Experimental research of face mill wear effect to flat surface roughness

Pimenov, Danil Yurievich

doi:10.3103/s1068366614030118

Cited by 29 publications

(21 citation statements)

References 12 publications

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“…Considering the same machining operation but for Inconel 718 superalloy, Tian et al [19] conducted research to evaluate the influence of machining speed on cutting forces, as well as on tool wear. The study of Pimenov [20] revealed the influence of face mill wear on the surface roughness parameter when the subject material was steel-45. Cui and Zhao [21] studied the machinability of AISI H13 steel, hardened and machined as face milling.…”

Section: Introductionmentioning

confidence: 99%

Effect of the Relative Position of the Face Milling Tool towards the Workpiece on Machined Surface Roughness and Milling Dynamics

et al. 2019

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In face milling one of the most important parameters of the process quality is the roughness of the machined surface. In many articles, the influence of cutting regimes on the roughness and cutting forces of face milling is considered. However, during flat face milling with the milling width B lower than the cutter’s diameter D, the influence of such an important parameter as the relative position of the face mill towards the workpiece and the milling kinematics (Up or Down milling) on the cutting force components and the roughness of the machined surface has not been sufficiently studied. At the same time, the values of the cutting force components can vary significantly depending on the relative position of the face mill towards the workpiece, and thus have a different effect on the power expended on the milling process. Having studied this influence, it is possible to formulate useful recommendations for a technologist who creates a technological process using face milling operations. It is possible to choose such a relative position of the face mill and workpiece that will provide the smallest value of the surface roughness obtained by face milling. This paper shows the influence of the relative position of the face mill towards the workpiece and milling kinematics on the components of the cutting forces, the acceleration of the machine spindle in the process of face milling (considering the rotation of the mill for a full revolution), and on the surface roughness obtained by face milling. Practical recommendations on the assignment of the relative position of the face mill towards the workpiece and the milling kinematics are given.

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Section: Introductionmentioning

confidence: 99%

Effect of the Relative Position of the Face Milling Tool towards the Workpiece on Machined Surface Roughness and Milling Dynamics

et al. 2019

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“…One of the main precision criteria in face milling is sur-face roughness, Ra. Surface roughness has in various papers (Fernández-Valdivielso et al 2016;Pimenov 2013aPimenov , 2014 been shown to increase with increased flank wear of face mill teeth. Workpiece specifications will usually detail the maximum roughness value that has to be machined.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

2017

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Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, V B into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N , in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power-that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements. Keywords

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“…The coefficients of determination values are comparable to that of and, hence, the same conclusions are valid. Equation (2) represents the relationship between and studied factors: The values for optimum (minimum) are as follows: = 500 rpm, = 0.29 mm, and = 50 mm/min. It is noticed that only the feed rate value is common between minimum and minimum .…”

Section: 2mentioning

confidence: 99%

“…Pimenov [2] investigated the effect of feed, cutting speed, and tool wear of a T5K10 carbide tool on the roughness of flat surfaces, produced by face milling, of steel 45. The 2 Advances in Materials Science and Engineering work analyzed the nature of the microprofile of changes in machined surfaces based on increasing the wear surface on the tooth flank.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Cutting Conditions for Minimum Surface Roughness in Face Milling of High Strength Steel Using Carbide Inserts

Abbas¹,

Ragab²,

Bahkali³

et al. 2016

Advances in Materials Science and Engineering

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A full factorial design technique is used to investigate the effect of machining parameters, namely, spindle speed(N), depth of cut(ap),and table feed rate(Vf),on the obtained surface roughness (RaandRt) during face milling operation of high strength steel. A second-order regression model was built using least squares method depending on the factorial design results to approximate a mathematical relationship between the surface roughness and the studied process parameters. Analysis of variance was conducted to estimate the significance of each factor and interaction with respect to the surface roughness. ForRa, the results show that spindle speed, depth of cut, and table feed rate have a significant effect on the surface roughness in both linear and quadratic terms. There is also an interaction between depth of cut and feed rate. It also appears that feed rate has the greatest effect on the data variation followed by depth of cut. ForRt, the results show that the table feed rate is the most effective factor followed by the depth of cut, while the spindle speed had a significant small effect only in its quadratic term. The conditions of minimumRaandRtare identified through least square optimization. Moreover, multiobjective optimization for minimizingRaand maximizing metal removal rateQis conducted and the results are presented.

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Experimental research of face mill wear effect to flat surface roughness

Cited by 29 publications

References 12 publications

Effect of the Relative Position of the Face Milling Tool towards the Workpiece on Machined Surface Roughness and Milling Dynamics

Effect of the Relative Position of the Face Milling Tool towards the Workpiece on Machined Surface Roughness and Milling Dynamics

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Optimizing Cutting Conditions for Minimum Surface Roughness in Face Milling of High Strength Steel Using Carbide Inserts

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