Statistical and Experimental Investigation of Hardened AISI H11 Steel in CNC Turning with Alternative Measurement Methods

Şahin, Emrah; Esen, İsmail

doi:10.1155/2021/9480303

Cited by 2 publications

(8 citation statements)

References 42 publications

(64 reference statements)

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“…Similar results for AISI H11 5,10 and AISI S1 steels 27 were found in other studies. As the f and a increase, the cutting forces increase.…”

Section: Power Consumption and Energy Consumptionsupporting

confidence: 91%

“…Increasing f increases temperature and friction, causing roughness. 23 Similar results have been found in AISI H11 5,8,9 and AISI S1 steels 27 by other researchers. The effect of the a on the Ra is very low compared to the f. Similar results have been found in AISI H11 steel by other researchers.…”

Section: Surface Roughnesssupporting

confidence: 90%

“…Therefore, a tool with a tip radius of 0.8 mm and a between 0.1 mm and 0.2 mm was preferred in this experimental study. Similarly, in the machining of AISI H11 steel with a hardness of 50 HRC, with a tool with a tip radius of 0.8 mm; a of 0.15 mm, 5 0.45 mm, 6 0.15 mm, 8 0.5 mm, 9 0.45 mm 10 were preferred.…”

Section: Methodsmentioning

confidence: 99%

“…According to the test results, the current value increases as the depth of cut (a), cutting speed (v) and feed rate (f) increase. The Ra value is increased by increasing f. 5 Fnides et al turned AISI H11 (50 HRC) hot work tool steel with a ceramic insert. It has been stated that the cutting forces increase by increasing f and a, and the cutting force decreases by increasing v. 6 Qamar investigated the hardness and strength properties of AISI H11 steel at different temperatures.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have been carried out on the hard turning process when the studies are examined. When the studies on hard turning in the literature are examined, studies on surface roughness, 5 tool wear, 9,13 cutting force 6 , 8 and cryogenic cooling have been carried out in studies on AISI H11 steels. 7,10 However, in AISI H11 steel, vibration, energy consumption, power consumption and Ra values under different cooling conditions were not investigated.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of machinability properties of AISI H11 tool steel for sustainable manufacturing

Şahinoğlu

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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In this experimental study, vibration, energy consumption, power consumption and surface roughness values that occur in the machining of AISI H11 tool steel under CO2, coolant and dry cutting conditions were investigated. The effects of different cutting parameters and cooling systems on machinability were investigated. Analysis of variance was done. Regression equations were obtained. Relationships between power consumption, vibration and surface roughness are explained with mathematical equations. Finally, the optimum CC were determined by the multiple optimization methods. According to the test results, while the instantaneous power consumption increases by increasing cutting parameters, energy consumption decreases as the processing time is shortened. Vibration value increases by increasing cutting parameters. The highest vibration value occurs in cutting with CO2. Compared to dry and CO2 cutting, the vibration is lowest in coolant cutting. The friction decreases with the coolant and the vibration value decreases. There is a similar relationship between vibration and surface roughness value. The most effective parameter on the surface roughness value is the feed rate. It was seen that the most suitable CC for the most efficient cutting, the lowest energy consumption, vibration and surface roughness value, under coolant cutting, 0.2 mm depth of cut, 175 m/min cutting speed and 0.119 mm/rev feed rate. With optimum CC, the vibration value was reduced by 5.18%, the surface roughness value by 37.12%, energy consumption by 36.19% and the machine efficiency was increased by 7.16%.

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“…Similar results for AISI H11 5,10 and AISI S1 steels 27 were found in other studies. As the f and a increase, the cutting forces increase.…”

Section: Power Consumption and Energy Consumptionsupporting

confidence: 91%

Section: Surface Roughnesssupporting

confidence: 90%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigation of machinability properties of AISI H11 tool steel for sustainable manufacturing

Şahinoğlu

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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Comparative Machining Performance Analysis between Taguchi’s Method and Random Forest Model

Yugeshwar,

Prasad,

Ramana

2024

IJST

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Objectives: To optimize the process parameters in dry turning of hardened 20MnCr5 using a PVD coated insert to minimize surface roughness and power consumption and maximize material removal rate using the Taguchi technique and machine learning model. Methods: For carrying out the experiments, an L27 orthogonal array was employed and Taguchi's means of mean method was used to find the optimal condition. ANOVA was utilized for determining the significance of factors. The Random Forest model uses an ensemble learning method where multiple decision trees are developed and then predictions are made. To boost the prediction efficiency, averages are made from these multiple decision trees and the voting method to finalize the model that needs to be used for the prediction. Finally, experimental and predicted values are compared using statistical metrics to determine the model's effectiveness. Findings: From experimental data at 80 m/min, 0.2 mm/rev, 0.5 mm and 120 m/min, 0.05 mm/rev, 0.25 mm and 100 m/min, 0.2 mm/rev, 0.1 mm are the optimal conditions found for material removal rate, surface roughness and power consumption respectively. Feed was the most influential factor, with a percentage contribution of 90.98%, 46.86% and 87.053% for surface roughness, material removal rate and power consumption respectively. The developed random forest models had an accuracy of 91.057%, 96.546% and 96.0122% for surface roughness, power consumption and material removal rate respectively, and they had less mean absolute errors. Novelty: Alloy steels are used to manufacture components that need to resist wear and plastic deformation, so it's important to know how hardened material behaves under dry turning operations as the world is focusing on green manufacturing. Moreover, the experimental approach is time-consuming and costly, so a machine learning model was developed based on the available experimental data to predict the output responses for additional inputs.

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Statistical and Experimental Investigation of Hardened AISI H11 Steel in CNC Turning with Alternative Measurement Methods

Cited by 2 publications

References 42 publications

Investigation of machinability properties of AISI H11 tool steel for sustainable manufacturing

Investigation of machinability properties of AISI H11 tool steel for sustainable manufacturing

Comparative Machining Performance Analysis between Taguchi’s Method and Random Forest Model

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