Mean Cutting Force Prediction of Conical Picks Using Ensemble Learning Paradigm

Fathipour‐Azar, Hadi

doi:10.1007/s00603-022-03095-0

Cited by 10 publications

(2 citation statements)

References 65 publications

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“…Adam optimizer has demonstrated its efciency in solving practical DL problems, and its results outperform the other stochastic optimization methods. Te proposed DL model uses the mean square error (MSE) loss function, given by equation (10), that is, given a training data…”

Section: Te Proposed Stacked Gru-dnn Modelmentioning

confidence: 99%

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Forecasting the Friction Coefficient of Rubbing Zirconia Ceramics by Titanium Alloy

Salah,

Fathalla,

Eldesouky

et al. 2023

International Journal of Intelligent Systems

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The thermal issues generated from friction are the key obstacle in the high-performance machining of titanium alloys. The friction between the workpiece being cut and the cutting tool is the dominant parameter that affects the heat generation during the machining processes, i.e., the temperature inside the cutting zone and the consumed cutting energy. Besides, the complexity is associated with the nature of the friction phenomenon. However, there are limited efforts to forecast the friction coefficient during the machining operations. In this work, the friction coefficients between the titanium alloy against zirconia ceramics lubricated by minimum quantity lubrication were recorded and measured using a universal mechanical tester pin-on-disc tribometer. Then, we proposed two models for forecasting the friction coefficient which are trained and tested on the recorded data. The two predictive models are based on autoregressive integrated moving average and gated recurrent unit deep neural network methods. The proposed models are evaluated through a set of exhaustive experiments. These experiments demonstrated that the proposed models can efficiently be used to reduce power consumption dedicated to monitoring the friction coefficients. Besides, they can reduce or avoid surface thermal damage by predicting the high level of friction coefficients in advance, which can be used as an alert to enable or readjust the lubrication parameters (fluid pressure, fluid flow rate, etc.) to maintain lower ranges of friction coefficients and power consumption.

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Section: Te Proposed Stacked Gru-dnn Modelmentioning

confidence: 99%

“…Tere are several applications for forecasting the friction coefcient in a cutting machine [6,10]. For instance, a lubrication system integrated into the machining cycle could be implemented.…”

Section: Introductionmentioning

confidence: 99%