scholarly journals Model Based on an Effective Material Removal Rate to Evaluate the Specific Energy Consumption in Grinding

2019 ◽  
Author(s):  
A. Nápoles Alberro ◽  
H.A González Rojas ◽  
A.J. Sánchez Egea ◽  
S. Hameed ◽  
R.M. Peña Aguilar
Keyword(s):  
Energy Consumption ◽  
Material Removal Rate ◽  
Specific Energy ◽  
Chip Formation ◽  
Material Removal ◽  
Removal Rate ◽  
Specific Energy Consumption ◽  
Industrial Scale ◽  
Depth Of Cut ◽  
Grinding Process

The energy efficiency of grinding depends on the appropriate selection of cutting conditions, grinding wheel and workpiece material. Additionally, the estimation of specific energy consumption is a good indicator to control the energy consumed during the grinding process. Consequently, this study develops a model of material removal rate to estimate the specific energy consumption based on the measurement of active power consumed in a plane surface grinding of C45K with different thermal treatments and AISI 304. This model identifies and evaluates the power dissipated by sliding, ploughing and chip formation in a industrial-scale grinding process. Furthermore, the instantaneous positions of the abrasive grains during cutting are described to study the material removal rate. The estimation of specific chip formation energy is similar to that described by other authors in laboratory scale, which allows to validate the model and experiments. Finally, the results show that the energy consumed by sliding is the main phenomenon of energy dissipation in industrial-scale grinding process, where it is denoted that sliding energy by volume unity decreases as the depth of cut and speed of workpiece increase.

scholarly journals Model Based on an Effective Material-Removal Rate to Evaluate Specific Energy Consumption in Grinding

Materials ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 939 ◽  
Author(s):  
Amelia Nápoles Alberro ◽  
Hernán González Rojas ◽  
Antonio Sánchez Egea ◽  
Saqib Hameed ◽  
Reyna Peña Aguilar
Keyword(s):  
Energy Consumption ◽  
Material Removal Rate ◽  
Specific Energy ◽  
Chip Formation ◽  
Material Removal ◽  
Removal Rate ◽  
Specific Energy Consumption ◽  
Industrial Scale ◽  
Depth Of Cut ◽  
Grinding Process

Grinding energy efficiency depends on the appropriate selection of cutting conditions, grinding wheel, and workpiece material. Additionally, the estimation of specific energy consumption is a good indicator to control the consumed energy during the grinding process. Consequently, this study develops a model of material-removal rate to estimate specific energy consumption based on the measurement of active power consumed in a plane surface grinding of C45K with different thermal treatments and AISI 304. This model identifies and evaluates the dissipated power by sliding, ploughing, and chip formation in an industrial-scale grinding process. Furthermore, the instantaneous positions of abrasive grains during cutting are described to study the material-removal rate. The estimation of specific chip-formation energy is similar to that described by other authors on a laboratory scale, which allows to validate the model and experiments. Finally, the results show that the energy consumed by sliding is the main mechanism of energy dissipation in an industrial-scale grinding process, where it is denoted that sliding energy by volume unity decreases as the depth of cut and the speed of the workpiece increase.

Optimization of Cutting Parameters in Turning of AISI 1018 Steel With Constant Material Removal Rate Using Robust Design for Minimizing Cutting Power

2013 ◽  
Author(s):  
C. Camposeco-Negrete ◽  
J. Calderón-Nájera ◽  
J. C. Miranda-Valenzuela
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Robust Design ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Optimization Of Cutting Parameters

Environmental and energy efficiency awareness of manufacturers and customers along with high electricity costs have promoted the development of strategies to reduce energy consumption in manufacturing processes. Machine tools are one of the main contributors to energy consumption in the industrial sector. Several studies have been undertaken to optimize the cutting parameters in order to minimize the power consumed in the removal of material. However, these studies do not consider the influence that different combinations of cutting parameters can have on power consumption at a constant material removal rate, quantity that has a direct influence in production rates. This paper describes an experimental study of AISI 1018 steel turning under roughing conditions and constant material removal rate, in order to obtain the cutting parameters that minimize power consumption. Robust design is used to analyze the effects of the depth of cut, feed rate and cutting speed on electric power consumed.

