Optimization of Cutting Parameters for Desirable Surface Roughness in End-Milling Hardened AISI H13 Steel under a Certain Metal Removal Rate

2011 ◽  
Vol 188 ◽  
pp. 307-313 ◽  
Author(s):  
Tong Chao Ding ◽  
Song Zhang ◽  
Z.M. Li ◽  
Yuan Wei Wang
Keyword(s):  
Surface Roughness ◽  
Metal Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Metal Removal Rate ◽  
H13 Steel ◽  
Orthogonal Experiments ◽  
Aisi H13

In this paper, the orthogonal experiments and the optimization experiments with the same metal removal rate are designed to investigate the main effects and primary interaction of cutting parameters on surface roughness and to search the optimal cutting parameter under a certain removal rate when end-milling hardened AISI H13 steel with the PVD coated carbide insert. The empirical model for surface roughness based on the orthogonal experiments and the optimization experiments with the same metal removal rate and the optimal cutting parameter were all verified. Under a certain metal removal rate, the combination of high cutting speed, small axial depth of cut and high feed, small radial depth of cut generates the best surface roughness in hard milling of AISI H13.

Effect Analysis and ANN Prediction of Surface Roughness in End Milling AISI H13 Steel

2014 ◽  
Vol 800-801 ◽  
pp. 590-595
Author(s):  
Qing Zhang ◽  
Song Zhang ◽  
Jia Man ◽  
Bin Zhao
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Experimental Results ◽  
Depth Of Cut ◽  
H13 Steel ◽  
Aisi H13 Steel ◽  
Radial Depth ◽  
Aisi H13

Surface roughness has a significant effect on the performance of machined components. In the present study, a total of 49 end milling experiments on AISI H13 steel are conducted. Based on the experimental results, the signal-to-noise (S/N) ratio is employed to study the effects of cutting parameters (axial depth of cut, cutting speed, feed per tooth and radial depth of cut) on surface roughness. An ANN predicting model for surface roughness versus cutting parameters is developed based on the experimental results. The testing results show that the proposed model can be used as a satisfactory prediction for surface roughness.

Optimization of MRR and Surface Roughness of AlMg3 (AA5754) Alloy in CNC Lathe Machine by Using Taguchi Method

2018 ◽  
Vol 877 ◽  
pp. 110-117 ◽  
Author(s):  
Poornesh Kumar Chaturvedi ◽  
Harendra Kumar Narang ◽  
Atul Kumar Sahu
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Metal Removal ◽  
Removal Rate ◽  
Surface Condition ◽  
Point Of View ◽  
Depth Of Cut ◽  
Metal Removal Rate ◽  
Cnc Lathe ◽  
Lathe Machine

Quality of the product is the major concern in manufacturing industries from customers as well as producers point of view. There are number of factors in the product such as surface condition, height, weight, length, width etc., which may be consider for the measurement of the quality. Surface roughness and Metal Removal Rate (MRR) are the two main outcomes on which numerous researchers have applied different approaches for several years to get optimum results. In this study, Taguchi Method is applied for getting optimum parameters settings for Surface roughness and Metal Removal Rate (MRR) in case of turning AlMg3 (AA5754) in CNC Lathe machine, which is an aluminum alloy having diameter 20 mm and length 100 mm. The three parameters i.e. spindle speed, feed rate and depth of cut with 3 levels are taken as the process variables and the working ranges of these parameters for conducting experiments are selected based on Taguchi’s L9 Orthogonal Array (OA) design. To analyze the significant process parameters; main effect plots for data means and for S/N ratio are generated using Minitab statistical software.

Optimization of Cutting Parameters for Improving Surface Roughness during Hard Milling of AISI H13 Steel

2020 ◽  
Vol 831 ◽  
pp. 35-39 ◽  
Author(s):  
The Vinh Do ◽  
Quoc Manh Nguyen ◽  
Minh Tan Pham
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
H13 Steel ◽  
Hard Milling ◽  
Aisi H13 Steel ◽  
Cooling Conditions ◽  
Aisi H13

In metal cutting, surface roughness plays an important role in assessing the quality of processed products. The roughness depends greatly on the selection of machining parameters such as cooling conditions and cutting parameters. For this purpose, cooling conditions including dry, MQL, and Silica-based nanofluid MQL as well as cutting parameters including cutting speed, depth-of-cut and feed-rate were investigated to determine their influence on machining roughness during hard milling of AISI H13 steel. The DOE method developed by G. Taguchi was used to design the experiments. An analysis of the signal-to-noise response and ANOVA were carried to obtain the optimal values of cutting parameters for minimizing surface roughness. The results of the present study show that Silica-based nanofluid MQL, minimum feed-rate, minimum depth-of-cut, and maximum cutting speed is an optimal cutting condition for reducing machining roughness.

An Experimental Study of Cutting Forces in Ball-End Milling of AISI H13

2013 ◽  
Vol 690-693 ◽  
pp. 2403-2407
Author(s):  
Tong Chao Ding
Keyword(s):  
Cutting Forces ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
H13 Steel ◽  
Hard Milling ◽  
Radial Depth ◽  
Aisi H13 ◽  
Coated Carbide

In the present study, an attempt has been made to experimentally investigate the effects of the cutting parameters on cutting forces in hard milling of AISI H13 steel with coated carbide tools. Designed based on Taguchi method, four factor (cutting speed, feed, radial depth of cut and axial depth of cut) four level orthogonal experiments were conducted. Three components of cutting forces were measured during hard milling experiments and then variance analysis was performed. Finally, the linear regression model was established.

