Optimal cutting parameters estimation to improve surface finish in turning operation in AISI 1045 using Taguchi's robust design

2013 ◽  
Vol 15 (1) ◽  
pp. 19 ◽  
Author(s):  
V. Suresh Babu ◽  
Rajakannu Amuthakkannan ◽  
S. Sriram Kumar ◽  
A. Muruganandam
Keyword(s):  
Robust Design ◽  
Surface Finish ◽  
Cutting Parameters ◽  
Parameters Estimation ◽  
Turning Operation ◽  
Aisi 1045 ◽  
Optimal Cutting Parameters ◽  
Improve Surface Finish

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.

scholarly journals Multi Objective Optimization for Turning Operation using Hybrid Extreme Learning Machine and Multi Objective Genetic Algorithm

2018 ◽  
Vol 7 (4.35) ◽  
pp. 876
Author(s):  
Tiagrajah V. Janahiraman ◽  
Nooraziah Ahmad
Keyword(s):  
Genetic Algorithm ◽  
Extreme Learning Machine ◽  
Cutting Parameters ◽  
Numerical Control ◽  
Machining Process ◽  
Turning Operation ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Learning Machine ◽  
Optimal Cutting Parameters

Turning operation, a type of machining process using Computer Numerical Control (CNC) machine in which a cutting tool, typically a non-rotary tool bit, moves to describe a helix toolpath while the cylindrical metal workpiece rotates. Numerous conflicting performance functions such as maximizing material removal rate, minimizing the product’s quality, maximizing the tool life and others, remains crucial for a system to optimize in order to obtain optimum benefit. The machinist is required to assign the optimal cutting parameters in CNC turning machine which have direct influence on the performance of each cutting process and machined product. It is very crucial for optimal parameters selection to maximize the performance function. A new optimisation model has been proposed in this paper. This model, uses Box Behnken Design (BBD) for design of experiment and the prediction model has been developed using Extreme Learning Machine (ELM) which is tuned using Particle Swarm Optimization. A powerful and effective, Multi Objective Genetic Algorithm (MOGA) will act as an optimizer of the developed model. Turning input parameters such as feed rate, cutting speed and depth of cut were considered as input variables and surface roughness, specific power consumption and cutting force were used as output variables. This novel approach, BBD-ELM-PSO-MOGA can predict the optimal cutting parameters as demonstrated in our case studies with less number of tunable parameters and number of experiments. Therefore, it is fast, less time consuming and easy to be implemented. 

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 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.

Effect of Cutting Parameters on Surface Roughness in Turning of Bone

2013 ◽  
Vol 845 ◽  
pp. 708-712 ◽  
Author(s):  
P.Y.M. Wibowo Ndaruhadi ◽  
S. Sharif ◽  
M.Y. Noordin ◽  
Denni Kurniawan
Keyword(s):  
Surface Roughness ◽  
Bone Tissue ◽  
Influencing Factor ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
Machining Process ◽  
Turning Operation ◽  
Cutting Direction

Surface roughness indicates the damage of the bone tissue due to bone machining process. Aiming at inducing the least damage, this study evaluates the effect of some cutting conditions to the surface roughness of machined bone. In the turning operation performed, the variables are cutting speed (26 and 45 m/min), feed (0.05 and 0.09 mm/rev), tool type (coated and uncoated), and cutting direction (longitudinal and transversal). It was found that feed did not significantly influence surface roughness. Among the influencing factor, the rank is tool type, cutting speed, and cutting direction.

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