Prediction of energy consumption of machine tools using multi-gene genetic programming

Pawanr, Shailendra; Garg, Girish Kant; Routroy, Srikanta

doi:10.1016/j.matpr.2022.01.156

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…Prevalent methods include RSM, 12 MRA, [13][14][15][16][17][18] artificial intelligent (AI) algorithm application methods such as ANN, Machine Learning (ML), [19][20][21][22][23][24][25][26] GP. [27][28][29][30][31][32][33][34][35] The MRA linear models proved to be very effective in empirical statistical problems because of its simplicity. MRA models have a smaller number of coefficients than other models.…”

Section: Introductionmentioning

confidence: 99%

“…Prevalent methods include RSM , 12 MRA , 13–18 artificial intelligent ( AI ) algorithm application methods such as ANN , Machine Learning ( ML ), 19–26 GP . 27–35…”

Section: Introductionmentioning

confidence: 99%

“…In machining, the GP method is widely applied to erect predictive models of objective functions in general and predict SR values in particular. 18,27–30 The GP model makes predictions with a deviation of less than 15% from the real data in Brezocnik et al 27 SR is studied in Oguz et al 28 during high-speed milling of aluminum alloys. The multi-stage GP is introduced in Amir and Amir 29 and is assessed to be more accurate than the standard GP and ANN through typical examples.…”

Section: Introductionmentioning

confidence: 99%

“…The multi-stage GP is introduced in Amir and Amir 29 and is assessed to be more accurate than the standard GP and ANN through typical examples. The energy consumption of the lathe is evaluated in Pawanr et al 30 based on the GP predictive model. This method shows the reliability and the mentioned problem can be applied in production.…”

Section: Introductionmentioning

confidence: 99%

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Predictive modeling of surface roughness in hard turning with rotary cutting tool based on multiple regression analysis, artificial neural network, and genetic programing methods

Bien

2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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This present paper deals with the results of setting up and evaluating the quality of surface roughness (SR) prediction models through hard turning with self-driven rotary cutting tools for 40X steel shaft with hardness 45HRC. The cutting parameters are considered to establish the SR prediction model include angle tilt of cutting tool axis, depth of cut, feed rate, and cutting speed. The predictive models (PM) are established utilizing Multi-variables Regression Analysis (MRA), Artificial Neural Network (ANN), and Genetic Programming (GP) methods. In this regard, four MRA, four ANN, and three GP structures are considered to select the most suitable model. The criteria to estimate the PM quality include Coefficient of Determination ( R2), Mean Square Error (MSE), and Mean Absolute Percent Error (MAPE). Whereby, two data sets were collected to construct regression models (RM) and to serve verification. That dataset was originated from 63 experiments (Ep) including 54 Ep for establishing PM and 9 extensive experiments (EEp) for testing PM. The prediction criteria of the MRA3 model gave the best results in four MRA models with R2 of 0.99, MSE of 0.042, and MAPE of 8.087%. The ANN1 gives the most reliable assessment criteria in four ANN models and the GP3 model gave the best in three GP models. The results of predicting the SR value between the selected MRA and ANN and GP models will be assessed in detail. Accordingly, the evaluation criteria of the ANN1 model are the best with the smallest MSE (0.032) and MAPE (7.207%). The MRA3 and GP3 have a lower confidence predictive criteria value than the ANN1 model.

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Section: Introductionmentioning

confidence: 99%

“…Prevalent methods include RSM , 12 MRA , 13–18 artificial intelligent ( AI ) algorithm application methods such as ANN , Machine Learning ( ML ), 19–26 GP . 27–35…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predictive modeling of surface roughness in hard turning with rotary cutting tool based on multiple regression analysis, artificial neural network, and genetic programing methods

