scholarly journals A Correlation between Deformation-Induced Surface Roughness and In-Plane Plastic Strain in an Aluminum Alloy at the Mesoscale

2022 ◽  
Vol 35 ◽  
pp. 66-73
Author(s):  
V. Romanova ◽  
V. Shakhidzhanov ◽  
O. Zinovieva ◽  
O. Nekhorosheva ◽  
R. Balokhonov
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Plastic Strain ◽  
Plane Plastic

scholarly journals A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Material Removal Rate ◽  
Cutting Forces ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Data Driven ◽  
Optimal Cutting Parameters

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

Roller Burnishing Method with Active Rotation Tool - Better Surface Finish than Conventional Roller Burnishing

2017 ◽  
Vol 749 ◽  
pp. 9-14 ◽  
Author(s):  
Masato Okada ◽  
Yuki Miyagoshi ◽  
Masaaki Otsu
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Surface Finish ◽  
Circumferential Direction ◽  
Theoretical Equation ◽  
Sliding Angle ◽  
Roller Burnishing ◽  
Sliding Direction ◽  
Cylindrical Workpiece ◽  
Sectional Profile

This paper proposes a roller burnishing method that controls the sliding direction of the burnishing tool on the surface of cylindrical workpiece. In this study, the sliding direction was set by inclining the axis of the burnishing tool with respect to the axis of the workpiece and by actively rotating the roller of the burnishing tool. The workpiece was a cylindrical aluminum alloy bar, which was rotated in a bench lathe. The burnished surfaces at several sliding angles between 15º and 90º were evaluated. The sliding direction, which is set according to a theoretical equation, was experimentally obtained for every sliding angle in the range of 15-90º with respect to the circumferential direction of the workpiece. The sectional profile was flattened and surface roughness was decreased with increasing sliding angle. As a result, the burnished surfaces obtained in this work were superior to those obtained in an earlier study by the authors, in which the burnishing tool was not actively rotated.

Experimental Research on Surface Roughness of Ultrasonic Assisted Grinding on 7075 Aluminum Alloy

2021 ◽  
Vol 1047 ◽  
pp. 62-67
Author(s):  
Shen Wang ◽  
Le Tong ◽  
Guang Jun Chen ◽  
Mao Xun Wang ◽  
Bin Dai ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Processing Parameters ◽  
7075 Aluminum Alloy ◽  
Machined Surface ◽  
Ultrasonic Assisted Grinding ◽  
Great Performance ◽  
Ultrasonic Assisted ◽  
Conventional Grinding ◽  
Micro Morphology

7075 aluminum alloy is widely used due to its great performance, especially in aerospace area. In this paper, ultrasonic-assisted grinding technology is used to process 7075 aluminum alloy. The data is obtained through experiments, and the surface roughness and morphology of ultrasonic assisted grinding and conventional grinding under different spindle speeds, feed rates, and amplitudes are analyzed. Research has found that the increase in spindle speed and amplitude will improve the quality of the machined surface and reduce the surface roughness by 82.1% and 36%. However, with the increase of feed rate, the surface quality decreased significantly, and the surface roughness increased by 55.6%. The surface micro-morphology of the machined workpiece is observed, and the effects of different processing parameters on the surface micro-morphology are obtained.

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