Optimization of cutting parameters to minimize energy consumption during turning of AISI 1018 steel at constant material removal rate using robust design

2015 ◽  
Vol 83 (5-8) ◽  
pp. 1341-1347 ◽  
Author(s):  
Carmita Camposeco-Negrete ◽  
Juan de Dios Calderón Nájera ◽  
José Carlos Miranda-Valenzuela
Keyword(s):  
Energy Consumption ◽  
Robust Design ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Optimization Of Cutting Parameters

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.

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 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.

scholarly journals A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Material Removal Rate ◽  
Cutting Forces ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Data Driven ◽  
Optimal Cutting Parameters

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

Effects of Cutting Parameters on the Surface Roughness of Ti6Al4V Titanium Alloys in Side Milling

2011 ◽  
Vol 175 ◽  
pp. 289-293 ◽  
Author(s):  
Hao Liu ◽  
Chong Hu Wu ◽  
Rong De Chen
Keyword(s):  
Surface Roughness ◽  
Titanium Alloys ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Cutting Depth ◽  
Side Milling ◽  
Milling Parameters ◽  
Fine Grain

Side milling Ti6Al4V titanium alloys with fine grain carbide cutters is carried out. The influences of milling parameters on surface roughness are investigated and also discussed with average cutting thickness, material removal rate and vibration. The results reveal that the surface roughness increases with the increase of average cutting thickness and is primarily governed by the radial cutting depth.

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.

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.

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