Analysis of tool wear and surface roughness in high-speed milling process of aluminum alloy Al6061

Nguyen, Nhu-Tung; Dung, Ho Trung; Tung, Nguyen Tien; Luan, Nguyen Duc

doi:10.21303/2461-4262.2021.001824

Cited by 4 publications

(4 citation statements)

References 22 publications

(34 reference statements)

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“…In another series of experiments examining the effect of machining time as well as cutting parameters on tool wear and surface roughness during high-speed milling of Al6061 with face carbide inserts, it was found that the most significant factors in connection with surface roughness were the axial depth of cut and feed rate [25]. In the present study, no such dependency was found.…”

Section: The Effect Of Axial Depth Of Cut On Surface Roughnesscontrasting

confidence: 56%

“…Another example of research in this field involved investigating how the relative position of the face mill and the machined piece, as well as the milling kinematics (conventional vs climb), impact the various components of cutting force and the surface roughness when performing face milling with a milling width greater than the diameter of the cutter, as opposed to them being equal [24]. Another study investigated how changing the cutting parameters and machining time affects tool wear and surface roughness during milling [25]. In the case of another study, a high-feed milling cutter allowed for a two-fold increase in the volume of cut and higher machining efficiency but resulted in greater surface roughness and vibration amplitudes [26].…”

Section: Introductionmentioning

confidence: 99%

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Investigation of the Impact of Face Milling Parameters on the Roughness of the Machined Surface for 1.4301 Steel

Bembenek¹,

Dzienniak²,

Dzindziora³

et al. 2023

Adv. Sci. Technol. Res. J.

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The objective of this research was to analyze how different milling parameters impact the roughness of the surface produced during the machining process. Kinematic parameters, such as cutting speed and feed per tooth, as well as geometric parameters, such as axial and radial depth of machining, were considered in various configurations to determine which one had the greatest impact on the surface quality of 1.4301 stainless steel (also known as AISI 304, among other designations). This type of steel is commonly used in a number of industries, such as construction, automotive, food, chemical, decoration, oil, and petrochemical, owing to its favorable properties. It is also relatively cheap. The analyzed roughness parameters included Ra, Rq, Rz, Rt, which, considered collectively, provide a comprehensive picture of the overall surface quality. Based on the results, feed per tooth is the one parameter that was to a large degree responsible for the overall quality roughness of the surface of the analyzed samples. The remaining tested parameters also had an impact on the surface quality, which resulted in a dynamic increase or decrease in roughness (extremes), but not to the same degree as in the case of feed per tooth. At one point, for a relatively low axial depth of cut, a sudden increase in the resulting roughness was recorded.

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Section: The Effect Of Axial Depth Of Cut On Surface Roughnesscontrasting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Investigation of the Impact of Face Milling Parameters on the Roughness of the Machined Surface for 1.4301 Steel

Bembenek¹,

Dzienniak²,

Dzindziora³

et al. 2023

Adv. Sci. Technol. Res. J.

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show abstract

“…However, traditional machining is considered to have low e ciency, especially from the perspectives of machining cost and surface quality. As an alternative, high-speed milling can provide surface quality and gloss comparable to those obtained with a grinding method [2]. Moreover, high-speed milling allows us to obtain a better surface-roughness finish and better geometric accuracy than traditional machining methods.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies on high-speed milling have commonly been based on mathematical models and single-objective optimization [2,27,28]. However, to our knowledge, research on multiobjective optimization in high-speed milling 6061 aluminum alloy is rare, especially with a combination of machine learning and multiobjective optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II

Nguyen

et al. 2022

Advances in Materials Science and Engineering

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This work addresses the prediction and optimization of average surface roughness (Ra) and maximum flank wear (Vbmax) of 6061 aluminum alloy during high-speed milling. The investigation was done using a DMU 50 CNC 5-axis machine with Ultracut FX 6090 fluid. Four factors were examined: the table feed rate, cutting speed, depth of cut, and cutting length. Three levels of each factor were examined to conduct 81 experiment runs. The response parameters in these experiments were measurements of Ra and Vbmax. We applied a two-pronged approach that combines machine learning (ML) and a Nondominated Sorting Genetic Algorithm (NSGA-II) to model and optimize Ra and Vbmax. Four ML models were used to predict Ra and Vbmax: linear regression (LIN), support vector machine regression (SVR), a gradient boosting tree (GBR), and an artificial neural network (ANN). The input variables were the significant factors that affect the surface quality and tool wear: the feed rate, depth of cut, cutting speed, and cutting time. Several quality metrics were employed to quantify the performance of the models, such as the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). As a result, SVR and ANN were found to have the best predictive performance for Ra and Vbmax. These models and the NSGA-II-based approach were then employed for multiobjective optimization of cutting parameters during high-speed milling of aluminum 6061. Fifty Pareto solutions were found with Ra in the range of 0.257 to 0.308 µm and Vbmax in the range of 136.198 to 137.133 μm. Experimental validations were then conducted to confirm that the optimum solution was within an acceptable error range. More precisely, the absolute percentage errors for Ra and Vbmax were 2.5% and 1.5%, respectively. This work proposes an effective strategy for efficiently combining machine learning techniques and the NSGA-II multiobjective optimization algorithm. The experimental validations have reflected the potential for applying this strategy in various machining-optimization problems.

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Prediction of surface roughness in duplex stainless steel face milling using artificial neural network

Vasconcelos,

Francisco,

da Costa

et al. 2024

Int J Adv Manuf Technol

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Analysis of tool wear and surface roughness in high-speed milling process of aluminum alloy Al6061

Cited by 4 publications

References 22 publications

Investigation of the Impact of Face Milling Parameters on the Roughness of the Machined Surface for 1.4301 Steel

Investigation of the Impact of Face Milling Parameters on the Roughness of the Machined Surface for 1.4301 Steel

Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II

Prediction of surface roughness in duplex stainless steel face milling using artificial neural network

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