, energy consumption, and surface roughness was analyzed in order to determine the levels of the Cutting parameters that lead to minimum energy consumption and minimum surface roughness. The results of this research work showed that feed rate is the most significant factor for minimizing energy consumption and surface roughness. Nevertheless, the level of this factor needed to achieve minimum energy consumption is not the same as the one needed to obtain minimum surface roughness. Higher feed rate provides minimum energy consumption but will lead to higher surface roughness.
Fei Liu - One of the best experts on this subject based on the ideXlab platform.
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a method for predicting the energy consumption of the main driving system of a machine tool in a machining process
Journal of Cleaner Production, 2015Co-Authors: Fei Liu, Jun Xie, Shuang LiuAbstract:The machining systems that mainly consist of machine tools are numerous and are used in a wide range of applications in industry, which usually exhibit very low energy efficiency; as a result, they have great potential for energy savings and environmental emissions reduction. To achieve such energy savings, the prediction of the energy consumption of the machining process has great significance. Also, it can provide a decision-support tool for the establishment of an energy consumption quota, the energy-saving optimization of Cutting parameters, energy efficiency evaluation, and so on. Although existing researches on the energy consumption prediction of machine tools have been performed, a practical method is still lacking. Therefore, a new method for predicting the energy consumption of the main driving system of a machine tool in a machining process is proposed. First, a machining process is divided into three types of periods: start-up periods, idle periods and Cutting periods. Second, the energy consumption prediction models for each type of period and the total prediction model for the machining process are established. Third, by measuring energy consumption data of the start-up and idle processes at discrete speeds, the functions of the fitted curves of the energy consumption of start-up periods and idle periods are obtained, which enables the energy consumption of the start-up period and the idle period at any different speed to be predicted. Fourth, using the Cutting Power calculated based on the machining parameters and the additional loss coefficients obtained based on the additional loss coefficients equation set, the energy consumption of the Cutting periods can be predicted. Finally, the prediction error analysis model is constructed, and the reasons why the error is not big in the prediction are expounded. The results of a case study indicate that the method is practical and has good application prospect.
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an on line approach for energy efficiency monitoring of machine tools
Journal of Cleaner Production, 2012Co-Authors: Fei LiuAbstract:Machining processes cause measurable impacts on environment due to substantial amounts of energy consumption. Enhancing energy efficiency of machine tools can significantly improve the environmental performance of machining systems. For this reason, an on-line energy efficiency monitoring system is necessary. Most conventional approaches monitored the energy efficiency by directly measuring Cutting Power with torque sensors or dynamometers. In contrast, we propose a new on-line approach without using any torque sensor or dynamometer which leads to a decreased implementation cost. The energy efficiency monitoring model of this approach is constructed based on an energy consumption model of machine tool. Then the entire machine-tool energy consumption can be divided into two parts, i.e. constant energy consumption and variable energy consumption. The former is measured in advance and stored in database, and the latter is derived from Cutting Power that can be estimated on-line according to Power balance equation and additional load loss function. The additional load loss function can be identified off-line through input Power and Cutting Power of the machine-tool spindle. Several experiments are performed on a CNC machine tool CJK6136 and the results show the effectiveness of the proposed method.
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characteristics of additional load losses of spindle system of machine tools
Journal of Advanced Mechanical Design Systems and Manufacturing, 2010Co-Authors: H U Shaohua, Fei Liu, I PengAbstract:Energy consumption of machine tool has drawn wide attention in recent years. The additional load losses of machine tools are of great importance for investigating the energy consumption of machine tools because those account for 15-20% of the Cutting Power and may even be up to nearly 30% of the Cutting Power in our researches. For lack of adequate understanding of the characteristics of additional load losses in the past, the additional load losses coefficient, defined as the ratio of additional load losses to Cutting Power, was regarded as a constant while the spindle speed was unchanged. However, it is discovered in our practical measurements that it is not so. In this paper, it proposes an additional load losses model based on Power flow model, under the condition of the slip of spindle motor being small, in order to fully understand the characteristics of additional load losses. The characteristics of additional load losses include the relationship between additional load losses and Cutting Power, the relationship between additional load losses and spindle speed, and the relationship between additional load losses and Cutting torque. Further more, an experimental system is developed to acquire the additional load losses through measuring Cutting torque, spindle speed and input Power of machine tool. As an example, several experiments are carried out on the CNC lathe by adjusting Cutting parameters including spindle speed, feed rate and Cutting depth. The experimental results show that the additional load losses coefficient varies with spindle speed and Cutting torque, which can be fitted by a 1st order polynomial.
