Comparison of Optimum Cutting Parameters for AISI1042 in Turning Operation by Genetic Algorithm and Particle Swarm Optimization

2015 ◽  
Vol 813-814 ◽  
pp. 285-292 ◽  
Author(s):  
A. Hemantha Kumar ◽  
G. Subba Rao ◽  
T. Rajmohan
Keyword(s):  
Surface Finish ◽  
Metal Cutting ◽  
Cutting Parameters ◽  
Output Parameter ◽  
Machining Process ◽  
Depth Of Cut ◽  
Optimum Level ◽  
Skilled Operator ◽  
Optimal Cutting Parameters ◽  
Selection Of

In metal cutting surface finish is a crucial output parameter in determining the quality of the product. Good surface finish not only assures quality, but also reduces manufacturing cost. Surface finish is an important parameter in terms of tolerances, it reduces assembly time and avoids the need for secondary operation, thus reduces operation time and leads to overall cost reduction. It is very important to select optimum parameters in metal operations. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The main aim of the present work is to build a model to solve real world optimization problems in manufacturing processes.The selection of optimal cutting parameters are speed, feed and depth of cut. are important for all machining process. Experiments have been designed using Taguchi technique, dry and single pass turning of AISI No. 1042 (EN-41B) steel with cermet insert tool performed on PSG A141 lathe. By using signal to noise (S/N) ratio and Analysis of variance (ANOVA) are performed to find the optimum level and percentage of contribution of each parameter. A mathematical model is developed using regression analysis for surface roughness and the model is validated.Moreover, the proposed algorithm, namely GA and PSO were utilized to optimize the output parameter Rain terms of cutting speed, feed and depth of cut by using MATLAB.

OPTIMISING CUTTING PARAMETERS FOR HOT TURNING ON EN31 MATERIAL

2021 ◽  
Vol 5 (10) ◽  
Author(s):  
Prof. Hemant k. Baitule ◽  
Satish Rahangdale ◽  
Vaibhav Kamane ◽  
Saurabh Yende
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
Multiple Time ◽  
Turning Process ◽  
Workpiece Material ◽  
Hot Turning

In any type of machining process the surface roughness plays an important role. In these the product is judge on the basis of their (surface roughness) surface finish. In machining process there are four main cutting parameter i.e. cutting speed, feed rate, depth of cut, spindle speed. For obtaining good surface finish, we can use the hot turning process. In hot turning process we heat the workpiece material and perform turning process multiple time and obtain the reading. The taguchi method is design to perform an experiment and L18 experiment were performed. The result is analyzed by using the analysis of variance (ANOVA) method. The result Obtain by this method may be useful for many other researchers.

Optimal Cutting Parameters for Precision Machining Process

2010 ◽  
Vol 431-432 ◽  
pp. 381-384
Author(s):  
Qing Hua Song ◽  
Wei Xiao Tang ◽  
Xing Ai ◽  
Yi Wan
Keyword(s):  
Cutting Parameters ◽  
Machining Process ◽  
Precision Machining ◽  
Depth Of Cut ◽  
Stable Limit ◽  
Finite Dimensional ◽  
Stability Chart ◽  
Dimensional State Space ◽  
And Performance ◽  
Optimal Cutting Parameters

Semi-discretization method is applied to construct stability chart and performance contour in the parametric space for milling processes. The method creates a mapping of the system responses in a finite dimensional state space. Based on the discipline of that, the smaller the largest absolute value (μmax) of the characteristic multipliers of the mapping is, the faster the system converges to zero, minimization of μmax leads to optimal stable limit. The optimal limits are obtained by using stability chart and performance contours. Additional, a novel analytical method for selection of optimal depth of cut (axial depth of cut) is presented. An example of 2-DOF down-milling model is employed to demonstrate the method. It is shown that the spindle speeds corresponding to the optimal depths of cut locate the left side of the resonant spindle speeds, and the optimal cutting parameters pair (spindle speed and depth of cut) can be used to offer high finishing accuracy in precision machining.

Prediction of Surface Roughness in High Speed Machining of Inconel 718

2011 ◽  
Vol 264-265 ◽  
pp. 1193-1198
Author(s):  
Mokhtar Suhaily ◽  
A.K.M. Nurul Amin ◽  
Anayet Ullah Patwari
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Inconel 718 ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
High Speed Machining

Surface finish and dimensional accuracy is one of the most important requirements in machining process. Inconel 718 has been widely used in the aerospace industries. High speed machining (HSM) is capable of producing parts that require little or no grinding/lapping operations within the required machining tolerances. In this study small diameter tools are used to achieve high rpm to facilitate the application of low values of feed and depths of cut to investigate better surface finish in high speed machining of Inconel 718. This paper describes mathematically the effect of cutting parameters on Surface roughness in high speed end milling of Inconel 718. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. Machining were performed using CNC Vertical Machining Center (VMC) with a HES510 high speed machining attachment in which using a 4mm solid carbide fluted flat end mill tool. Wyko NT1100 optical profiler was used to measure the definite machined surface for obtaining the surface roughness data. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to predict the surface roughness value with in the specified cutting conditions limit.

