A Study on the Optimal Machining Parameters of the Induction Assisted Milling with Inconel 718

Kim, Eun Jung; Lee, Choon-Man

doi:10.3390/ma12020233

Cited by 38 publications

(14 citation statements)

References 31 publications

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“…In addition, they have implemented the hybrid modeling to extract better machining optimization results. Kim and Lee [13] optimized the induction-assisted milling process using finite element analysis, signal-to-noise ratio, and analysis of variance. The optimized responses were surface roughness, tool wear, and surface roughness in selected machining environment.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing

et al. 2019

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Recently, the concept of smart manufacturing systems urges for intelligent optimization of process parameters to eliminate wastage of resources, especially materials and energy. In this context, the current study deals with optimization of hard-turning parameters using evolutionary algorithms. Though the complex programming, parameters selection, and ability to obtain the global optimal solution are major concerns of evolutionary based algorithms, in the present paper, the optimization was performed by using efficient algorithms i.e., teaching–learning-based optimization and bacterial foraging optimization. Furthermore, the weighted sum method was used to transform the diverse responses into a single response, and then multi-objective optimization was performed using the teaching–learning-based optimization method and the standard bacterial foraging optimization method. Finally, the optimum results reported by these methods are compared to choose the best method. In fact, owing to better convergence within shortest time, the teaching–learning-based optimization approach is recommended. It is expected that the outcome of this research would help to efficiently and intelligently perform the hard-turning process under automatic and optimized environment.

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Section: Introductionmentioning

confidence: 99%

Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing

et al. 2019

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“…The experimental design proposed by the Taguchi method uses orthogonal arrays to organize the parameters that affect the process and varies the levels of those parameters. The Taguchi approach values the importance of the logic of the parameters and has a strong effect compared to actual experiments [28,29,30,31,32]. The Taguchi method used signal to noise (S/N) as a quality characteristic.…”

Section: Laser-assisted Machiningmentioning

confidence: 99%

A Study on the Laser-Assisted Machining of Carbon Fiber Reinforced Silicon Carbide

2019

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In recent years, as replacements for traditional manufacturing materials, monolithic ceramics and carbon fiber reinforced silicon carbide (C/SiC) ceramic matrix composites have seen significantly increased usage due to their superior characteristics of relatively low density, lightweight, and good high temperature mechanical properties. Demand for difficult-to-cut materials is increasing in a variety of area such as the automotive and aerospace industries, but these materials are inherently difficult to process because of their high hardness and brittleness. When difficult-to-cut materials are processed by conventional machining, tool life and quality are reduced due to the high cutting force and temperatures. Laser-assisted machining (LAM) is a method of cutting a workpiece by preheating with a laser heat source and lowering the strength of the material. LAM has been studied by many researchers, but studies on LAM of carbon–ceramic composites have been carried out by only a few researchers. This paper focuses on deducing the optimal machining parameters in the LAM of C/SiC. In this study, the Taguchi method is used to obtain the major parameters for the analysis of cutting force and surface roughness under various machining conditions. Before machining experiments, finite element analysis is performed to determine the effective depth of the cut. The cutting parameters for the LAM operation are the depth of cut, preheating temperature, feed rate, and spindle speed. The signal to noise (S/N) ratio and variance analysis (ANOVA) of the cutting force and surface roughness are analyzed, and the response optimization method is used to suggest the optimal machining parameters.

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“…A very common type of energy assisted machining is the laser assisted machining as can be seen in a research conducted by Kim et al [ 16 ]. In another study of Kim et al [ 17 ], induction assisted milling of Inconel 718 has been reported and optimized process parameters are proposed. For the same analogy, plasma has been used as an assisting source of energy to machine Ti 6Al 4V, and the process is termed as plasma-assisted machining [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Achieving the Minimum Roughness of Laser Milled Micro-Impressions on Ti 6Al 4V, Inconel 718, and Duralumin

et al. 2020

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Titanium-aluminium-vanadium (Ti 6Al 4V) alloys, nickel alloys (Inconel 718), and duraluminum alloys (AA 2000 series) are widely used materials in numerous engineering applications wherein machined features are required to having good surface finish. In this research, micro-impressions of 12 µm depth are milled on these materials though laser milling. Response surface methodology based design of experiment is followed resulting in 54 experiments per work material. Five laser parameters are considered naming lamp current intensity (I), pulse frequency (f), scanning speed (V), layer thickness (LT), and track displacement (TD). Process performance is evaluated and compared in terms of surface roughness through several statistical and microscopic analysis. The significance, strength, and direction of each of the five laser parametric effects are deeply investigated for the said alloys. Optimized laser parameters are proposed to achieve minimum surface roughness. For the optimized combination of laser parameters to achieve minimum surface roughness (Ra) in the titanium alloy, the said alloy consists of I = 85%, f = 20 kHz, V = 250 mm/s, TD = 11 µm, and LT = 3 µm. Similarly, optimized parameters for nickel alloy are as follows: I = 85%, f = 20 kHz, V = 256 mm/s, TD = 8 µm, and LT = 1 µm. Minimum roughness (Ra) on the surface of aluminum alloys can be achieved under the following optimized parameters: I = 75%, f = 20 kHz, V = 200 mm/s, TD = 12 µm, and LT = 3 µm. Micro-impressions produced under optimized parameters have surface roughness of 0.56 µm, 2.46 µm, and 0.54 µm on titanium alloy, nickel alloy, and duralumin, respectively. Some engineering applications need to have high surface roughness (e.g., in case of biomedical implants) or some desired level of roughness. Therefore, validated statistical models are presented to estimate the desired level of roughness against any laser parametric settings.

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A Study on the Optimal Machining Parameters of the Induction Assisted Milling with Inconel 718

Cited by 38 publications

References 31 publications

Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing

Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing

A Study on the Laser-Assisted Machining of Carbon Fiber Reinforced Silicon Carbide

Achieving the Minimum Roughness of Laser Milled Micro-Impressions on Ti 6Al 4V, Inconel 718, and Duralumin

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