Influence of Cutting Parameters on Energy Consumption and Material Removal Rate in Turning Process

2015 ◽  
Vol 799-800 ◽  
pp. 282-290
Author(s):  
Fredrick Joseph Otim ◽  
Seong Joo Choi
Keyword(s):  
Energy Consumption ◽  
Mild Steel ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Grey Relational

This paper presents a novel approach for the optimization of machining parameters on turning of Mild Steel alloy with multiple responses based on orthogonal array with grey relational analysis. Experiments are conducted on mild steel alloy. Turning tests are carried out using coated carbide insert under dry cutting condition. In this work, turning parameters such as cutting speed, feed rate and depth of cut are optimized considering the multiple responses such as Energy Consumption (EC), and Material Removal Rate (MRR). A grey relational grade (GRG) of 0.746 is determined from the grey analysis for experimental run 27 meaning the control factors of this combination exhibit a stronger relationship with the response variables. Therefore, a spindle speed of 440 rpm, a feed rate of 0.24 mm/rev, and a depth of cut of 0.75 mm is the optimal parameter combination for the turning operation. The order of importance determined for the controllable factors to the Energy Consumption, in sequence, is the feed rate, spindle speed and depth of cut; while order to the Material Removal Rate, in sequence is depth of cut, feed rate and spindle speed. Optimum levels of parameters have been identified based on the values of grey relational grade and then finally, it was observed through ANOVA that the feed rate is the most influential and significant control factor among the three cutting parameters when turning mild steel in the conventional lathe tool, in order to minimize Energy Consumption and maximize Material Removal Rate.

Effects of Process Parameters on Surface Roughness and MRR in the hard turning of EN 36 steel

2018 ◽  
pp. 102-110
Author(s):  
Amritpal Singh ◽  
Rakesh Kumar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Hard Turning ◽  
Control Parameter ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Dry Cutting

In the present study, Experimental investigation of the effects of various cutting parameters on the response parameters in the hard turning of EN36 steel under the dry cutting condition is done. The input control parameters selected for the present work was the cutting speed, feed and depth of cut. The objective of the present work is to minimize the surface roughness to obtain better surface finish and maximization of material removal rate for better productivity. The design of experiments was done with the help of Taguchi L9 orthogonal array. Analysis of variance (ANOVA) was used to find out the significance of the input parameters on the response parameters. Percentage contribution for each control parameter was calculated using ANOVA with 95 % confidence value. From results, it was observed that feed is the most significant factor for surface roughness and the depth of cut is the most significant control parameter for Material removal rate.

Performance Analysis of Trihexyltetradecylphosphonium Chloride Ionic Fluid under MQL Condition in Hard turning

2020 ◽  
Vol 17 (1) ◽  
pp. 7629-7647
Author(s):  
A. Pandey ◽  
R. Kumar ◽  
A. K. Sahoo ◽  
A. Paul ◽  
A. Panda
Keyword(s):  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Coconut Oil ◽  
Chip Morphology ◽  
Weight Fraction ◽  
Cutting Fluid ◽  
Depth Of Cut

The current research presents an overall performance-based analysis of Trihexyltetradecylphosphonium Chloride [[CH3(CH2)5]P(Cl)(CH2)13CH3] ionic fluid mixed with organic coconut oil (OCO) during turning of hardened D2 steel. The application of cutting fluid on the cutting interface was performed through Minimum Quantity Lubrication (MQL) approach keeping an eye on the detrimental consequences of conventional flood cooling. PVD coated (TiN/TiCN/TiN) cermet tool was employed in the current experimental work. Taguchi’s L9 orthogonal array and TOPSIS are executed to analysis the influences, significance and optimum parameter settings for predefined process parameters. The prime objective of the current work is to analyze the influence of OCO based Trihexyltetradecylphosphonium Chloride ionic fluid on flank wear, surface roughness, material removal rate, and chip morphology. Better quality of finish (Ra = 0.2 to 1.82 µm) was found with 1% weight fraction but it is not sufficient to control the wear growth. Abrasion, chipping, groove wear, and catastrophic tool tip breakage are recognized as foremost tool failure mechanisms. The significance of responses have been studied with the help of probability plots, main effect plots, contour plots, and surface plots and the correlation between the input and output parameters have been analyzed using regression model. Feed rate and depth of cut are equally influenced (48.98%) the surface finish while cutting speed attributed the strongest influence (90.1%). The material removal rate is strongly prejudiced by cutting speed (69.39 %) followed by feed rate (28.94%) whereas chip reduction coefficient is strongly influenced through the depth of cut (63.4%) succeeded by feed (28.8%). TOPSIS significantly optimized the responses with 67.1 % gain in closeness coefficient.