Experimental Investigation of Surface Roughness in High Speed Turning of Hardened AISI P20 Steel

2011 ◽  
Vol 418-420 ◽  
pp. 1228-1231 ◽  
Author(s):  
Bo Di Cui
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
High Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
High Speed Turning ◽  
Aisi P20 ◽  
Optimal Cutting Parameters ◽  
Aisi P20 Steel

Surface roughness is one of the most important product quality characteristics. In this paper, experimental investigation of surface roughness was performed in high speed turning of hardened AISI P20 steel with CBN tool based on design of experiment. The influence of cutting speed, feed rate, depth of cut and nose radius on surface roughness were assessed using analysis of variance (ANOVA). Optimal cutting parameters were found to improve the machining performance. Due to the complexity of machining process, artificial neural network (ANN) was employed to develop the predictive model of surface roughness. Simulations were done to study the relationship between surface roughness and cutting parameters based on the proposed model.

scholarly journals Optimal Machining Parameters for Achieving the Desired Surface Roughness in Turning of Steel

2012 ◽  
Vol 9 (1) ◽  
pp. 37 ◽  
Author(s):  
LB Abhang ◽  
M Hameedullah
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Metal Cutting ◽  
Cutting Speed ◽  
Cutting Conditions ◽  
Machining Process ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Engineering Sciences

 Due to the widespread use of highly automated machine tools in the metal cutting industry, manufacturing requires highly reliable models and methods for the prediction of output performance in the machining process. The prediction of optimal manufacturing conditions for good surface finish and dimensional accuracy plays a very important role in process planning. In the steel turning process the tool geometry and cutting conditions determine the time and cost of production which ultimately affect the quality of the final product. In the present work, experimental investigations have been conducted to determine the effect of the tool geometry (effective tool nose radius) and metal cutting conditions (cutting speed, feed rate and depth of cut) on surface finish during the turning of EN-31 steel. First and second order mathematical models are developed in terms of machining parameters by using the response surface methodology on the basis of the experimental results. The surface roughness prediction model has been optimized to obtain the surface roughness values by using LINGO solver programs. LINGO is a mathematical modeling language which is used in linear and nonlinear optimization to formulate large problems concisely, solve them, and analyze the solution in engineering sciences, operation research etc. The LINGO solver program is global optimization software. It gives minimum values of surface roughness and their respective optimal conditions. 

scholarly journals Computational Inteligence in Optimization of Machining Operation Parameters of ST-37 Steel

2012 ◽  
Vol 248 ◽  
pp. 456-461
Author(s):  
Abolfazl Golshan ◽  
Danial Ghodsiyeh ◽  
Soheil Gohari ◽  
Ayob Amran ◽  
B.T. Hang Tuah Baharudin
Keyword(s):  
Performance Optimization ◽  
High Performance ◽  
Cutting Speed ◽  
Optimization Procedure ◽  
Cutting Parameters ◽  
Rake Angle ◽  
Depth Of Cut ◽  
Optimal Cutting Parameters ◽  
Machining Characteristics ◽  
Selection Of

Optimal selection of cutting parameters is one of the significant issues in achieving high quality machining. In this study, a method for the selection of optimal cutting parameters during lathe operation is presented. The present study focuses on multiple-performance optimization on machining characteristics of St-37 steel. The cutting parameters used in this experimental study include cutting speed, feed rate, depth of cut and rake angle. Two output parameters, namely, surface roughness and tool life are considered as process performance. A statistical model based on linear polynomial equations is developed to describe different responses. For optimal conditions, the Non-dominated Sorting Genetic Algorithm (NSGA) is employed in achieving appropriate models. The optimization procedure shows that the proposed method has a high performance in problem-solving.

Machinability Improvement by Workpiece Preheating during End Milling AISI H13 Hardened Steel

2011 ◽  
Vol 264-265 ◽  
pp. 888-893
Author(s):  
Mokhtar Suhaily ◽  
A.K.M. Nurul Amin ◽  
Anayet Ullah Patwari
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Inconel 718 ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
High Speed Machining

Surface finish and dimensional accuracy is one of the most important requirements in machining process. Inconel 718 has been widely used in the aerospace industries. High speed machining (HSM) is capable of producing parts that require little or no grinding/lapping operations within the required machining tolerances. In this study small diameter tools are used to achieve high rpm to facilitate the application of low values of feed and depths of cut to investigate better surface finish in high speed machining of Inconel 718. This paper describes mathematically the effect of cutting parameters on Surface roughness in high speed end milling of Inconel 718. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. Machining were performed using CNC Vertical Machining Center (VMC) with a HES510 high speed machining attachment in which using a 4mm solid carbide fluted flat end mill tool. Wyko NT1100 optical profiler was used to measure the definite machined surface for obtaining the surface roughness data. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to predict the surface roughness value with in the specified cutting conditions limit.

