Mechanistic modelling for predicting cutting forces in machining considering effect of tool nose radius on chip formation and tool wear land

Orra, Kashfull; Choudhury, S.K.

doi:10.1016/j.ijmecsci.2018.05.004

Cited by 43 publications

(15 citation statements)

References 24 publications

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“…Regarding cutting force determination in the turning process, the study performed by Orra et al [44] proposes a mechanistic model for the cutting forces prediction in the turning of AISI 52,100 and AISI 4340 steels. The cutting forces are predicted with the information provided by chip morphology and nose radius influence on it.…”

Section: Cutting Force Prediction Methodsmentioning

confidence: 99%

Cutting Forces Assessment in CNC Machining Processes: A Critical Review

Sousa

Silva

Fecheira

et al. 2020

Sensors

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Machining processes remain an unavoidable technique in the production of high-precision parts. Tool behavior is of the utmost importance in machining productivity and costs. Tool performance can be assessed by the roughness left on the machined surfaces, as well as of the forces developed during the process. There are various techniques to determine these cutting forces, such as cutting force prediction or measurement, using dynamometers and other sensor systems. This technique has often been used by numerous researchers in this area. This paper aims to give a review of the different techniques and devices for measuring the forces developed for machining processes, allowing a quick perception of the advantages and limitations of each technique, through the literature research carried out, using recently published works.

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Section: Cutting Force Prediction Methodsmentioning

confidence: 99%

Cutting Forces Assessment in CNC Machining Processes: A Critical Review

Sousa

Silva

Fecheira

et al. 2020

Sensors

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show abstract

“…Tool wear is considered a tribological property, which propagates with the machining process and further increases surface roughness [59]. Tool wear directly affects the surface quality, tool life, dimensional accuracy, and economy of the machining process [60]. An important parameter in the milling process is the milling force.…”

Section: Influence On Milling Toolsmentioning

confidence: 99%

Milling Force Model for Aviation Aluminum Alloy: Academic Insight and Perspective Analysis

Duan

Ding

et al. 2021

Chin. J. Mech. Eng.

167

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Aluminum alloy is the main structural material of aircraft, launch vehicle, spaceship, and space station and is processed by milling. However, tool wear and vibration are the bottlenecks in the milling process of aviation aluminum alloy. The machining accuracy and surface quality of aluminum alloy milling depend on the cutting parameters, material mechanical properties, machine tools, and other parameters. In particular, milling force is the crucial factor to determine material removal and workpiece surface integrity. However, establishing the prediction model of milling force is important and difficult because milling force is the result of multiparameter coupling of process system. The research progress of cutting force model is reviewed from three modeling methods: empirical model, finite element simulation, and instantaneous milling force model. The problems of cutting force modeling are also determined. In view of these problems, the future work direction is proposed in the following four aspects: (1) high-speed milling is adopted for the thin-walled structure of large aviation with large cutting depth, which easily produces high residual stress. The residual stress should be analyzed under this particular condition. (2) Multiple factors (e.g., eccentric swing milling parameters, lubrication conditions, tools, tool and workpiece deformation, and size effect) should be considered comprehensively when modeling instantaneous milling forces, especially for micro milling and complex surface machining. (3) The database of milling force model, including the corresponding workpiece materials, working condition, cutting tools (geometric figures and coatings), and other parameters, should be established. (4) The effect of chatter on the prediction accuracy of milling force cannot be ignored in thin-walled workpiece milling. (5) The cutting force of aviation aluminum alloy milling under the condition of minimum quantity lubrication (mql) and nanofluid mql should be predicted.

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“…Masmati et al [17] studied the end milling on S50C medium carbon steel, obtained the quadratic polynomial regression equation of residual stress, milling force, and surface roughness by the RSM, and discussed the influence of primary and interactive cutting parameters on residual stress milling force and surface roughness. Orra and Choudhury [18] studied the influence of chip formation, cutting tip radius, and tool wear on cutting force. e orthogonal experiments of turning two different materials AISI 52100 steel and AISI 4340 steel were carried out, and the prediction model of three-way cutting force was obtained.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Milling Parameters for Ultrahigh‐Strength Steel AF1410 Based on the NSGA‐II Method

Yan

et al. 2020

Advances in Materials Science and Engineering

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In this paper, ultrahigh-strength steel AF1410 was milled with the carbide tool, and a total of thirty experiments were performed based on central composite design (CCD) of response surface methodology. The prediction models of milling force and surface roughness are established, respectively. The influence of milling parameters (milling speed, each tooth feed, radial depth of cut, and axial depth of cut) on milling force and surface roughness is studied by ANOVA and established prediction model. Multiobjective optimization of milling parameters is accomplished based on nondominated sorting genetic algorithm II (NSGA-II) with milling force, surface roughness, and material removal rate as optimization objectives. The surface roughness, cutting force, and material removal rate are important indexes to measure the energy consumed in the process of product, the surface machining quality, and machining efficiency of processing, respectively. In order to minimize milling force and surface roughness and maximize material removal rate, NSGA-II was used for multiobjective optimization to obtain the optimal fitness value of the objective function. The NSGA-II has been applied to obtain a set of optimal combination of parameters from the Pareto-optimal solution set to enhance the machining conditions.

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Mechanistic modelling for predicting cutting forces in machining considering effect of tool nose radius on chip formation and tool wear land

Cited by 43 publications

References 24 publications

Cutting Forces Assessment in CNC Machining Processes: A Critical Review

Cutting Forces Assessment in CNC Machining Processes: A Critical Review

Milling Force Model for Aviation Aluminum Alloy: Academic Insight and Perspective Analysis

Multiobjective Optimization of Milling Parameters for Ultrahigh‐Strength Steel AF1410 Based on the NSGA‐II Method

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