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.

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.

Research on Microscopic Grain-Workpiece Interaction in Grinding through Micro-Cutting Simulation, Part 2: Factorial Study

2009 ◽  
Vol 76-78 ◽  
pp. 15-20 ◽  
Author(s):  
Lan Yan ◽  
Xue Kun Li ◽  
Feng Jiang ◽  
Zhi Xiong Zhou ◽  
Yi Ming Rong
Keyword(s):  
Cutting Force ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Shear Angle ◽  
Depth Of Cut ◽  
Specific Cutting Force ◽  
Single Grain

The grinding process can be considered as micro-cutting processes with irregular abrasive grains on the surface of grinding wheel. Single grain cutting simulation of AISI D2 steel with a wide range of cutting parameters is carried out with AdvantEdgeTM. The effect of cutting parameters on cutting force, chip formation, material removal rate, and derived parameters such as the specific cutting force, critical depth of cut and shear angle is analyzed. The formation of chip, side burr and side flow is observed in the cutting zone. Material removal rate increases with the increase of depth of cut and cutting speed. Specific cutting force decreases with the increase of depth of cut resulting in size effect. The shear angle increases as the depth of cut and cutting speed increase. This factorial analysis of single grain cutting is adopted to facilitate the calculation of force consumption for each single abrasive grain in the grinding zone.

scholarly journals Optimization for an efficient and highly productive turning process

2021 ◽  
Vol 2 (1) ◽  
pp. 212-221
Author(s):  
Sonja Jozić ◽  
◽  
Dražen Bajić ◽  
Ivana Dumanić ◽  
Željko Bagavac ◽  
...  
Keyword(s):  
Power Consumption ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Process Efficiency ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Functional Requirements ◽  
Machining Processes

The required quality of the product arises from the customer preferences and functional requirements of the product and is determined mostly by the machining operation. Properly selected machining parameters in machining processes are of great importance for improving process efficiency and product quality. The aim of this paper is to find cutting parameters with which above mentioned process and product characteristics will be achieved. Experiments were performed according to Box-Behnken design of experiments. Influential input variables were cutting speed, feed per revolution and depth of cut and output variables were surface roughness, power consumption and material removal rate. Multi-objective optimization function was developed to find the machining parameters with which the lowest power consumption, the best surface quality and the greatest material removal rate will be achieved.

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.

scholarly journals Optimization of Dimensional Tolerances and Material Removal Rate in the Orthogonal Turning of AISI 4340 Steel

2021 ◽  
Author(s):  
Chathakudath Sukumaran Sumesh ◽  
Dawood Sheriff Akbar ◽  
Hari Shankar Purandharadass ◽  
Raghunandan J. Chandrasekaran
Keyword(s):  
Material Removal Rate ◽  
Material Removal ◽  
Metal Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Aisi 4340 Steel ◽  
4340 Steel ◽  
Aisi 4340

Turning is one of the most used metal removal operations in the industry. It can remove material faster, giving reasonably good surface quality apart from geometrical requirements. Conformity of geometry is one of the most significant requirements of turned components to perform their intended functions. Apart from dimensional requirements, the important geometrical necessities are Circularity, Straightness, Cylindricity, Perpendicularity, etc. Since they have a direct influence on the functioning of the components, the effect of the cutting parameters on them has greater significance. In this paper experiments are carried out to examine the effect of turning parameters such as cutting speed, feed rate, and depth of cut on responses like; straightness, roundness, surface roughness, and material removal rate during turning of AISI 4340 steel. Analysis of Variance (ANOVA) is performed and the influence of parameters on each response is studied. The optimal values of parameters obtained from the study are further confirmed by conducting experiments.

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.

Modelling and Optimization of End Milling Process Using TLBO and TOPSIS Algorithm

2017 ◽  
pp. 54-81 ◽  
Author(s):  
Atul Tiwari ◽  
Mohan Kumar Pradhan
Keyword(s):  
Material Removal Rate ◽  
Material Removal ◽  
Metal Cutting ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Depth Of Cut ◽  
Multi Objective ◽  
Order Preference

To assure desire quality of machined products at minimum machining costs and maximum material removal rate, it is very important to select optimum parameters when metal cutting machine tool are used. Minimum Surface Roughness (Ra) is commonly desirable for the component; however Material Removal Rate (MRR) should be maximized. This chapter presents an approach for determination of the best cutting parameters precede to minimum Ra and maximum MRR simultaneously by integrating Response Surface Methodology with Multi-Objective Technique for Order Preference by Similarity to Ideal Solution and Teaching and learning based optimization algorithm in face milling of Al-6061 alloy. 30 experiments have been conducted based on RSM with 4 parameters, namely Speed, Feed, Depth of Cut and Coolant Speed and three levels each. ANOVA is performed to find the most influential input parameters for both MRR and Ra. Later the multi-objective attribution selection method TOPSIS and multi objective optimization method TLBO is used to optimize the responses.

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.

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