scholarly journals Selection of Parameters and Strategies to Reduce Energy Consumption and Improve Surface Quality in EN-AW 7075 Molds Machining

Metals ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 688 ◽  
Author(s):  
Oscar Rodriguez-Alabanda ◽  
Maria Bonilla ◽  
Guillermo Guerrero-Vaca ◽  
Pablo Romero
Keyword(s):  
Surface Roughness ◽  
Energy Consumption ◽  
Surface Finish ◽  
Tool Path ◽  
Orthogonal Design ◽  
Electrical Energy ◽  
Cutting Parameters ◽  
Influential Factor ◽  
Depth Of Cut ◽  
Selection Of

The machining of cavities for blow molding is a long and costly process, with the objective of obtaining an excellent surface finish with the minimal possible electrical energy consumption (EEC). This work has studied which combination of cutting parameters and cutting strategies to use to achieve an optimum surface finish on the mold using the minimal associated EEC: in roughing operation, tool path strategy and axial depth of cut were studied; in finishing operation, tool path strategy, spindle-speed, feed-rate, and step-over were evaluated. Thirty-two molds were machined in blocks of aluminium alloy EN-AW 7075 T6 in a machining center of a three-axis, following an orthogonal design of experiments. The analysis of results demonstrates that: a roughing strategy has influence on the surface roughness on the bottom of the mold; a finishing strategy is an influential factor on the surface roughness on the walls of the mold; certain parameters have no relevance on the surface roughness but have an influence on the EEC; an adequate selection of cutting strategies and cutting parameters permit an improvement of surface roughness of up to 70%, and a reduction of 40% in EEC, compared to the less favorable tests.

Studying The Effect of a New Mixed Cutting Fluid on Surface Roughness

2020 ◽  
Vol 38 (11A) ◽  
pp. 1593-1601
Author(s):  
Mohammed H. Shaker ◽  
Salah K. Jawad ◽  
Maan A. Tawfiq
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Cutting Parameters ◽  
Machining Process ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Cutting Fluids ◽  
Machining Processes ◽  
Dry Cutting ◽  
Cutting Speeds

This research studied the influence of cutting fluids and cutting parameters on the surface roughness for stainless steel worked by turning machine in dry and wet cutting cases. The work was done with different cutting speeds, and feed rates with a fixed depth of cutting. During the machining process, heat was generated and effects of higher surface roughness of work material. In this study, the effects of some cutting fluids, and dry cutting on surface roughness have been examined in turning of AISI316 stainless steel material. Sodium Lauryl Ether Sulfate (SLES) instead of other soluble oils has been used and compared to dry machining processes. Experiments have been performed at four cutting speeds (60, 95, 155, 240) m/min, feed rates (0.065, 0.08, 0.096, 0.114) mm/rev. and constant depth of cut (0.5) mm. The amount of decrease in Ra after the used suggested mixture arrived at (0.21µm), while Ra exceeded (1µm) in case of soluble oils This means the suggested mixture gave the best results of lubricating properties than other cases.

Effects of Insert Nose Radius and Processing Cutting Parameter on the Surface Roughness of Aisi 316 Stainless Steel

2010 ◽  
Vol 447-448 ◽  
pp. 51-54
Author(s):  
Mohd Fazuri Abdullah ◽  
Muhammad Ilman Hakimi Chua Abdullah ◽  
Abu Bakar Sulong ◽  
Jaharah A. Ghani
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Finish ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Aisi 316

The effects of different cutting parameters, insert nose radius, cutting speed and feed rates on the surface quality of the stainless steel to be use in medical application. Stainless steel AISI 316 had been machined with three different nose radiuses (0.4 mm 0.8 mm, and 1.2mm), three different cutting speeds (100, 130, 170 m/min) and feed rates (0.1, 0.125, 0.16 mm/rev) while depth of cut keep constant at (0.4 mm). It is seen that the insert nose radius, feed rates, and cutting speed have different effect on the surface roughness. The minimum average surface roughness (0.225µm) has been measured using the nose radius insert (1.2 mm) at lowest feed rate (0.1 mm/rev). The highest surface roughness (1.838µm) has been measured with nose radius insert (0.4 mm) at highest feed rate (0.16 mm/rev). The analysis of ANOVA showed the cutting speed is not dominant in processing for the fine surface finish compared with feed rate and nose radius. Conclusion, surface roughness is decreasing with decreasing of the feed rate. High nose radius produce better surface finish than small nose radius because of the maximum uncut chip thickness decreases with increase of nose radius.

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