Optimization of Cutting Parameters in Milling Process Using Genetic Algorithm and ANOVA (March 2020)

2020 ◽  
Vol 38 (10A) ◽  
pp. 1489-1503
Author(s):  
Marwa Q. Ibraheem
Keyword(s):  
Genetic Algorithm ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Optimal Value ◽  
Optimization Of Cutting Parameters

In this present work use a genetic algorithm for the selection of cutting conditions in milling operation such as cutting speed, feed and depth of cut to investigate the optimal value and the effects of it on the material removal rate and tool wear. The material selected for this work was Ti-6Al-4V Alloy using H13A carbide as a cutting tool. Two objective functions have been adopted gives minimum tool wear and maximum material removal rate that is simultaneously optimized. Finally, it does conclude from the results that the optimal value of cutting speed is (1992.601m/min), depth of cut is (1.55mm) and feed is (148.203mm/rev) for the present work.

scholarly journals Optimization of Laser Engraving of Acrylic Plastics from the Perspective of Energy Consumption, CO2 Emission and Removal Rate

2021 ◽  
Vol 5 (3) ◽  
pp. 78
Author(s):  
Mohammad Muhshin Aziz Khan ◽  
Shanta Saha ◽  
Luca Romoli ◽  
Mehedi Hasan Kibria
Keyword(s):  
Energy Consumption ◽  
Production Rate ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Scanning Speed ◽  
Gas Emissions ◽  
Laser Engraving ◽  
Co2 Gas ◽  
Optimal Set

This paper focuses on optimizing the laser engraving of acrylic plastics to reduce energy consumption and CO2 gas emissions, without hindering the production and material removal rates. In this context, the role of laser engraving parameters on energy consumption, CO2 gas emissions, production rate, and material removal rate was first experimentally investigated. Grey–Taguchi approach was then used to identify an optimal set of process parameters meeting the goal. The scan gap was the most significant factor affecting energy consumption, CO2 gas emissions, and production rate, whereas, compared to other factors, its impact on material removal rate (MRR) was relatively lower. Moreover, the defocal length had a negligible impact on the response variables taken into consideration. With this laser-process-material combination, to achieve the desired goal, the laser must be focused on the surface, and laser power, scanning speed, and scan gap must be set at 44 W, 300 mm/s, and 0.065 mm, respectively.

Process Design and Optimization of end Milling Parameters of Al 7075 Metal Matrix Composite

2021 ◽  
pp. 929-937
Author(s):  
D. S. Sai Ravi Kiran ◽  
Alavilli Sai Apparao ◽  
Vempala GowriSankar ◽  
Shaik Faheem ◽  
Sheik Abdul Mateen ◽  
...  
Keyword(s):  
Tool Wear ◽  
Material Removal Rate ◽  
Metal Matrix ◽  
Material Removal ◽  
Hybrid Composites ◽  
End Milling ◽  
Removal Rate ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Weight Percentage

This paper investigates the machinability characteristics of end milling operation to yield minimum tool wear with the maximum material removal rate using RSM. Twenty-seven experimental runs based on Box-Behnken Design of Response Surface Methodology (RSM) were performed by varying the parameters of spindle speed, feed and depth of cut in different weight percentage of reinforcements such as Silicon Carbide (SiC-5%, 10%,15%) and Alumina (Al2O3-5%) in alluminium 7075 metal matrix. Grey relational analysis was used to solve the multi-response optimization problem by changing the weightages for different responses as per the process requirements of quality or productivity. Optimal parameter settings obtained were verified through confirmatory experiments. Analysis of variance was performed to obtain the contribution of each parameter on the machinability characteristics. The result shows that spindle speed and weight percentage of SiC are the most significant factors which affect the machinability characteristics of hybrid composites. An appropriate selection of the input parameters such as spindle speed of 1000 rpm, feed of 0.02 mm/rev, depth of cut of 1 mm and 5% of SiC produce best tool wear outcome and a spindle speed of 1838 rpm, feed of 0.04 mm/rev, depth of cut of 1.81 mm and 6.81 % of SiC for material removal rate.

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