scholarly journals Green Machining Characteristics Study of Al-6063 in CNC Milling Using Taguchi Method and Grey Relational Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
R. Suresh Kumar ◽  
S. Senthil Kumar ◽  
K. Murugan ◽  
Sintayehu Mekuria Hailegiorgis
Keyword(s):  
Grey Relational Analysis ◽  
Removal Rate ◽  
Minimum Quantity Lubrication ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Minimum Quantity ◽  
Relational Analysis ◽  
Green Machining ◽  
Multiresponse Optimization ◽  
Grey Relational

Green machining strategies in the manufacturing sector help to maintain the product value by considering the environmental impacts. Also, improvisation in the quality contribution of the parts can minimize the environmental consequences by improving resource efficiency, specifically in terms of coolants used in machining. Certain hazardous impacts have been witnessed because of longer exposure to such a machining environment. To address it, many researchers have concentrated on providing a healthy machining environment either by introducing dry machining or by minimum quantity lubrication (MQL). The proposed study addresses this context. The influence of these tactics on the attained surface quality of Al-6063 is quantified in this paper in terms of surface integrity (Ra) and removal rate of material (MRR). The study involves single-response optimization using the Taguchi design and multiresponse optimization using grey relational analysis (GRA). The results reveal that the depth of cut (Dc) and spindle speed (Ss) have the greatest impact on Ra and MRR. The machinability of Al-6063 is examined by considering the key machinability parameters, such as the spindle speed (Ss), feed rate (Fr), and the depth of cut (Dc), to arrive at the best possible surface roughness and removal rate of the material. As a typical Taguchi approach cannot perform multiresponse optimization, grey relational analysis is used. The grey relational analysis combined with Taguchi gives a novel methodology for multioptimization. The entire study is performed in dry condition and under minimum quantity lubrication. The results suggest that the responses are highly influenced by the depth of cut and spindle speed.

scholarly journals Multi Performance Optimizationof Shoulder Milling Process Parameters of AA6063 T6 Aluminium Alloy by Taguchi Based GRA

2019 ◽  
Vol 8 (10S) ◽  
pp. 420-425
Keyword(s):  
Process Parameters ◽  
Grey Relational Analysis ◽  
Removal Rate ◽  
Analysis Data ◽  
Milling Process ◽  
Depth Of Cut ◽  
Analysis Method ◽  
Relational Analysis ◽  
Grey Relational Grade ◽  
Grey Relational

In this paper, a grey relational analysis method based on Taguchi is proposed to improve the multi-performance characteristics of VMC shoulder milling process parameters in the processing of AA6063 T6. Taking into account four process parameters such as coolant, depth of cut,speed and feed, there are three level of each process parameter in addition to two levels of coolant. 18 experiments were used by L18 orthogonal array using the taguchi method. Multi-performance features like surface roughness and material removal rate are used. Grey Relational Analysis method is used to obtain the Grey Relational Grade, and the multiperformance characteristics of the process are pointed out. Then, the Taguchi response table method and ANOVA are used to analysis data. In order to ensure the validity of the test results, a confirmation test was conducted. The study also shows that this method can effectively improve the multi-function characteristics of shoulder milling process.In his work microstructure and mechanical properties of AA6063 T6before and after shoulder milling have been investigated.

scholarly journals Optimum combination of process parameters to optimize Surface Roughness and Chip Thickness during End Milling of Aluminium 6351-T6 Alloy Using Taguchi Grey Relational Analysis

2017 ◽  
Vol 8 (2) ◽  
pp. 287
Author(s):  
Reddy Sreenivasulu
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Feed Rate ◽  
End Milling ◽  
Chip Thickness ◽  
Optimum Combination ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Relational Analysis ◽  
Grey Relational

In any machining operations, quality is the important conflicting objective. In order to give assurance for high productivity, some extent of quality has to be compromised. Similarly productivity will be decreased while the efforts are channelized to enhance quality. In this study,  the experiments were carried out on a CNC vertical machining center (KENT and INDIA Co. Ltd, Taiwan make) to perform 10mm slots on Al 6351-T6 alloy work piece by K10 carbide, four flute end milling cutter as per taguchi design of experiments plan by L9 orthogonal array was choosen to determine experimental trials. Furthermore the spindle speed (rpm), the feed rate (mm/min) and depth of cut (mm) are regulated in these experiments. Surface roughness and chip thickness was measured by a surface analyser of Surf Test-211 series (Mitutoyo) and Digital Micrometer (Mitutoyo) with least count 0.001 mm respectively. Grey relational analysis was employed to minimize surface roughness and chip thickness by setting of optimum combination of machining parameters. Minimum surface roughness and chip thickness obtained with 1000 rpm of spindle speed, 50 mm/min feed rate and 0.7 mm depth of cut respectively. Confirmation experiments showed that Gray relational analysis precisely optimized the drilling parameters in drilling of Al 6351-T6 alloy. 

