A comparison RSM and ANN surface roughness models in thin-wall machining of Ti6Al4V using vegetable oils under MQL-condition

Mohruni, Amrifan Saladin; Yanis, Muhammad; Sharif, Safian; Yani, Irsyadi; Yuliwati, Erna; Ismail, Ahmad Fauzi; Shayfull, Z.

doi:10.1063/1.5002355

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Given the characteristics of the milling process used to generate thin walls, it is expected that the machined surface roughness of the thin wall will also be significantly affected by the cutting conditions [ 5 , 29 , 55 , 62 , 63 , 64 ]. The successive entry of the cutting teeth of the tool into the material of the workpiece, the variation of the chip thickness, the variation of the cutting force, the vibrations generated by the milling process, the values of the milling parameters, etc., can be factors with a strong influence on the values of some parameters characterizing the roughness of the machined surface of the thin wall.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Thin Wall Milling Ability Using Disc Cutters

et al. 2023

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In some cases, industrial practice requires the production of walls or parts with a thickness of less than one millimeter from a metal workpiece. Such parts or walls can be made by milling using disc cutters. This machining method can lead to the generation of residual stresses that determine the appearance of a form deviation characterized by bending the part or the thin wall. To evaluate the suitability of a metallic material for the manufacturing of thin walls by milling with disc cutters, different factors capable of exerting influence on the deviation generated by the residual deformation of the walls were taken into account. A test sample and an experimental research program were designed for the purpose of obtaining an empirical mathematical model. The empirical mathematical model highlights the magnitude of the influence exerted by different input factors on the disc cutter milling process regarding the size of the deviation from the form, and the correct position of the wall or thin part, in the case of a test sample workpiece made of an aluminum alloy. Input factors considered were cutting speed, feed rate, cutter thickness, wall or part thickness, thin wall length, and height. To rank the input factors whose increase leads to an increase in shape deviation, the values of the exponents attached to the factors in question in the empirical mathematical model of the power-type function were taken into account. It was found that the values of the exponents are in the order 0.782 > 0.319 > 0.169 for wall height, feed rate, and wall width, respectively. It was thus established that the strongest influence on the residual deformation of the thin wall is exerted by its height.

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Section: Methodsmentioning

confidence: 99%

Evaluation of Thin Wall Milling Ability Using Disc Cutters

et al. 2023

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“…Data analysis were carried out using RSM and ANN. Many researchers reported that both methods are capable of finding the optimum result [19], [20], [21].…”

Section: Design Of Experiments (Doe)mentioning

confidence: 99%

“…The information has processed the neuron and is propagated to other neurons through the synaptic weight of the links connecting the neuron (wi). Summation the weight input to neurons and including bias is given in Equation 9 [20], [19].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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Optimum Performance of Green Machining on Thin Walled Ti6al4v Using RSM and Ann in Terms of Cutting Force and Surface Roughness

et al. 2019

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Thin walled titanium alloys are mostly applied in the aerospace industry owing to their favorable characteristic such as high strength-to-weight ratio. Besides vibration, the friction at the cutting zone in milling of thin-walled Ti6Al4V will create inconsistencies in the cutting force and increase the surface roughness. Previous researchers reported the use of vegetable oils in machining metal as an effort towards green machining in reducing the undesirable cutting friction. Machining experiments were conducted under Minimum Quantity Lubrication (MQL) using coconut oil as cutting fluid, which has better oxidative stability than other vegetable oil. Uncoated carbide tools were used in this milling experiment. The influence of cutting speed, feed and depth of cut on cutting force and surface roughness were modeled using response surface methodology (RSM) and artificial neural network (ANN). Experimental machining results indicated that ANN model prediction was more accurate compared to the RSM model. The maximum cutting force and surface roughness values recorded are 14.89 N, and 0.161 µm under machining conditions of 125 m/min cutting speed, 0.04 mm/tooth feed, 0.25 mm radial depth of cut (DOC) and 5 mm axial DOC.

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Modeling and prediction of surface roughness at the drilling of SLM-Ti6Al4V parts manufactured with pre-hole with optimized ANN and ANFIS

Dedeakayogulları

Keser

2022

Measurement

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A comparison RSM and ANN surface roughness models in thin-wall machining of Ti6Al4V using vegetable oils under MQL-condition

Cited by 6 publications

References 17 publications

Evaluation of Thin Wall Milling Ability Using Disc Cutters

Evaluation of Thin Wall Milling Ability Using Disc Cutters

Optimum Performance of Green Machining on Thin Walled Ti6al4v Using RSM and Ann in Terms of Cutting Force and Surface Roughness

Modeling and prediction of surface roughness at the drilling of SLM-Ti6Al4V parts manufactured with pre-hole with optimized ANN and ANFIS

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