Cutting force modeling using artificial neural networks

Szécsi, T.

doi:10.1016/s0924-0136(99)00183-1

Cited by 60 publications

(33 citation statements)

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“…A simulated annealing technique with a performance index was then applied to optimize process parameters [13]. Szecsi (1999) proposed a method for cutting forces modeling using multi-layer feed-forward back-propagation neural networks trained by the experimental machining data for simulating and defining the cutting forces in the process [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Cutting Forces in Drilling Operation: Measurement and Modeling for Medium-scale Manufacturing Firms

Das¹,

Das²

2015

IJCA

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Advanced manufacturing systems often caters to rapidly changing product specification determination by the continuously increasing productivity, flexibility and quality demands. The estimation of cutting forces is mandatory to select tools and accessories for machining. Complex interrelationships exist between process parameters and these forces. In the present work, the applicability and relative effectiveness of artificial neural network based model has been investigated for rapid estimation of cutting forces. The results obtained are found to correlate well with the actual experimental readings of cutting forces. Experiments were conducted at different process parameters of cutting in Drilling operation. The proposed work has wide application in selection of tools and online tool wear monitoring. General TermsMeasurement and modeling of Cutting forces.

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Section: Literature Reviewmentioning

confidence: 99%

Cutting Forces in Drilling Operation: Measurement and Modeling for Medium-scale Manufacturing Firms

Das¹,

Das²

2015

IJCA

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“…However, the limitation of the FEM is in computing time and the accuracy of the results always need to be improved. Artificial neural network provides new ways for cutting force model [5][6][7], and it can process the nonlinear relationship of the process parameters to determine the force. But the calculation is very complicated and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Improved method to predict cutting force in end milling considering cutting process dynamics

Zhao

Wang

et al. 2015

Int J Adv Manuf Technol

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Accurate cutting force prediction in end milling plays an important role in studying the characteristics of milling to improve the machining quality and optimize the machining processes. To this end, a new method that improves cutting force prediction accuracy by considering cutting process dynamics and simplifies the process of computing the dynamic chip thickness is presented in this paper. The static chip thickness is calculated and the cutting process dynamics is taken into account in this method. To ensure the result accuracy and improve computing efficiency, Taylor's series is adopted to acquire static chip thickness. Meanwhile, as feedback, the dynamic chip thickness caused by the cutter-workpiece system subjected to the cutting force is superposed on static chip thickness to get more accurate cutting force. Finally, the proposed method by dint of closed-loop system to acquire more accurate cutting force is experimentally validated in milling of plastic die steel NAK80 with a four fluted flat-end milling cutter. Cutting force coefficients and modal parameters are identified based on the experiment. Both the simulations with and without considering dynamics show good agreement with

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“…Although an enormous amount of cutting force and surface roughness related data is available in machining handbooks, most of them attempt to define the relationship between a specific number of process parameters whilst keeping others constant [8]. Taking into account this observation it is not surprising that from the era of conventional machine tools to the present era of CNC machine tools, the prediction of cutting behaviour of processes and the optimization of machining parameters have been major areas of scientific and industrial research.…”

Section: Introductionmentioning

confidence: 99%

“…ANNs have been extensively applied to model various machining operations; either conventional (turning, milling, grinding) or nonconventional [Electro-discharge machining (EDM), Abrasive water-jet machining (AWJM) etc]; see Refs [8,[12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…As far as turning is concerned, ANNs have been used for the prediction of cutting forces [8,17]), surface roughness [5,[18][19][20][21]; dimensional deviation [18], tool-wear [20], tool life [22] and process optimization [23]. Of relevant interest are also the combinations of ANN with other soft computing techniques such as GA together with Taguchi experimental design; see for example Refs.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Machinability in Turning of Engineering Alloys by Applying Artificial Neural Networks

Vaxevanidis¹,

Kechagias²,

Fountas³

et al. 2014

TOBCTJ

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Abstract:The present paper investigates the influence of main cutting parameters on the machinability during turning process for three typical materials namely AISI D6 tool steel, Ti6Al4V ELI and CuZn39Pb3 brass, all three under dry cutting environment. Spindle speed, feed rate and depth of cut were selected for study whilst arithmetic surface roughness average (R a ) and main cutting force component (F C ) were treated as quality objectives characterizing machinability. For the aforementioned materials a full factorial design of experiments was conducted to exploit main effects and interactions among parameters it terms of quality objectives. The results obtained from dry turning experiments were utilized as a data set to test, train and validate a feed-forward back propagation artificial neural network for machinability prediction regarding all three materials. The work presents the results obtained from the aforementioned experimental effort under an extensive state-of-the-art survey concerning neural network technology and implementation to machining optimization problems.

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Cutting force modeling using artificial neural networks

Cited by 60 publications

References 1 publication

Cutting Forces in Drilling Operation: Measurement and Modeling for Medium-scale Manufacturing Firms

Cutting Forces in Drilling Operation: Measurement and Modeling for Medium-scale Manufacturing Firms

Improved method to predict cutting force in end milling considering cutting process dynamics

Evaluation of Machinability in Turning of Engineering Alloys by Applying Artificial Neural Networks

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