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.

Quantification of tool chatter and metal removal rate using wavelet denoising and statistical approach

2018 ◽  
Vol 49 (2) ◽  
pp. 62-81 ◽  
Author(s):  
Shailendra Kumar ◽  
Bhagat Singh
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Metal Removal ◽  
Removal Rate ◽  
Statistical Significance ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Metal Removal Rate ◽  
Chatter Index ◽  
Tool Chatter

Tool chatter is an unavoidable phenomenon encountered in machining processes. Acquired raw chatter signals are contaminated with various types of ambient noises. Signal processing is an efficient technique to explore chatter as it eliminates unwanted background noise present in the raw signal. In this study, experimentally recorded raw chatter signals have been denoised using wavelet transform in order to eliminate the unwanted noise inclusions. Moreover, effect of machining parameters such as depth of cut ( d), feed rate ( f) and spindle speed ( N) on chatter severity and metal removal rate has been ascertained experimentally. Furthermore, in order to quantify the chatter severity, a new parameter called chatter index has been evaluated considering aforesaid denoised signals. A set of 15 experimental runs have been performed using Box–Behnken design of experiment. These experimental observations have been used to develop mathematical models for chatter index and metal removal rate considering response surface methodology. In order to check the statistical significance of control parameters, analysis of variance has been performed. Furthermore, more experiments are conducted and these results are compared with the theoretical ones in order to validate the developed response surface methodology model.

Multiresponse Optimization of Al Alloy-SiC Composite Machining Parameters for Minimum Tool Wear and Maximum Metal Removal Rate

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Rajesh Kumar Bhushan
Keyword(s):  
Tool Wear ◽  
Metal Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Al Alloy ◽  
Machining Parameters ◽  
Metal Removal Rate ◽  
Nose Radius ◽  
Multiresponse Optimization

Optimization in turning means determination of the optimal set of the machining parameters to satisfy the objectives within the operational constraints. These objectives may be the minimum tool wear, the maximum metal removal rate (MRR), or any weighted combination of both. The main machining parameters which are considered as variables of the optimization are the cutting speed, feed rate, depth of cut, and nose radius. The optimum set of these four input parameters is determined for a particular job-tool combination of 7075Al alloy-15 wt. % SiC (20–40 μm) composite and tungsten carbide tool during a single-pass turning which minimizes the tool wear and maximizes the metal removal rate. The regression models, developed for the minimum tool wear and the maximum MRR were used for finding the multiresponse optimization solutions. To obtain a trade-off between the tool wear and MRR the, a method for simultaneous optimization of the multiple responses based on an overall desirability function was used. The research deals with the optimization of multiple surface roughness parameters along with MRR in search of an optimal parametric combination (favorable process environment) capable of producing desired surface quality of the turned product in a relatively lesser time (enhancement in productivity). The multi-objective optimization resulted in a cutting speed of 210 m/min, a feed of 0.16 mm/rev, a depth of cut of 0.42 mm, and a nose radius of 0.40 mm. These machining conditions are expected to respond with the minimum tool wear and maximum the MRR, which correspond to a satisfactory overall desirability.

Investigation of Surface Roughness and Material Removal Rate for High Speed Micro End Milling on PMMA

2015 ◽  
Vol 1115 ◽  
pp. 12-15
Author(s):  
Nur Atiqah ◽  
Mohammad Yeakub Ali ◽  
Abdul Rahman Mohamed ◽  
Md. Sazzad Hossein Chowdhury
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
High Speed ◽  
End Milling ◽  
Removal Rate ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Micro End Milling

Micro end milling is one of the most important micromachining process and widely used for producing miniaturized components with high accuracy and surface finish. This paper present the influence of three micro end milling process parameters; spindle speed, feed rate, and depth of cut on surface roughness (Ra) and material removal rate (MRR). The machining was performed using multi-process micro machine tools (DT-110 Mikrotools Inc., Singapore) with poly methyl methacrylate (PMMA) as the workpiece and tungsten carbide as its tool. To develop the mathematical model for the responses in high speed micro end milling machining, Taguchi design has been used to design the experiment by using the orthogonal array of three levels L18 (21×37). The developed models were used for multiple response optimizations by desirability function approach to obtain minimum Ra and maximum MRR. The optimized values of Ra and MRR were 128.24 nm, and 0.0463 mg/min, respectively obtained at spindle speed of 30000 rpm, feed rate of 2.65 mm/min, and depth of cut of 40 μm. The analysis of variance revealed that spindle speeds are the most influential parameters on Ra. The optimization of MRR is mostly influence by feed rate. Keywords:Micromilling,surfaceroughness,MRR,PMMA

Improvement of Surface Roughness in End Milling of Ti6Al4V by Coupling RSM with Genetic Algorithm

2011 ◽  
Vol 264-265 ◽  
pp. 1154-1159
Author(s):  
Anayet Ullah Patwari ◽  
A.K.M. Nurul Amin ◽  
S. Alam
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Titanium Alloys ◽  
End Milling ◽  
Fitness Function ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Resistance Surface

Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematically the effect of cutting parameters on Surface roughness in end milling of Ti6Al4V. 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. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The developed RSM is coupled as a fitness function with genetic algorithm to predict the optimum cutting conditions leading to the least surface roughness value. MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value.

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