Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy

Aslantaş, Kubilay; Danish, Mohd; Hasçelik, Ahmet; Mia, Mozammel; Gupta, Munish Kumar; Ginta, Turnad Lenggo; Ijaz, Hassan

doi:10.3390/ma13132998

Cited by 51 publications

(17 citation statements)

References 31 publications

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“…Finally, it is worth mentioning the employment of techniques based on the DOE and regression techniques that have also been widely used for the study of technological variables found in manufacturing processes as shown in research studies such as that of Airao et al [54] which analyzed the effect of cutting speed, feed rate, and axial depth of cut on surface roughness obtained in end-milling of a stainless steel, that of Kasdekara et al [55], which employed a 2 4 full factorial (DOE) for determining the most important factors which influence MRR in Electro-chemical machining of AA6061 by using MLP and regression, and that of Ahmed et al [56] in which the MRR, in laser milling of three alloys (Ti6Al4V, Inconel 718 and AA 2024), was evaluated using the response surface method and DOE. On the other hand, Aslantas et al [57] obtained empirical relations between cutting speed, feed rate, depth of cut and surface roughness parameters using the RSM for the micro-turning process in a Ti6Al4V alloy and Su et al [58] employed a multi-objective optimization method based on grey relational analysis and RSM along with Taguchi method for analyzing surface roughness and MRR in turning of an AISI 304 austenitic stainless steel. Regression analysis is also used by Zajac et al [59] to make predictions of cutting tool durability in turning processes.…”

Section: State Of the Artmentioning

confidence: 99%

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

Pérez

2020

Mathematics

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In Manufacturing Engineering there is a need to be able to model the behavior of technological variables versus input parameters in order to predict their behavior in advance, so that it is possible to determine the levels of variation that lead to optimal values of the response variables to be obtained. In recent years, it has been a common practice to rely on regression techniques to carry out the above-mentioned task. However, such models are sometimes not accurate enough to predict the behavior of these response variables, especially when they have significant non-linearities. In this present study a comparative analysis between the precision of different techniques based on conventional regression and soft computing is initially carried out. Specifically, regression techniques, based on the response surface model, as well as the use of artificial neural networks and fuzzy inference systems along with adaptive neuro-fuzzy inference systems will be employed to predict the behavior of the aforementioned technological variables. It will be shown that when there are difficulties in predicting the response parameters by using regression models, soft computing models are highly effective, being much more efficient than conventional regression models. In addition, a new method is proposed in this study that consists of using an iterative process to obtain a fuzzy inference system from a design of experiments and then using an adaptive neuro-fuzzy inference system for tuning the constants of the membership functions. As will be shown, with this method it is possible to obtain improved results in the validation metrics. The means of selecting the membership functions to develop this model from the design of experiments is discussed in this present study in order to obtain an initial solution, which will be then tuned by using an adaptive neuro-fuzzy inference system, to predict the behavior of the response variables. Moreover, the obtained results will also be compared.

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Section: State Of the Artmentioning

confidence: 99%

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

Pérez

2020

Mathematics

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“…a) & (b) repersent the normal probalility curve for toughness and hardness and this plot is used to check the adquancy of model. as all points are in a straight line,it can concluded that model is adequate[16][17][18].…”

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confidence: 92%

Development of mathematical model and optimization of GMA welding parameters of IS 2062 grade A steel weldments

Rizvi

Ali

2021

Frattura Ed Integrità Strutturale

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In this experimental works, the effect of GMA welding process parameters, such as arc voltage, wire feed speed, and gas flow rate on the mechanical quality of IS 2062 structural steel of grade A has been studied. Process parameters play an important role in determining the weld quality. In this research work response surface methodology (RSM) technique via design expert (DOE) 12 version software was applied to determining the weld quality and also to develop a mathematical model that can predict the main effect of the above said parameters on weld quality i.e. toughness and hardness. A set of experiments has been conducted to collect the data using a central composite design and ANOVA was used to predict the impact of welding parameters on toughness and hardness and Comparison also made between the actual result and predicted value and from the result that is clear that toughness and hardness of weldment is significantly affected by arc voltage, wire feed speed, and follow by gas flow rate.

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“…The best surface with regard to roughness was reached at 150 m/min cutting speed and feed rate of 0.25 mm/rev. Machining of other steels, like the AISI 1040, was studied using "response surface methodology" (RSM) to find that tool wear and total machining time influence the surface roughness [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Investigation into the surface quality and stress corrosion cracking resistance of AISI 316L stainless steel via precision end-milling operation

Yasir¹,

Danish

Mia

et al. 2020

Int J Adv Manuf Technol

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This study presents a two-fold investigation on precision end-milling of stainless steel (AISI 316L). First, the impact of end-milling variables (cutting speed and feed rate) on the surface quality (surface roughness, microhardness, and surface morphology) was analyzed. The best surface quality with surface roughness (Ra) 0.65 ± 0.02 μm was observed for cutting speed of 140 m/min and 0.025 mm/tooth of feed rate. Microhardness was increased with increment in cutting speed. Second, the impact of surface roughness (Ra) on the stress corrosion cracking under two different mediums, i.e., body solutions (Hank’s solution) and 1 M hydrochloric acid solution, was studied. The investigations showed that the samples with higher surface roughness values were more prone to stress corrosion cracking.

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Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy

Cited by 51 publications

References 31 publications

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

Development of mathematical model and optimization of GMA welding parameters of IS 2062 grade A steel weldments

Investigation into the surface quality and stress corrosion cracking resistance of AISI 316L stainless steel via precision end-milling operation

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