Modelling and Optimization of Machining parameters for Turning Automotive Shafts using RSM and Grey Relational Analysis

2020 ◽  
Vol 12 (4) ◽  
Author(s):  
S. Dinesh ◽  
K. Rajaguru ◽  
K. Saravanan ◽  
R. Yokeswaran ◽  
V. Vijayan
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Removal Rate ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Relational Analysis ◽  
Input Parameters ◽  
Grey Relational ◽  
Central Composite ◽  
Modelling And Optimization

Automotive shafts require maximum strength with regard to axial, bending and torsional loading to transmit power to various parts of a vehicle. Hence, it is very critical to analyse the manufacturing process and its governing parameters to exercise control over the surface properties of the shaft as it needs to be precisely manufactured in terms of dimensions and the surface roughness. The effect of three input parameters over two responses are considered as two major criteria's for production of shaft. The input parameters are speed, feed and depth of cut whereas the responses are material removal rate and surface roughness. Central Composite design was used and experimental results were analysed with Response Surface Methodology. ANOVA analysis was carried out to identify the most contributing parameter for MRR and SR. Grey Relational Analysis was adopted to identify the most feasible combination of machining parameters for turning process. The optimized parameter is identified as speed of 1000 rpm, 0.15 mm of feed and 0.35 mm of depth of cut using Grey Relational Analysis.

scholarly journals Implementation of MCDM model in lean manufacturing using grey relational analysis in automotive industry

2018 ◽  
Vol 7 (3.3) ◽  
pp. 48
Author(s):  
Venkataraman K ◽  
T V. Sivaramakrishnan ◽  
N Murugan ◽  
D Deepankar ◽  
B Srinivasa Raghavan
Keyword(s):  
Cycle Time ◽  
Grey Relational Analysis ◽  
Lean Manufacturing ◽  
Removal Rate ◽  
Depth Of Cut ◽  
Business Organizations ◽  
Production Methods ◽  
Relational Analysis ◽  
Manufacturing Techniques ◽  
Grey Relational

The present economic situation shows the consistent demand for profitable solutions that allow business organizations to gain better ad-vantage. Due to this reason, many organizations search for methodologies that allow them to improve their products and services improve their processes, reduce costs, and improve the profit and customer satisfaction. This has been implemented through lean techniques in their managerial and production methods. As we know Lean production system mainly emphasizes on the waste elimination, using simple and visual techniques. This paper is an attempt to optimize process layout and waste expulsion for automotive powertrain in a leading automo-tive company by using lean manufacturing techniques. In this paper, the existing process in place for machining of cam shaft is analysed and process layout is optimized to reduce cycle time and lead time. In order to reduce the cycle time, the process parameters such a speed, feed and depth of cut are optimized for increased material removal rate (MRR) and decreased surface roughness (RA) by using Grey Relational Analysis (GRA) technique. As MRR is inversely related to cycle time, optimization of parameters for higher MRR reduces the cycle time. Grey Relational Analysis is done by using Design Xpert software.  

Parametric Optimization of CNC End Milling Process on Aluminium 6063 Alloy Using Grey Based Taguchi Method

2017 ◽  
Vol 867 ◽  
pp. 148-156
Author(s):  
Md Ashfaq Hussain ◽  
K.K. Prasad ◽  
Anil S. Jadhav ◽  
Gangadhar Biradar
Keyword(s):  
Taguchi Method ◽  
Grey Relational Analysis ◽  
End Milling ◽  
Removal Rate ◽  
Milling Process ◽  
Depth Of Cut ◽  
Relational Analysis ◽  
Response Optimization ◽  
Grey Relational ◽  
Cnc End Milling

This investigation focused on the multi-response optimization of CNC end milling of Aluminium 6063 Alloy material using Grey relational analysis and Taguchi method. Experiments were designed based on L9 Taguchi Orthogonal array, to arrive at an optimum parameter combination within the experimental domain. The spindle speed (S), feed rate (f) and depth of cut (d) which are known to have considerable effect on the selected responses i.e. surface roughness (Ra) and Material removal rate (MRR) and are considered as control parameters. The single objective optimization using Taguchi method more often results in conflicting requirements on control variables. To overcome this challenge, the Taguchi approach followed by Grey relational analysis was applied to solve this multi response optimization problem. The significance of these factors on overall quality characteristics of the milling process has also been evaluated quantitatively with the Analysis of variance method (ANOVA). Optimal results were verified through confirmation experiments. This shows feasibility of the Grey relation analysis in combination with Taguchi technique for continuous improvement in product quality in manufacturing industry and the suitability of the method to optimize the multi objective problems involved in CNC milling.

