A study of the influence of processing parameters and tool wear on elastic displacements of the technological system under face milling

Pimenov, Danil Yurievich; Guzeev, V. I.; Mikołajczyk, Tadeusz; Patra, Karali

doi:10.1007/s00170-017-0516-6

Cited by 32 publications

(12 citation statements)

References 31 publications

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“…Likewise, a MITSUBISHI SE445-125-B09 R tool was selected, with a diameter of 125 mm, fitted with nine carbide coated flutes (Z=9), reference SEER 1203 AFEN-JS F620, with a lead angle of 45º. The recommended rotational speed range for the tool is between 400 and 600 rpm, in accordance with similar works about face milling operations [42,43]. A tangential cutting coefficient of Kt=1685 N/mm 2 was experimentally characterized for the cutting operation.…”

Section: Analytical Calculation Of Chatter Frequency and Critical Depthmentioning

confidence: 99%

A machine-learning based solution for chatter prediction in heavy-duty milling machines

et al. 2018

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The main productivity constraints of milling operations are self-induced vibrations, especially regenerative chatter vibrations. Two key parameters are linked to these vibrations: the depth of cut achievable without vibrations and the chatter frequency. Both parameters are linked to the dynamics of machine component excitation and the milling operation parameters. Their identification in any cutting direction in milling machine operations requires complex analytical models and mechatronic simulations, usually only applied to identify the worst cutting conditions in operating machines. This work proposes the use of machine-learning techniques with no need to calculate the two abovementioned parameters by means of a 3-step strategy. The strategy combines: 1) experimental frequency responses collected at the tool center point; 2) analytical calculations of both parameters; and, 3) different machine-learning techniques. The results of these calculations can then be used to predict chatter under different combinations of milling directions and machine positions. This strategy is validated with real experiments on a bridge milling machine performing concordance roughing operations on F-114 steel with a 125 mm diameter mill fitted with nine cutters at 45°, the results of which have confirmed the high variability of both parameters along the working volume. The following regression techniques are tested: artificial neural networks, regression trees and random forest. The results show that random forest ensembles provided the highest accuracy with a statistical advantage over the other machine learning models; they achieved a final accuracy of 0.95 mm for the critical depth and 7.3 Hz for the chatter frequency (RMSE) in the whole working volume and in all feed directions, applying a 10x10 cross validation scheme. These RMSE values are acceptable from the industrial point of view, taking into account that the critical depth of this range varies between 0.68 mm and 19.20 mm and the chatter frequency between 1.14 Hz and 65.25 Hz. Besides, random forest ensembles are more easily optimized than artificial neural networks (1 parameter configuration versus 210 MLPs). Additionally, tools that incorporate regression trees are interesting and highly accurate, providing immediately accessible and useful information in visual formats on critical machine performance for the design engineer.

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Section: Analytical Calculation Of Chatter Frequency and Critical Depthmentioning

confidence: 99%

A machine-learning based solution for chatter prediction in heavy-duty milling machines

et al. 2018

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“…Thus, the use of lattice matrices allows to lead the model of machining accuracy for different types of multi-tool adjustments to the simplest form from the standpoint of algorithmic (24), (29), (33), (39).…”

Section: Lattice Matrix Models Of the Accuracy Of Multi-tool Machiningmentioning

confidence: 99%

“…In these cases, a significant contribution to the machining error can be made by turns of the workpiece, especially in the directions of the prevailing overall dimensions. The need to take into account angular displacements of workpiece under the action of cutting forces was indicated in [3,[29][30][31][32][33][34][35][36][37][38][39][40][41][42]. They even proposed the simplest analytical relationships for calculating these angular displacements.…”

Section: Full-factor Model -Distortion Of Executed Dimensions In Multmentioning

confidence: 99%

Models for Machining Accuracy in Multi-Tool Adjustment

Yusubov

Abbasova

2020

IJAME

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The article discusses the technology capabilities of multi-purpose CNC machines, and possible options for implementing parallel multi-tool processing. It was revealed that the technological capabilities of these machines are used at best by 50% in factories. This is due to the lack of recommendations for the design and use of such adjustments for these machines. To this end, generalised lattice matrix models of the accuracy of multi-tool machining have been developed in order to fulfill the requirements of algorithmic uniformity models and their structural transparency. The use of lattice matrices greatly simplifies the error in model of multi-tool machining and makes it extremely visual. Also, full-factorial distortion models and scattering fields of the dimensions of multi-tool machining performed on modern multi-purpose CNC lathe machines have been developed to take into account the angular displacements of the workpiece when machining parts with prevailing overall dimensions. They take into account the flexibility of the technological system for all six degrees of freedom to identify the influence degree of complex of technological factors on the machining accuracy (structure of multi-tool adjustment, deformation properties of subsystems of a technological system, cutting conditions). A methodology has been developed for determining the complex characteristics of compliance of a technological system. On the basis of the developed accuracy models in spatial adjustments, it is possible to develop recommendations for the design of adjustments for modern multi-purpose machines in CNC turning group (creation of CAD of multi-tool machining). Thus, it is possible to achieve a number of ways to control multi-tool machining, including improving the structure of multi-tool adjustment, calculating the limiting cutting conditions.

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“…Для плоско-параллельных перемещений вводилось понятие объединенной матрицы податливости 12 1 2 e e e   [5]. Из уравнения (7) видно, что для случая полного учета перемещений по всем шести направлениям степеней свободы общую объединенную матрицу податливости ввести невозможно, так как эффективные матрицы угловой податливости для заданных точек каждого тела представляют собой произведения из трех матриц.…”

Section: пусть вектор ( )unclassified

Full Factorial Models Ofdimensional Accuracy of Multi-Tool Machining on Automatic Turning Machines

Юсубов¹,

Аббасова²,

Yusubov³

et al. 2019

MEI

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A study of the influence of processing parameters and tool wear on elastic displacements of the technological system under face milling

Cited by 32 publications

References 31 publications

A machine-learning based solution for chatter prediction in heavy-duty milling machines

A machine-learning based solution for chatter prediction in heavy-duty milling machines

Models for Machining Accuracy in Multi-Tool Adjustment

Full Factorial Models Ofdimensional Accuracy of Multi-Tool Machining on Automatic Turning Machines

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