Optimal selection of operating parameters in end milling of Al-6061 work materials using multi-objective approach

Shaik, Jakeer Hussain; Srinivas, J.

doi:10.1186/s40759-017-0020-6

Cited by 28 publications

(16 citation statements)

References 25 publications

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“…The surface plots of 3D model are provided with two factors at the same time. These fixed levels of the response factors provide the interaction information effects the two input factors, which helps to optimize level for each variable to obtain the maximum response [19].…”

Section:   mentioning

confidence: 99%

Predictive Modeling of Surface Roughness Based on Response Surface Methodology after WAAM

Tian¹,

Lu²,

Li³

et al. 2019

Proceedings of the 2019 International Conference on Electronical, Mechanical and Materials Engineering (ICE2ME 2019)

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A hybrid technique of additive manufacturing and subtractive process has provided a new solution combining product design and control by software. Wire Arc Additive Manufacturing (WAAM) process wins well-respected because of its low cost and high efficiency of deposition, nevertheless the process has its limitation of high heat input and low forming accuracy. A new process of additive manufacturing with high efficiency of modeling is urgent needed which can control heat transfer, mass transfer and force transfer. To overcome the disadvantage upon, various hybrid manufacturing techniques have been developed with high efficiency and controlled modeling in recently. The machining process in hybrid manufacturing has more different characteristics from traditional material removal processes, such as residual stress and heat in the blank. These influence the whole efficiency of the hybrid manufacturing. The essential task of this paper is to explore the feasibility of thermal machining during this process and make rational use of AM in order to obtain optimal accuracy.

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Section:   mentioning

confidence: 99%

Predictive Modeling of Surface Roughness Based on Response Surface Methodology after WAAM

Tian¹,

Lu²,

Li³

et al. 2019

Proceedings of the 2019 International Conference on Electronical, Mechanical and Materials Engineering (ICE2ME 2019)

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“…Individuals with best characteristics constitute the best solution to the problem; (5) this process continues in a repetitive manner until convergence criteria are met, i.e., the chromosomes have the best fitness or potential (optimum) solution for a specific problem is obtained. Immediately after the new generation is created, it is further assessed and checked through experimentation for the conformability and agreement (Shaik and Srinivas 2017).…”

Section: Optimization Using Genetic Algorithmmentioning

confidence: 99%

Parametric optimization of Nd:YAG laser microgrooving on aluminum oxide using integrated RSM-ANN-GA approach

2018

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Nowadays in highly competitive precision industries, the micromachining of advanced engineering materials is extremely demand as it has extensive application in the fields of automobile, electronic, biomedical and aerospace engineering. The present work addresses the modeling and optimization study on dimensional deviations of square-shaped microgroove in laser micromachining of aluminum oxide (Al 2 O 3) ceramic material with pulsed Nd:YAG laser by considering the air pressure, lamp current, pulse frequency, pulse width and cutting speed as process parameters. Thirty-two sets of laser microgrooving trials based on central composite design (CCD) design of experiments (DOEs) are performed, and response surface method (RSM), artificial neural network (ANN) and genetic algorithm (GA) are subsequently applied for mathematical modeling and multi-response optimization. The performance of the predictive ANN model based on 5-8-8-3 architecture gave the minimum error (MSE = 0.000099) and presented highly promising to confidence with percentage error less than 3% in comparison with experimental result data set. The ANN model combined with GA leads to minimum deviation of upper width, lower width and depth value of − 0.0278 mm, 0.0102 mm and − 0.0308 mm, respectively, corresponding to optimum laser microgrooving process parameters such as 1.2 kgf/cm 2 of air pressure, 19.5 Amp of lamp current, 4 kHz of pulse frequency, 6% of pulse width and 24 mm/s of cutting speed. Finally, the results have been verified by performing a confirmatory test.

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“…e authors used stir cast Alumina reinforced Aluminum Metal Matrix Composites (AAMMC) and found that the cutting speed (1750 rev/min), feed rate (0.3 mm/rev), and depth of cut (0.2 mm) are the optimum parameters for end milling operations. Shaik and Srinivas used the neural network-based RSM to find the optimum process parameter combination [10] for end milling operations in AA-6061 work material using a multiobjective approach. e authors found that the cutting speed of 1800.954 rpm, feed rate of 41.826 mm/min, and axial depth of 0.799 mm are the optimum combination for machining the AA-6061 material.…”

Section: Introductionmentioning

confidence: 99%

Statistical Modelling to Study the Implications of Coated Tools for Machining AA 2014 Using Grey Taguchi‐Based Response Surface Methodology

Manoharan

Kulandaivel²,

Arunagiri

et al. 2021

Advances in Materials Science and Engineering

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Milling is the surface machining process by removing material from the raw stock using revolving cutters. This process accounts for a major stake in most of the Original Equipment Manufacturing (OEM) industries. This paper discusses optimizing process parameters for machining the AA 2014 T 651 using a vertical milling machine with coated cutting tools. The process parameters such as cutting speed, depth of cut, and type of the cutting tool with all its levels are identified from the previous literature study and several trial experiments. The Taguchi L9 Orthogonal Array (OA) is used for the experimental order with the chosen input parameters. The commonly used cutting tools in the machining industry, such as High-Speed Steel (HSS) and its coated tools, are considered in this study. These tools are coated with Titanium Nitride (TiN) and Titanium Aluminum Nitride (TiAlN) by Physical Vapor Deposition (PVD) technique. The output responses such as cutting forces along the three-axis are measured using a milling tool dynamometer for the corresponding input factors. The input process parameters are optimized by considering the output responses such as MRR, machining torque, and thrust force. Grey Taguchi-based Response Surface Methodology (GTRSM) is used for multiobjective multiresponse optimization problems to find the optimum input process parameter combination for the desired response. Polynomial regression equations are generated to understand the mathematical relation between the input factor and output responses as well as Grey Relational Grade (GRG) values. The optimum process parameter combination from the desirability analysis is the HSS tool coated with TiAlN at a cutting speed of 270 rpm and a depth of cut value of 0.2 mm.

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Optimal selection of operating parameters in end milling of Al-6061 work materials using multi-objective approach

Cited by 28 publications

References 25 publications

Predictive Modeling of Surface Roughness Based on Response Surface Methodology after WAAM

Predictive Modeling of Surface Roughness Based on Response Surface Methodology after WAAM

Parametric optimization of Nd:YAG laser microgrooving on aluminum oxide using integrated RSM-ANN-GA approach

Statistical Modelling to Study the Implications of Coated Tools for Machining AA 2014 Using Grey Taguchi‐Based Response Surface Methodology

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