Development of Cutting Forces and Surface Roughness Prediction Models for Turning a CoCrWNi Alloy

Dijmărescu, Manuela Roxana; Tiriplica, Petre Gheorghe

doi:10.4028/www.scientific.net/msf.957.148

Cited by 2 publications

(4 citation statements)

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“…The quality of the surface has a significant impact on how effectively turning works [2,3]. Other practical characteristics of parts, such as light reflection, wear, surface friction, lubricant retaining and dispersing, load-bearing capacity, coating, and fatigue resistance are also influenced by the quality of the surface [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Another study was conducted on a CoCrWNi alloy to see how the cutting factors affected the forces and the quality of the machined surface. The feed, followed by the cutting depth, had the utmost effect on the forces and roughness values(Ra), whilst the speed had little effect [4]. Using Deform-3D software, a turning process model for AISI 1025 carbon steel and Al6061 billets was developed [8].…”

Section: Introductionmentioning

confidence: 99%

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Experimental and numerical investigation of dry turning AISI 1030 carbon steel using CNC lathe machining

2023

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Nowadays, modern metal industries have difficulty obtaining the required surface quality during machining. This is because various process parameters have an impact on the quality of the surface. As a result, optimizing the turning input factors such as cutting speed, depth of cut and feed is crucial for finishing quality. In addition to this, to minimize the limitations of experimental studies of metal cutting, finite element simulation is used, which improves time and cost. This study aims to investigate the dry turning of AISI 1030 carbon steel material experimentally and numerically using finite element simulation, as well as optimizing cutting parameters such as cutting speed, depth of cut, and feed to get a better surface finish and optimize material removal rate. The experiment was designed using a Taguchi L9 orthogonal array, and an Analysis of Variance was used to determine the significance of cutting parameters on end responses. According to the results, cutting speed (67.21%)was the most significant parameter which influences surface roughness followed by feed rate (26.91%).At 250 m/min cutting speed, 0.5 mm depth of cut, and 0.15 mm/rev feed rate, minimum surface roughness (1.21 µm) was achieved. The depth of cut had a significant impact on the rate of material removal (66.13%), and the optimum material removal rate was 453.57 mm3/s, which was attained at 90 m/min, 2.5 mm, and 0.35 mm/rev. With an average relative error of 4.85%, the Taguchi prediction and finite element simulation were in excellent agreement with the experimental result.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental and numerical investigation of dry turning AISI 1030 carbon steel using CNC lathe machining

2023

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“…The relative measure that defines how easily a material can be cut is called machinability [ 11 , 14 ]. Cutting process reliability is one of the parameters that defines the machinability of a material [ 14 ] and it can be characterized by effective surface quality, chip breaking and cutting tool durability [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Several researchers have proposed approaches that evaluate, predict and/or optimize surface roughness obtained from different machining processes, such as analytical models, multiple regression functions, fuzzy logic, genetic algorithms and artificial neural networks (ANN) [ 15 , 20 , 21 , 24 , 33 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 ].…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys

et al. 2021

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The aim of this paper is to conduct an experimental study in order to obtain a roughness (Ra) prediction model for dry end-milling (with an AlTiCrSiN PVD-coated tool) of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni biomedical alloys, a model that can contribute to more quickly obtaining the desired surface quality and shortening the manufacturing process time. An experimental plan based on the central composite design method was adopted to determine the influence of the axial depth of cut, feed per tooth and cutting speed process parameters (input variables) on the Ra surface roughness (response variable) which was recorded after machining for both alloys. To develop the prediction models, statistical techniques were used first and three prediction equations were obtained for each alloy, the best results being achieved using response surface methodology. However, for obtaining a higher accuracy of prediction, ANN models were developed with the help of an application made in LabView for roughness (Ra) prediction. The primary results of this research consist of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni prediction models and the developed application. The modeling results show that the ANN model can predict the surface roughness with high accuracy for the considered Co–Cr alloys.

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Development of Cutting Forces and Surface Roughness Prediction Models for Turning a CoCrWNi Alloy

Cited by 2 publications

References 20 publications

Experimental and numerical investigation of dry turning AISI 1030 carbon steel using CNC lathe machining

Experimental and numerical investigation of dry turning AISI 1030 carbon steel using CNC lathe machining

Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys

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