Bien

2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…This statistical tool has been used by others to compare the material removal rate of 2 bars of steel machined by electric discharge 32 ; the roughness attained with two values of cut-off (0.08 and 0.25 mm) 19 ; the cutting efforts generated under two machining environments (dry and nanofluid), 33 and two tool coatings (AlTiN and TiAlCrN). 34 Besides, paired hypothesis testing was effective for inferring if a predictive model differs significantly (or not) from experimental measurements 35 or other models. 36 Therefore, this work focuses on answering the following research questions, do the alloys behave similarly, in terms of surface finish, when machined under the same conditions?…”

Section: Introductionmentioning

confidence: 99%

The role of roughness parameters in grading the machined surface quality in Ti-alloys

Carvalho

Horovistiz

Davim

2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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Titanium alloys are extensively employed in machining to produce high-value components, where surface roughness is crucial for their functionality. In this study, the roughness of Ti6Al4V and Ti6Al7Nb turned surfaces was analysed. The goal was to compare the alloy’s roughness behaviour when machined under the same conditions. Therefore, hypothesis testing was performed to assess the equality of the expected values and population median. It was found that, for some parameters, the mean ( RzI, Rt) and median ( Rq, RzD, Pt) roughness was statistically different when changing alloys. However, for others, it did not present a significant difference, in terms of mean ( RmD, Rp) and median ( Ra, Rpm). Consequently, a relevant insight from this study was that the phenomenon interpretation was affected by roughness parameter selection. On the other hand, as expected, the feed rate played a critical role in roughness control. It was observed that Ti6Al7Nb had a smoother topography ( Ra was 0.15 ± 0.01 μm and 0.62 ± 0.04 μm) than Ti6Al4V (0.31 ± 0.02 μm and 0.67 ± 0.03 μm) at 0.05 and 0.15 mm/rev. Yet, at 0.1 mm/rev, this behaviour changed, as Ti6Al4V had a smoother surface ( Ra was 0.40 ± 0.02 μm) than Ti6Al7Nb ( Ra was 0.51 ± 0.03 μm). Finally, the multi-optimization indicated that a low feed rate (0.05 mm/rev) and medium (60 m/min) and high (90 m/min) cutting speed led to lower roughness and higher material removal rate for turning Ti6Al7Nb and Ti6Al7V, respectively. The insights from this study may have a practical application in the manufacturing and quality control of biomedical components.

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Optimization techniques for energy efficiency in machining processes—a review

abdelaoui

Jabri

Barkany

2023

Int J Adv Manuf Technol

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Metal working process is one of the main activities in mechanical manufacturing industry; it is considered as a major consumer of energy and natural resources. In material removal process, the selection of cutting parameters and cooling or cutting liquid is necessary to save energy and achieve energy efficiency as well as sustainability. During the last two decades, the number of publications in this field has rapidly increased and has shown the importance of this research area. This review paper identifies and reviews in detail a total of 166 scientific studies which exhibit original contributions to the field and address multiple energy efficiency challenges. The recently developed models of energy consumption and different materials used in the machining process are presented. Therefore, this study describes various techniques for modeling and optimizing machining operations such as turning, milling, and drilling. Modeling techniques, experimental methods, multi-objective and single-objective optimization methods, and hybrid techniques optimization are presented in a detailed manner compared to previous review papers where only energy models are discussed. It can help practitioners and researchers to select the most appropriate approach for the desired experience and to highlight the progress of these methods in terms of machining energy efficiency. Additionally, this paper provides a review of different cutting fluids adopted in machining processes. This paper assists researchers and manufacturers in making advantageous technical decisions that have substantial economics in terms of energy saving.

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Prediction of energy consumption of machine tools using multi-gene genetic programming

Cited by 10 publications

References 17 publications

Predictive modeling of surface roughness in hard turning with rotary cutting tool based on multiple regression analysis, artificial neural network, and genetic programing methods

Predictive modeling of surface roughness in hard turning with rotary cutting tool based on multiple regression analysis, artificial neural network, and genetic programing methods

The role of roughness parameters in grading the machined surface quality in Ti-alloys

Optimization techniques for energy efficiency in machining processes—a review

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