scholarly journals Optimization of Machining Parameters using ANOVA and Grey Relational Analysis while Turning Aluminium 7075

2019 ◽  
Vol 8 (2) ◽  
pp. 5682-5686
Keyword(s):  
Grey Relational Analysis ◽  
High Speed ◽  
Removal Rate ◽  
High Speed Steel ◽  
Depth Of Cut ◽  
Total Production ◽  
Machining Parameters ◽  
Turning Operation ◽  
Relational Analysis ◽  
Grey Relational

In this research a detailed study is carried out on machining parameters for turning operation on aluminium 7075 with high speed steel. This grade of aluminium is known for its applications in aerospace industry and research about its machining parameters will lead to more developments in the field of production. Aim of this work is to optimize turning operation. Machining parameters viz. speed, feed and depth of cut are taken as input parameters. Material removal rate (MRR), tool wear (TWR), surface roughness (SR) are taken as output parameters and the set of optimized parameters means reduction in total production cost. The experiments are planned using Taguchi’s L9 orthogonal array. Grey relational analysis (GRA) is used for multi objective optimization using grey relational grades. Application of analysis of variance(ANOVA) helps in the identification of most prominent parameters among speed, feed and depth of cut

Optimization of Cutting Parameters of Multiple Performance Characteristics in End Milling of AlSi/AIN MMC – Taguchi Method and Grey Relational Analysis

2014 ◽  
Vol 68 (4) ◽  
Author(s):  
S. H. Tomadi ◽  
J. A. Ghani ◽  
C. H. Che Haron ◽  
M. S. Kasim ◽  
A. R. Daud
Keyword(s):  
Taguchi Method ◽  
Grey Relational Analysis ◽  
Volume Fraction ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Relational Analysis ◽  
Grey Relational

The main objective of this paper is to investigate and optimize the cutting parameters on multiple performance characteristics in end milling of Aluminium Silicon alloy reinforced with Aluminium Nitride (AlSi/AlN MMC) using Taguchi method and Grey relational analysis (GRA). The fabrication of AlSi/AlN MMC was made via stir casting with various volume fraction of particles reinforcement (10%, 15% and 20%). End milling machining was done under dry cutting condition by using two types of cutting tool (uncoated & PVD TiAlN coated carbide). Eighteen experiments (L18) orthogonal array with five factors (type of tool, cutting speed, feed rate, depth of cut, and volume fraction of particles reinforcement) were implemented. The analysis of optimization using GRA concludes that the better results for the combination of lower surface roughness, longer tool life, lower cutting force and higher material removal could be achieved when using uncoated carbide with cutting speed 240m/min, feed 0.4mm/tooth, depth of cut 0.3mm and 15% volume fraction of AlN particles reinforcement. The study confirmed that with a minimum number of experiments, Taguchi method is capable to design the experiments and optimized the cutting parameters for these performance characteristics using GRA for this newly develop material under investigation.

EXPERIMENTAL INVESTIGATION OF COCONUT OIL WITH NANOBORIC ACID DURING MILLING OF INCONEL 625 USING TAGUCHI-GREY RELATIONAL ANALYSIS

2020 ◽  
pp. 2150008
Author(s):  
T. MOHANRAJ ◽  
P. RAGAV ◽  
E. S. GOKUL ◽  
P. SENTHIL ◽  
K. S. RAGHUL ANANDH
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Flank Wear ◽  
Coconut Oil ◽  
Inconel 625 ◽  
Depth Of Cut ◽  
Individual Response ◽  
Relational Analysis ◽  
Grey Relational ◽  
Taguchi’S Design Of Experiments

This study is based on Taguchi’s design of experiments along with grey relational analysis (GRA) to optimize the milling parameters to minimize surface roughness, tool wear, and vibration during machining of Inconel-625 while using coconut oil as cutting fluid (CF). The experiments were conducted based on Taguchi’s L9 orthogonal array (OA). Taguchi’s S/N was used for identifying the optimal cutting parameter for individual response. Analysis of variance (ANOVA) was employed to analyze the outcome of individual parameters on responses. The surface roughness was mostly influenced by feed. Flank wear was influenced by speed and the vibration was mostly influenced by the depth of cut as well as speed. The multi-response optimization was done through GRA. From GRA, the optimal parameters were identified. Further, nanoboric acid of 0.5 and 0.9[Formula: see text]wt.% was mixed with coconut oil to enhance lubricant properties. Coconut oil with 0.5[Formula: see text]wt.% of nanoboric acid minimizes the surface roughness and flank wear by 3.92% and 6.28% and reduces the vibration in the [Formula: see text]-axis by 4.85%. The coconut oil with 0.5[Formula: see text]wt.% of nanoboric acid performs better than coconut oil with 0.9[Formula: see text]wt.% of nano boric acid and base oil.

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