Optimization of high-speed turning parameters for Inconel 713C based on Taguchi grey relational analysis

Parthiban, Velmurugan; Vijayakumar, S.; Sakthivel, M.

doi:10.1139/tcsme-2018-0221

Cited by 5 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6 with cutting speed (v) of 60 m/min, feed rate (f) of 0.3 mm/rev and depth-of-cut (d) of 0.25 mm were the attained optimal input machining parameters. Similar approach was used by Parthiban et al [11], Vasudevan et al [24], Pedkarand and Karidkar [25], Pawade and Joshi [26] and obtained results with their study were found to be in good agreement with experimental results attained in this study.…”

Section: Resultssupporting

confidence: 90%

“…The optimal process parameters were achieved (60 m/min for speed, 0.05 mm/rev for feed and 0.2 mm for depth of cut) from the selected range of L9 orthogonal array. Parthiban et al [11] employed Taguchi grey relational analysis for estimating the impact of turning Inconel 713C alloy with different tools (WC-Co tool and cryogenically treated and oil-quenched WC-Co tools). The analysis was performed with L27 orthogonal array with operating parameters (cutting speed, feed rate, and depth of cut) for recognizing the components influencing surface roughness.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of Machining Parameters using Taguchi Coupled Grey Relational Approach while Turning Inconel 625

Padhy¹,

Singh²

2021

JMechE

View full text Add to dashboard Cite

In manufacturing industries preparation of quality surfaces is very important. The surface roughness will influence the quality and effectiveness of the subsequent coatings for protection against corrosion, wear resistance etc. For achieving desired surface roughness, factors like cutting force (N) and material removal rate (mm3/sec) plays an important role towards final product optimization. This study helps to determine the contribution of each machining parameters [cutting speed (v), feed rate (f) and depth-of-cut (d)] and their interaction to investigate their optimum values during dry turning of Inconel 625 with the objective of enhancing the productivity (optimum production) by minimizing surface roughness (Ra), cutting forces (Fc), whereas maximizing material removal rate (MRR). This kind of multi response process variable (MRP) problems usually known as multi-objective optimizations (MOOs) are resolved with the help of Taguchi and Grey relation approach (T-GRA). As a result, the attained optimum cutting parameters are viz. cutting speed (60 m/min), feed rate (0.3 mm/rev), depth-of-cut (0.25 mm) lead to value of desired variables - cutting forces (340 N), surface roughness (0.998 μm) and material removal rate (0.786 mm3/min).

show abstract

Section: Resultssupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Optimization of Machining Parameters using Taguchi Coupled Grey Relational Approach while Turning Inconel 625

Padhy¹,

Singh²

2021

JMechE

View full text Add to dashboard Cite

show abstract

“…This is in good agreement with recent researches conducted by Zhuang et al [20] and Suresh Kumar et al [22]. In addition, this hybrid technique for optimization (Taguchi-Grey) considerably improves the optimization of the output process responses; this also is in good agreement with the findings of Kumar and Dhanabalan [38], and Parthiban et al [39].…”

Section: Accuracy and Prediction Errors Comparisonsupporting

confidence: 92%

Single and multiple quality characteristics optimization, expanded to the machinability assessment at the optimal cutting combinations across Taguchi OA, GRA and BBD: an overall view

Tebassi¹,

Yallese²,

Belhadi³

2022

Preprint

View full text Add to dashboard Cite

The Inconel 718 is among the most used alloy in several industrial fields, so it was necessary to develop appropriate modeling, optimization, and prediction methods during the turning of this material; in terms of minimum experiments for giving good competitiveness to final products. In this research paper, turning experiments have been performed under a dry environment with different conditions of cutting speed, feed rate, and depth of cut, using Taguchi design along with Grey Relational Analysis and Box-Behnken design when turning of Inconel 718. Initially, design evaluation is achieved based on their fraction of design space graphs. Then, optimization single and multi-objective are satisfied, and a general comparison of short and long-term is achieved apropos optimal cutting regimes. Mono-objective optimization through Taguchi orthogonal array shows the carelessness of responses outside the optimization object. On the other hand, multi-objective optimization relating to Taguchi-Grey produces better results regarding its reduced number of experiments. The Box-Behnken design implements a good fit for multi-attribute optimization. Nevertheless, its higher number of experiments must be considered regarding their costs. Moreover, a comparative study for the short and long term can demonstrate a suitable and accurate approach regarding its optimal cutting combination. The Grey-Taguchi approach displays good results in terms of multi-attribute optimization against the Box-Behnken design. This judgment is based on the balancing of the obtained accuracy regarding the smallest design. However, the tool wear long-term test is integrated as a criterion of better machinability relating to the optimal cutting combinations.

show abstract

“…The experimental approach, as the basis of scientific research, is increasingly supplemented with modeling and optimization methods. Response surface methodology [21], artificial neural network [22], fuzzy method [23], gray relational analysis [24], particle swarm optimization [25] and genetic algorithm [26] are widely used to reduce the cost and time of experimental research.…”

Section: Introductionmentioning

confidence: 99%

The Modelling of Surface Roughness After the Turning of Inconel 601 by Using Artificial Neural Network

Jovicic,

Milosevic,

Sokac

et al. 2023

J. Mater. Eng.

View full text Add to dashboard Cite

This research includes longitudinal turning of Inconel 601 in a dry environment with PVD coated cutting inserts. Turning was performed for different levels of cutting speeds, feeds, depth of cuts and corner radius. After turning, the arithmetical mean surface roughness was measured. Mean arithmetic surface roughness values ranging from 0.156 μm to 6.225 μm were obtained. Based on the obtained results, an artificial neural network (ANN) was created. This ANN model was used to predict surface roughness after machining for different variants of input variables. Performance evaluation of the generated model was performed on the basis of additional - confirmation experiments. The mean absolute errors are 0.005 μm and 0.012 μm for the training and confirmation experiments, respectively. The mean percentage errors are 0.894 % and 1.303 % for the training and confirmation experiments, respectively. The obtained results showcase the possibility of practical application of the developed ANN model.

show abstract

Optimization of high-speed turning parameters for Inconel 713C based on Taguchi grey relational analysis

Cited by 5 publications

References 15 publications

Optimization of Machining Parameters using Taguchi Coupled Grey Relational Approach while Turning Inconel 625

Optimization of Machining Parameters using Taguchi Coupled Grey Relational Approach while Turning Inconel 625

Single and multiple quality characteristics optimization, expanded to the machinability assessment at the optimal cutting combinations across Taguchi OA, GRA and BBD: an overall view

The Modelling of Surface Roughness After the Turning of Inconel 601 by Using Artificial Neural Network

Contact Info

Product

Resources

About