CNC Turning Parameter Optimization for Surface Roughness of Aluminium-2014 Alloy Using Taguchi Methodology

With the increase in demand for producing products with good surface finish within reduced time. Selection of suitable parameters for machining in CNC lathe is much required from the operator’s point of view. To attain the desired parameters this study deals taguchi technique as a tool in process parameter optimization using carbon insert CNMG tool in turning of AA6063 alloy. L9 orthogonal array is selected for conducting the experiments and for this Feed, Cutting Speed, Depth of Cut are the process parameters. The ANOVA and S-N ratio is used to analyse the characteristic performance of the turning operation performed. From the results of ANOVA the most influential parameter in reducing the surface roughness is found as depth of cut. Finally for various process parameter combinations the surface roughness is calculated and the optimum parameter for machining is attained by using Taguchi. For authentication of the optimum output attained in optimization experiments have been conducted to get maximum surface finish when machining the aluminium alloy in CNC turning

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OPTIMISATION OF SAND CASTING PROCESS FOR IMPELLER USING TAGUCHI METHODOLOGY

NED University Journal of Research ◽

10.35453/nedjr-ascn-2019-0041 ◽

2020 ◽

Vol XVII (2) ◽

pp. 23-33

Author(s):

Faisal Hafeez ◽

Salman Hussain ◽

Wasim Ahmad ◽

Mirza Jahanzaib

Keyword(s):

Surface Roughness ◽

Surface Defects ◽

A356 Alloy ◽

Casting Process ◽

Sand Casting ◽

Gate Length ◽

Taguchi Methodology ◽

Confirmatory Tests ◽

Binder Ratio ◽

Response Measures

This paper presents the study to investigate the effects of binder ratio, in-gate length and pouring height on hardness, surface roughness and casting defects of sand casting process. Taguchi methodology with L9 orthogonal array was employed to design the experimentation. Sand casting of six blade impeller using A356 alloy was performed and empirical models for all the above response measures were formulated. Confirmatory tests and analysis of variance results confirmed the accuracy of the model. Binder ratio was found to be the most significant parameter affecting casting surface defects and surface roughness. This was followed by pouring height and in-gate length.

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Investigation of Two-Dimensional Ultrasonic Surface Burnishing Process on 7075-T6 Aluminum

Chinese Journal of Mechanical Engineering ◽

10.1186/s10033-021-00540-z ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Zhenyu Zhou ◽

Qiuyang Zheng ◽

Cong Ding ◽

Guanglei Yu ◽

Guangjian Peng ◽

...

Keyword(s):

Surface Roughness ◽

Parameter Optimization ◽

Wear Mechanism ◽

Feed Rate ◽

Sample Surface ◽

Original Sample ◽

Two Dimensional ◽

Delamination Wear ◽

Treated Sample ◽

Burnishing Process

AbstractA novel two-dimensional ultrasonic surface burnishing process (2D-USBP) is proposed. 7075-T6 aluminum samples are processed by a custom-designed 2D-USBP setup. Parameter optimization of 2D-USBP is conducted to determine the best processing strategy of 7075-T6 aluminum. A uniform design method is utilized to optimize the 2D-USBP process. U13(133) and U7(72) tables are established to conduct parameter optimization. Burnishing depth, spindle speed, and feed rate are taken as the control parameters. The surface roughness and Vickers hardness are taken as the evaluation indicators. It establishes the active control models for surface quality. Dry wear tests are conducted to compare the wear-resistance of the 2D-USBP treated sample and the original sample. Results show that the machining quality of 2D-USBP is best under 0.24 mm burnishing depth, 5000 r/min spindle speed, and 25 mm/min feed rate. The surface roughness Sa of the sample is reduced from 2517.758 to 50.878 nm, and the hardness of the sample surface is improved from 167 to 252 HV. Under the lower load, the wear mechanism of the 2D-USBP treated sample is mainly abrasive wear accompanied by delamination wear, while the wear mechanism of the original sample is mainly delamination wear. Under the higher load, the accumulation of frictional heat on the sample surface transforms the wear mechanisms of the original and the 2D-USBP treated samples into thermal wear.

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Hybrid Monitoring of Surface Roughness and Straightness in CNC Turning of Aluminium using Neural Networks Approach

Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference ◽

10.1145/3375959.3375964 ◽

2019 ◽

Author(s):

Somkiat Tangjitsitcharoen

Keyword(s):

Neural Networks ◽

Surface Roughness ◽

Cnc Turning

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Monitoring of Cutting Conditions with Dry Cutting on CNC Turning Machine

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.443.382 ◽

2010 ◽

Vol 443 ◽

pp. 382-387 ◽

Cited By ~ 6

Author(s):

Somkiat Tangjitsitcharoen ◽

Suthas Ratanakuakangwan

Keyword(s):

Surface Roughness ◽

Tool Wear ◽

Cutting Forces ◽

Cutting Temperature ◽

Cutting Conditions ◽

Cutting Condition ◽

Nose Radius ◽

Dry Cutting ◽

Cnc Turning ◽

Coated Carbide

This paper presents the additional work of the previous research in order to verify the previously obtained cutting condition by using the different cutting tool geometries. The effects of the cutting conditions with the dry cutting are monitored to obtain the proper cutting condition for the plain carbon steel with the coated carbide tool based on the consideration of the surface roughness and the tool life. The dynamometer is employed and installed on the turret of CNC turning machine to measure the in-process cutting forces. The in-process cutting forces are used to analyze the cutting temperature, the tool wear and the surface roughness. The experimentally obtained results show that the surface roughness and the tool wear can be well explained by the in-process cutting forces. Referring to the criteria, the experimentally obtained proper cutting condition is the same with the previous research except the rake angle and the tool nose radius.

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OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2016-0072 ◽

2016 ◽

Vol 40 (5) ◽

pp. 883-895 ◽

Cited By ~ 1

Author(s):

Wen-Jong Chen ◽

Chuan-Kuei Huang ◽

Qi-Zheng Yang ◽

Yin-Liang Yang

Keyword(s):

Surface Roughness ◽

Particle Swarm Optimization ◽

Predictive Models ◽

Particle Swarm ◽

Optimal Solution ◽

Cutting Parameters ◽

Optimization Methods ◽

Swarm Optimization ◽

Cnc Turning ◽

Multi Objective

This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

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