Prediction of cutting force in turning process: An experimental and fuzzy approach

Shankar, S.; Thangarasu, S. K.; Mohanraj, T.; Pravien, D. S.

doi:10.3233/ifs-141465

Cited by 13 publications

(4 citation statements)

References 19 publications

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“…FEA results were very close with the experimental results compared with thin ring theory [12]. The dynamometer was designed and developed with strain gauges to measure the cutting force [13][14][15].…”

Section: Introductionsupporting

confidence: 70%

Design, development, calibration, and testing of indigenously developed strain gauge based dynamometer for cutting force measurement in the milling process

Mohanraj¹,

Shankar²,

Rathanasamy³

et al. 2020

JMES

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In this work, a milling dynamometer based on strain gauge with an octagonal and square ring was designed and tested. Strain gauges were attached with the mechanical rings to detect the deformation, during the machining process. Wheatstone bridge circuit was equipped with gauges to acquire the strain as voltage owing to the deformation of mechanical rings when machining takes place. The finite element analysis (FEA) was used to identify the location of maximum deformation and stress. The direction of rings and location of gauges were decided to increase the sensitivity and decrease the cross-sensitivity. Then, the cutting force was acquired through NI 6221 M series data acquisition (DAQ) card. The dynamometer had undergone a cycle of tests to verify its static and dynamic characteristics. The metrological characterization was performed according to the calibration procedure based on ISO 376 – 2011 standard. The cutting force was measured with both the dynamometers through milling experiments based on Taguchi’s L9 orthogonal array and the results were recorded. The measured cutting force varied from 300 N to 550 N. The obtained results depicted that low-cost milling dynamometer was reliable to measure the three component machining force. Overall, the square ring based dynamometer provides the better static and dynamic characteristics in terms of linearity, cross-sensitivity (4%), uncertainty (0.054%), and natural frequency (362.41 rev/s).

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Section: Introductionsupporting

confidence: 70%

Design, development, calibration, and testing of indigenously developed strain gauge based dynamometer for cutting force measurement in the milling process

Mohanraj¹,

Shankar²,

Rathanasamy³

et al. 2020

JMES

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“…But in literature it had a direct relation with cutting force. 45,46 This variation may be the range of selected process parameters. From Figure 5(c), increase in depth accelerates the flank wear and increases the contact area between the tool and workpiece.…”

Section: Resultsmentioning

confidence: 99%

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Shankar

Mohanraj

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this work, the flank wear of the cutting tool is predicted using artificial neural network based on the responses of cutting force and surface roughness. EN8 steel is chosen as a work piece material and turning test is conducted with various levels of speed, feed and depth of cut. Cutting force and surface roughness are measured for both the fresh and dull tool under dry cutting conditions. The tool insert used is CNMG 120408 grade, TiN coated cemented carbide tool. The experiments are conducted based on the response surface methodology face central composite design of experiments. The feed rate (14.52%), depth of cut (27.72%) and the interaction of feed rate and depth of cut (50.39%) influence the cutting force. The feed rate (21.33%) and the interaction of cutting speed and depth of cut (26.67%) influence the flank wear. The feed rate (61.63%) has the significant influence on surface roughness. The feed forward back propagation neural network of 5-n-1 architecture is trained using the algorithms like Levenberg Marquardt, BFGS quasi-Newton, and Gradient Descent with Momentum and Gradient descent with adaptive learning rate. The network performance has been assessed based on their mean square error and computation time. From this analysis, the BFGS quasi-Newton back propagation algorithm produced the least mean squared error value with minimum computation time.

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“…Te CF during the turning operation was captured using a lathe tool dynamometer. For acquiring the CF, a data acquisition system with the necessary software and hardware [36] was arranged and is shown in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Flank Wear during Turning of EN8 Steel with Cutting Force Signals Using a Deep Learning Approach

Thangarasu¹,

Mohanraj

Devendran³

et al. 2023

Mathematical Problems in Engineering

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Currently, manufacturing industries focus on intelligent manufacturing. Prediction and monitoring of tool wear are essential in any material removal process, and implementation of a tool condition monitoring system (TCMS) is necessary. This work presents the flank wear prediction during the hard turning of EN8 steel using the deep learning (DL) algorithm. The turning operation is conducted with three levels of selected parameters. CNMG 120408 grade, TiN-coated cemented carbide tool is used for turning. Cutting force and flank wear are assessed under dry-cutting conditions. DL algorithms such as adaptive neuro-fuzzy inference system (ANFIS) and convolutional auto encoder (CAE) are used to predict the flank wear of the single-point cutting tool. The DL model is developed with turning parameters and cutting force to predict flank wear. The different ANFIS and CAE models are employed to develop the prediction model. Grid-based ANFIS structure with Gauss membership function performed better than ANFIS models. The ANFIS model’s average testing error of 0.0074011 mm and prediction accuracy of 99.81% are achieved.

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Prediction of cutting force in turning process: An experimental and fuzzy approach

Cited by 13 publications

References 19 publications

Design, development, calibration, and testing of indigenously developed strain gauge based dynamometer for cutting force measurement in the milling process

Design, development, calibration, and testing of indigenously developed strain gauge based dynamometer for cutting force measurement in the milling process

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Prediction of Flank Wear during Turning of EN8 Steel with Cutting Force Signals Using a Deep Learning Approach

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