Sustainable Hard Turning of High Chromium AISI D2 Tool Steel Using CBN and Ceramic Inserts

Rafighi, Mohammad; Özdemir, Mustafa; Shehabi, Shadi Al; Kaya, Mehmet Tuncay

doi:10.1007/s12666-021-02245-2

Cited by 26 publications

(18 citation statements)

References 25 publications

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“…Similar parameters have been utilized in the literature. [36][37][38] The number of data for training, testing, and validation was selected randomly. The training of the network was repeated several times to achieve anticipated results taking diverse initial random weights.…”

Section: Mathematical Modelingmentioning

confidence: 99%

An investigation on cutting sound effect on power consumption and surface roughness in CBN tool-assisted hard turning

Şahinoğlu

Rafighi

Kumar

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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In machining activities, sound emission is one of the key factors toward the operator's health and safety. Sound generation during cutting is the outcome of the interaction between tool and work. The intensity of sound greatly influences the cutting power consumption and surface finish obtained during machining. Therefore, the current work emphasized the analysis of sound emission, power consumption, and surface roughness in hard turning of AISI 4340 steel using a CBN tool which was rarely found in the literature. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized to formulate the model for each response. The results indicated that the maximum value of input parameters exhibited the highest level of sound due to the creation of vibration in the machine and tool. Higher sound level indicates the generation of lower power consumption but at the same instant surface roughness was leading with increment in sound level. The feed rate exhibited the utmost noteworthy consequence on surface quality with 87.71% contribution. The cutting power can be decreased by choosing the high level of cutting parameters. The RSM and ANN have a good correlation with experimental data, but the accuracy of the ANN is better than the RSM.

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Section: Mathematical Modelingmentioning

confidence: 99%

An investigation on cutting sound effect on power consumption and surface roughness in CBN tool-assisted hard turning

Şahinoğlu

Rafighi

Kumar

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…The results show that the empirical model predictions are in close agreement with the experimental results when tested within conditions. Rafighi et al [28] investigated the effect of CBN and ceramic inserts during the hard turning of AISI D2. ANN and multivariate multiple regression (MMR) models were developed to predict surface roughness and force components.…”

Section: Introductionmentioning

confidence: 99%

Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN tool

Adizue,

Tura,

Isaya

et al. 2023

Preprint

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High-quality machining is a crucial aspect of contemporary manufacturing technology due to the vast demand for precision machining for parts made from hardened tool steels and super alloys globally in the aerospace, automobile, and medical sectors. The necessity to upheave production efficiency and quality enhancement at minimum cost requires deep knowledge of this cutting process and development of machine learning-based modeling technique, adept in providing essential tools for design, planning, and incorporation in the machining processes. This research aims to develop a predictive surface roughness model and optimize its process parameters for Ultra-precision hard-turning finishing operation. Ultra-precision hard-turning experiments were carried out on AISI D2 of HRC 62. The response surface method (RSM) was applied to understand the effect of process parameters on surface roughness and carry out optimization. Based on the data gained from experiments, Machine learning models and algorithms were developed with Support vector machine (SVM), Gaussian process relation (GPR), Adaptive-neuro fuzzy inference system (ANFIS), and artificial neural network (ANN) for the prediction of surface roughness. The results show that ANFIS gave the best predictive accuracy of average R, RMSE, and MAPE values of 0.98, 0.06, and 9.98%, respectively, and that of additional validation tests were 0.81, 0.17 and 32.34%, respectively, which are found reasonably accurate. The RSM analysis shows that the feed is the most significant factor for minimizing surface roughness Ra among the process parameters, with 92% influence, and optimal cutting conditions was found to be cutting speed = 100 m/min, feed = 0.025 mm/rev and depth of cut = 0.09 mm, respectively. This finding can be helpful in the decision-making on process parameters in the precision machining industry.

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“…Their brittleness often results in excessive shavings and breakage during the machining of hardened steels. [7][8][9][10] In some other cases, low-cost carbide cutting tools can also be used to process hardened steel. Therefore, problems encountered with PCBN and ceramic cutting tools can be solved to some extent with carbide cutting tools.…”

Section: Introductionmentioning

confidence: 99%

Effects of shallow cryogenic treatment on surface characteristics and machinability factors in hard turning of AISI 4140 steel

Rafighi

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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Hardened AISI 4140 steel is widely used in automotive industries due to its high strength, high hardness, and good formability. It can be subjected to cryogenic treatments to increase wear resistance, increase the toughness and decrease the residual stress. Although many studies have been performed related to the machinability of AISI 4140 steel, the number of studies that considered four responses simultaneously are limited. In this study, the effects of cutting speeds, feed rates, and depths of cut on the vibration, motor current, machining noise, and surface roughness were investigated in turning hardened AISI 4140 steel using coated carbide insert under dry cutting conditions. Besides, the attempt was made to predict surface roughness based on machining noise, motor current, and chuck vibration during the turning process. Analysis of variance and response surface methods were carried out to analyze the experimental results and the prediction models were developed. The machining test revealed that the most effective parameter on the surface roughness value was the feed rate with a 96.07% contribution. The depth of cut and the feed rate affected vibration with 57.61% and 26.44% contribution, respectively. The machining noise was dominantly affected by the depth of cut with 76.15% contribution, it was followed by feed rate with 17.67% contribution. However, feed rate and depth of cut significantly affect motor current with 54.53% and 36.64% contribution, respectively. The graphical analysis of the effect of vibration, motor current, and machining noise on the surface roughness was presented. Based on the results, there is a direct relationship between machining noise, vibration, and motor current with surface roughness. As the level of the aforementioned responses increases the quality of the machined surface deteriorates.

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Sustainable Hard Turning of High Chromium AISI D2 Tool Steel Using CBN and Ceramic Inserts

Cited by 26 publications

References 25 publications

An investigation on cutting sound effect on power consumption and surface roughness in CBN tool-assisted hard turning

An investigation on cutting sound effect on power consumption and surface roughness in CBN tool-assisted hard turning

Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN tool

Effects of shallow cryogenic treatment on surface characteristics and machinability factors in hard turning of AISI 4140 steel

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