X M Zhao - One of the best experts on this subject based on the ideXlab platform.
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a Cutting Power model for tool wear monitoring in milling
International Journal of Machine Tools & Manufacture, 2004Co-Authors: H Shao, H L Wang, X M ZhaoAbstract:Abstract This paper describes a Cutting Power model in face milling operation, where Cutting conditions and average tool flank wear are taken into account. The Cutting Power model is verified with experiments. It is shown with the simulations and experiments that the simulated Power signals predict the mean Cutting Power better than the instantaneous Cutting Power. Finally, the Cutting Power model is used in a Cutting Power threshold updating strategy for tool wear monitoring which has been carried out successfully in milling operations under variable Cutting conditions.
D. V. Hutton - One of the best experts on this subject based on the ideXlab platform.
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On the closed form mechanistic modeling of milling: Specific Cutting energy, torque, and Power
Journal of Materials Engineering and Performance, 1994Co-Authors: A. E. Bayoumi, G. Yücesan, D. V. HuttonAbstract:Specific energy in metal Cutting, defined as the energy expended in removing a unit volume of workpiece material, is formulated and determined using a previously developed closed form mechanistic force model for milling operations. Cutting Power is computed from the Cutting torque, Cutting force, kinematics of the cutter, and the volumetric material removal rate. Closed form expressions for specific Cutting energy were formulated and found to be functions of the process parameters: pressure and friction for both rake and flank surfaces and chip flow angle at the rake face of the tool. Friction is found to play a very important role in Cutting torque and Power. Experiments were carried out to determine the effects of feedrate, Cutting speed, workpiece material, and flank wear land width on specific Cutting energy. It was found that the specific Cutting energy increases with a decrease in the chip thickness and with an increase in flank wear land.
Shuang Liu - One of the best experts on this subject based on the ideXlab platform.
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a method for predicting the energy consumption of the main driving system of a machine tool in a machining process
Journal of Cleaner Production, 2015Co-Authors: Fei Liu, Jun Xie, Shuang LiuAbstract:The machining systems that mainly consist of machine tools are numerous and are used in a wide range of applications in industry, which usually exhibit very low energy efficiency; as a result, they have great potential for energy savings and environmental emissions reduction. To achieve such energy savings, the prediction of the energy consumption of the machining process has great significance. Also, it can provide a decision-support tool for the establishment of an energy consumption quota, the energy-saving optimization of Cutting parameters, energy efficiency evaluation, and so on. Although existing researches on the energy consumption prediction of machine tools have been performed, a practical method is still lacking. Therefore, a new method for predicting the energy consumption of the main driving system of a machine tool in a machining process is proposed. First, a machining process is divided into three types of periods: start-up periods, idle periods and Cutting periods. Second, the energy consumption prediction models for each type of period and the total prediction model for the machining process are established. Third, by measuring energy consumption data of the start-up and idle processes at discrete speeds, the functions of the fitted curves of the energy consumption of start-up periods and idle periods are obtained, which enables the energy consumption of the start-up period and the idle period at any different speed to be predicted. Fourth, using the Cutting Power calculated based on the machining parameters and the additional loss coefficients obtained based on the additional loss coefficients equation set, the energy consumption of the Cutting periods can be predicted. Finally, the prediction error analysis model is constructed, and the reasons why the error is not big in the prediction are expounded. The results of a case study indicate that the method is practical and has good application prospect.
