Machinability Study of Developed Composite AA6061-ZrO2 and Analysis of Influence of MQL

James, Johny; Annamalai, A. Raja

doi:10.3390/met8070472

Cited by 16 publications

(7 citation statements)

References 23 publications

(18 reference statements)

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“…Consequently, the force is significantly reduced [ 1 ]. For the roughness parameter, the main factors that influence it are the feed rate, the cutting speed, the depth of cut, the vibrations of the cutting tools, the lubricant conditions, and the tool specifications [ 19 ]. In this study, it was found that the feed rate and a p were the variables that most influenced roughness.…”

Section: Resultsmentioning

confidence: 99%

“…Since heat generated during machining is a major problem, it is important to choose an adequate lubrication/cooling condition, to improve the machinability of a difficult-to-cut material [ 12 , 18 ]. The minimum quantity lubrication (MQL) is an attractive alternative solution for floods, which have occurred for decades and bring health risks to workers [ 17 , 19 ], and for dry machining, which can accelerate tool wear and the degradation of piece surface integrity [ 11 ]. This technique has also been used more often, due to pressure from governments, law enforcement, and environmental agencies that have compelled manufacturers to adopt it [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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MQL Strategies Applied in Ti-6Al-4V Alloy Milling—Comparative Analysis between Experimental Design and Artificial Neural Networks

et al. 2020

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This paper presents a study of the Ti-6Al-4V alloy milling under different lubrication conditions, using the minimum quantity lubrication approach. The chosen material is widely used in the industry due to its properties, although they present difficulties in terms of their machinability. A minimum quantity lubrication (MQL) prototype valve was built for this purpose, and machining followed a previously defined experimental design with three lubrication strategies. Speed, feed rate, and the depth of cut were considered as independent variables. As design-dependent variables, cutting forces, torque, and roughness were considered. The desirability optimization function was used in order to obtain the best input data indications, in order to minimize cutting and roughness efforts. Supervised artificial neural networks of the multilayer perceptron type were created and tested, and their responses were compared statistically to the results of the factorial design. It was noted that the variables that most influenced the machining-dependent variables were the feed rate and the depth of cut. A lower roughness value was achieved with MQL only with the use of cutting fluid with graphite. Statistical analysis demonstrated that artificial neural network and the experimental design predict similar results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MQL Strategies Applied in Ti-6Al-4V Alloy Milling—Comparative Analysis between Experimental Design and Artificial Neural Networks

et al. 2020

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“…The present Special Issue includes six scientific papers (Koklu and Basmaci [1], Razak et al [2], Russo Spena [3], Toulfatzis et al [4], Abbas et al [5], James and Annamalai [6]) mainly covering metal machinability analyzed by experimental means. In order of publication:…”

Section: Contributionsmentioning

confidence: 99%

“…James and Annamalai [6] studied the machinability of the developed composite AA6061-ZrO 2 and analyzed the influence of MQL (minimum quantity lubricant) on the cutting process.…”

mentioning

confidence: 99%

Metals Machining—Recent Advances in Experimental and Modeling of the Cutting Process

Haddag

2018

Metals

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“…In addition, it has been concluded that supplements with lubricating properties such as graphite reduce friction, improve surface quality, and facilitate machinability [6]. Johny Jamesand S. and Raja Annamalai A. stated that there is a linear relationship between the minimum amount of lubrication and surface roughness and tool wear, and it has no significant effect on cutting force [7]. Ravindra Nath Yadav et al [8] reported the results of numerous studies on turning metal matrix composites.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the tool wear, surface roughness, and chip formation in the machining of ZrO2-reinforced aluminum composites

Pul

2020

J Braz. Soc. Mech. Sci. Eng.

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The aim of this study is to reveal how ZrO 2 (zirconia) contributes to the machinability of aluminum 1050. In the first stage of this study, composite materials were produced by the vortex method by adding different amounts of ZrO 2 (5%, 10%, 20%, and 30%) into commercial aluminum of 99.5% purity. Then, microstructure images of composite materials were taken under the scanning electron microscope (SEM), and the hardness of these composite materials was measured. In the last stage, the machinability tests of the composite materials were performed on the lathe under dry machining conditions at 125, 175, and 225 m/min cutting speeds and 0.03-0.06 and 0.12 mm/rev feed rates, with 1.5 mm constant depth of cut. Uncoated cementite carbide cutting tool inserts were used in machining tests. SEM images of the cutting tool inserts were taken, and the roughness values of the machined surfaces were measured. Chip samples were taken and investigated. It was observed that all the cutting tools had a Built-Up Edge (BUE) formation. Surface roughness and BUE formation increased as the feed rates were increased and decreased with increasing cutting speed. The roughness values decreased slightly and then increased again depending on the ZrO 2 ratio. As the feed rates increased, a transition from ribbon chip type to the helical and saw-toothed forms was observed. It was also observed that the chip formation changed depending on the amount of ZrO 2 in the composite structure.

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Machinability Study of Developed Composite AA6061-ZrO2 and Analysis of Influence of MQL

Cited by 16 publications

References 23 publications

MQL Strategies Applied in Ti-6Al-4V Alloy Milling—Comparative Analysis between Experimental Design and Artificial Neural Networks

MQL Strategies Applied in Ti-6Al-4V Alloy Milling—Comparative Analysis between Experimental Design and Artificial Neural Networks

Metals Machining—Recent Advances in Experimental and Modeling of the Cutting Process

Investigation of the tool wear, surface roughness, and chip formation in the machining of ZrO2-reinforced aluminum composites

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