Turning operation of AISI 4340 steel in flooded, near-dry and dry conditions: a comparative study on tool-work interface temperature

Dennison, Milon Selvam; Sivaram, N.M.; Barik, Debabrata; Ponnusamy, Senthil Kumar

doi:10.2478/mme-2019-0023

Cited by 17 publications

(8 citation statements)

References 21 publications

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“…For the constraint boundary, ranges of values of machining parameters have been used to define the lower and upper variable bounds of cutting speed, feed rate, depth of cut, and tool nose radius. Recent literature [23], [25][26][27], [43][44] suggests the range of process parameters in the turning AISI 4340 steel, presented in Table 1. Therefore, this study chooses a range of cutting speed, feed rate, depth of cut, and tool nose radius of, respectively, 50-375 m/min, 0.02-0.25 mm/rev, 0.1-1.5 mm, and 0.4-0.8 mm.…”

Section: Hyperparameter Tuning With Gridsearchcvmentioning

confidence: 99%

A Hybridization of Machine Learning and NSGA-II for Multi-Objective Optimization of Surface Roughness and Cutting Force in ANSI 4340 Alloy Steel Turning

Nguyen¹,

Nguyen²,

Le³

et al. 2023

Journal of Machine Engineering

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This work focuses on optimizing process parameters in turning AISI 4340 alloy steel. A hybridization of Machine Learning (ML) algorithms and a Non-Dominated Sorting Genetic Algorithm (NSGA-II) is applied to find the Pareto solution. The objective functions are a simultaneous minimum of average surface roughness (Ra) and cutting force under the cutting parameter constraints of cutting speed, feed rate, depth of cut, and tool nose radius in a range of 50-375 m/min, 0.02-0.25 mm/rev, 0.1-1.5 mm, and 0.4-0.8 mm, respectively. The present study uses five ML modelsnamely SVR, CAT, RFR, GBR, and ANNto predict Ra and cutting force. Results indicate that ANN offers the best predictive performance in respect of all accuracy metrics: root-mean-squared-error (RMSE), mean-absolute-error (MAE), and coefficient of determination (R 2 ). In addition, a hybridization of NSGA-II and ANN is implemented to find the optimal solutions for machining parameters, which lie on the Pareto front. The results of this multi-objective optimization indicate that Ra lies in a range between 1.032 and 1.048 µm, and cutting force was found to range between 7.981 and 8.277 kgf for the five selected Pareto solutions. In the set of non-dominated keys, none of the individual solutions is superior to any of the others, so it is the manufacturer's decision which dataset to select. Results summarize the value range in the Pareto solutions generated by NSGA-II: cutting speeds between 72.92 and 75.11 m/min, a feed rate of 0.02 mm/rev, a depth of cut between 0.62 and 0.79 mm, and a tool nose radius of 0.4 mm, are recommended. Following that, experimental validations were finally conducted to verify the optimization procedure.

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Section: Hyperparameter Tuning With Gridsearchcvmentioning

confidence: 99%

A Hybridization of Machine Learning and NSGA-II for Multi-Objective Optimization of Surface Roughness and Cutting Force in ANSI 4340 Alloy Steel Turning

Nguyen¹,

Nguyen²,

Le³

et al. 2023

Journal of Machine Engineering

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“…The quality attribute with the sort of ‘smaller-the-better’ [ 1 , 5 ] measured in this research work was surface roughness (Ra) of the machined samples and tool-work interface temperature (T) while machining. The Signal-to-Noise Ratio (SNR) for the yield responses was computed by Eq.…”

Section: Data Descriptionmentioning

confidence: 99%

“…Tables 5 and Table 6 show the outcomes accomplished by ANOVA. The regression value is seen as under 0.05 for both response factors demonstrating that the created model is at 95% of the confidence limit [ 1 , 5 ]. The P-value is determined by 95% of the confidence limit.…”

Section: Data Descriptionmentioning

confidence: 99%

Data-set collected during turning operation of AISI 1045 alloy steel with green cutting fluids in near dry condition

Dennison

Umar

2020

Data in Brief

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This work is to explicate the data collected during the turning of AISI 1045 alloy steel components in near dry condition with emulsified cutting fluids prepared from cooking oils such as Palm oil and Peanut oil. The base oils are tested for its relative density, viscosity and flash point following ASTM standards . Highly influencing turning factors are identified and the experiments are planned and arranged using Taguchi's L 27 (3 5 ) orthogonal array, the experiments are repeated to reduce the errors. The quality aspect of machined components and the machining interface temperature is observed as the outcomes. The prediction models are created for the experiments through regression analysis.

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“…The chip continuously flows over the rack face and causes an increment in the tool-chip contact area resulting in crater wear on the rack face. Different sustainable dry machining methods like insert surface modification, minimum/low quantity lubrication (MQL/LQL), micro-MQL, nano-MQL, and cryonic cooling have been applied for dry machining hardened steels, Inconel, and titanium alloys [1,2].…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of dimple-texturing on the turning performance of hardened AISI H-13 steel

Vignesh

Barik

Aravind

et al. 2022

Int. J. Simul. Multidisci. Des. Optim.

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Forming micro-dimples nearer to the cutting edge on the rack face of the tungsten carbide cutting inserts will positively influence the machinability. However, it is challenging to machine the perfect micro-dimple dimensions by utilizing the available machining techniques. Finite element analysis can be an efficient way to observe the influence of dimple-texture area density, micro-dimple size, and various micro-dimple shapes on cutting inserts' machinability. This paper numerically analyses the impact of micro-dimple-textured cutting inserts in dry machining of AISI H-13 steel using AdvantEdge (virtual machining and finite element analysis software). Micro-dimples are formed on the rack face of tungsten carbide cutting inserts to observe the effect of dimple-textured cutting inserts on machinability compared to non-textured cutting inserts in terms of micro-dimple shape, micro-dimple size, and micro-dimple area density ratio. Their outcomes are analysed in terms of chip-insert contact length, main cutting force, and thrust force. It is observed that micro-dimple textured cutting inserts exhibit minimal main cutting force and thrust force in line with increasing the cutting insert life span. The abrasive wear was reduced in dimple-textured cutting inserts due to minimal contact between the cutting insert and chip developed compared to non-textured cutting inserts.

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Turning operation of AISI 4340 steel in flooded, near-dry and dry conditions: a comparative study on tool-work interface temperature

Cited by 17 publications

References 21 publications

A Hybridization of Machine Learning and NSGA-II for Multi-Objective Optimization of Surface Roughness and Cutting Force in ANSI 4340 Alloy Steel Turning

A Hybridization of Machine Learning and NSGA-II for Multi-Objective Optimization of Surface Roughness and Cutting Force in ANSI 4340 Alloy Steel Turning

Data-set collected during turning operation of AISI 1045 alloy steel with green cutting fluids in near dry condition

Numerical investigation of dimple-texturing on the turning performance of hardened AISI H-13 steel

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