Experimental data-set for prediction of tool wear during turning of Al-1061 alloy by high speed steel cutting tools

Okokpujie, Imhade P.; Ohunakin, Olayinka S.; Bolu, Christian; Okokpujie, Kennedy

doi:10.1016/j.dib.2018.04.003

Cited by 29 publications

(9 citation statements)

References 6 publications

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“…This proves that high cutting speed with low feed interactions leads to minimum cutting force during machining of Ti-6AL-4V-ELI alloy. Because the application of the nanoparticles in the mineral oil increases the mechanical property of the base oil, which significantly improves the machining condition during operation to reduce vibration and friction during the face-milling machining, this result is in line with [25,[31][32][33]. The vibration occurs due to the unwanted movement of the chips being removed from the workpiece in the machining process and can affect the workpiece during the machining operation.…”

Section: The Effects Of Cutting Speed Feed Rate and Depth-of-cut And Their Interaction On The Cutting Force Under The Three Cutting Condimentioning

confidence: 78%

Experimental analysis of cutting force during machining difficult to cut materials under dry, mineral oil, and TiO2 nano-lubricant

Okokpujie¹,

Tartibu²

2021

J. meas. eng.

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Difficult-to-machine materials, e.g., Titanium alloys, are highly applicable in diverse industries that yield strength and wear resistance. However, they prove difficult to machine due to high vibration, leading to high cutting forces during the machining process. This vibration occurs from chip discontinuity and thereby leads to high friction between the cutting tool and workpiece. In order to minimize these challenges, lubricants are employed in machining operations to reduce frictional and other unnecessary cutting forces and improve surface finish. This research focuses on studying the nano-lubricant effects in reducing cutting forces in the machining of TI-6AL-4V-ELI alloy. Also, carry out a comparative study of dry, mineral oil, and TiO2 nano-lubricant during face-milling machining for optimal performance. Additionally, the study develops a predictive mathematical model for cutting force using a Taguchi L9 orthogonal array. A two-step approach was employed to develop the nano-lubricant before the machining process. The dynamometer is used to collect the cutting force data at the end of each sample. The Results show that the lubrication conditions play a significant role in the reduction of cutting forces. The mineral oil-based-TiO2 nano-lubricant reduces the cutting force by 19 % compared with the mineral oil during the machining of TI-6AL-4V-ELI alloy. Furthermore, the optimal parameters to reduce cutting forces during face milling of TI-6AL-4V-ELI alloy are cutting speed at 3000 rpm, 200 mm/min feed rate, 0.3 mm depth of cut to obtain the minimum cutting force 30 (N). This study concludes that the application of TiO2 nanoparticles in mineral oil significantly improves the thermal and mechanical properties, which leads to a reduction of cutting force.

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Section: The Effects Of Cutting Speed Feed Rate and Depth-of-cut And Their Interaction On The Cutting Force Under The Three Cutting Condimentioning

confidence: 78%

Experimental analysis of cutting force during machining difficult to cut materials under dry, mineral oil, and TiO2 nano-lubricant

Okokpujie¹,

Tartibu²

2021

J. meas. eng.

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“…There is, however, the need to resolve the problem caused by vibration to enable manufacturers to have sustainable production of mechanical components for industrial use [14][15][16][17][18][19][20]. Therefore, this research work is focused on studying the variation of cutting speed and depth of cut on vibration frequency, when order parameters are kept constant.…”

Section: Experimental Analysis Of the Influence Of Depth Of Cut Time Of Cut And Machining Speed On Vibrationmentioning

confidence: 99%

Experimental Analysis of the Influence of Depth of Cut, Time of Cut, and Machining Speed on Vibration Frequency during Turning of AL1060 Alloy

Okokpujie¹,

Ohunakin²,

Bolu³

et al. 2020

IJATCSE

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Machining vibration is a significant concern in the manufacturing industry, as vibration cannot be eliminated during metal to metal turning operations. It can be controlled to the minimum when a proper study is carryout before the turning process. This study aims to experimentally analyse the effect of the individual parameters such as depth of cut (DOC) and cutting speed have on the machining vibration during turning of AL1060 alloy. A computer numerical control (CNC) program was applied to investigate the effects of the different depths of cut such as 1 mm, 1.5 mm, 2 mm, 2.5 mm, and 3 mm. Moreover, cutting speed of 200 m/min, 350 m/min, 500 m/min, and 600 m/min with a constant feed rate of 5 mm/rev have on the machining vibration during the turning process. DTO 32105 frequency analyser an MXC-1600 digital frequency counter was employed to measure the machining vibration for a specific time between 15 seconds to 500 seconds. The result shows that as the depth of cut continually increases, the machining vibration increases and the minimum machining vibration of 131.8 Hz was obtained at cutting speed of 600 m/min, depth of cut of 1 mm, and cutting time of 15 sec. The Pareto chart was further used to analysis the average vibration frequency at the optimised cutting speed of 600 m/min. The surface quality and the cutting tool life can be significantly enhanced with the study of the individual machining parameters.

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“…Where T represent the tool wear rate, while the premise parameters A which interact with as the consequent parameters to produce the output (Tool wear prediction). In machining, cutting liquids, otherwise called metal working liquids, are utilized in tasks as a coolant to diminish the intensity produced by prompted grinding between the cutting device and the contact surface of the workpiece [26]. It likewise works as a grease, diminishing grinding and eliminating chips produced by the slicing device-to-workpiece contact during machining.…”

Section: Introductionmentioning

confidence: 99%

An Overview of the Study of ANN-GA, ANN-PSO, ANFIS-GA, ANFIS-PSO and ANFIS-FCM Predictions Analysis on Tool Wear During Machining Process

Okokpujie¹,

Sinebe²

2023

JESA

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The implementation of soft computing procedures in tool wear prediction and optimization is a significant process in machining operations for sustainable manufacturing of components with quality finishing. Tool wear is one of the response parameters that leads to a high rate of production cost due to constant tool substitution during machining, mostly when machining hard metals that are difficult to machine. With these challenges, several techniques have been put in place to optimize and predict tool wear rates, including turning, milling, grinding, shaping, and drilling. This study focuses on the evaluation of existing literature that employs soft computing procedures such as ANN-GA, ANFIS, ANFIS-PSO, and ANFIS-FCM in the prediction of cutting tool wear rate during machining processes. From the different study reviews, the results show that the application of these soft computing procedures significantly improves tool life during the manufacturing process by employing the optimal machining parameters in an eco-friendly nano-lubrication environment. This study also points out the challenges currently faced with these soft computing techniques and gives a sustainable way forward as a recommendation to improve the manufacturing process.

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Experimental data-set for prediction of tool wear during turning of Al-1061 alloy by high speed steel cutting tools

Cited by 29 publications

References 6 publications

Experimental analysis of cutting force during machining difficult to cut materials under dry, mineral oil, and TiO2 nano-lubricant

Experimental analysis of cutting force during machining difficult to cut materials under dry, mineral oil, and TiO2 nano-lubricant

Experimental Analysis of the Influence of Depth of Cut, Time of Cut, and Machining Speed on Vibration Frequency during Turning of AL1060 Alloy

An Overview of the Study of ANN-GA, ANN-PSO, ANFIS-GA, ANFIS-PSO and ANFIS-FCM Predictions Analysis on Tool Wear During Machining Process

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