Optimization of multiple performance characteristics in turning using Taguchi’s quality loss function: An experimental investigation

Sahoo, Ashok Kumar; Mohanty, Tanmaya

doi:10.5267/j.ijiec.2013.04.002

Cited by 4 publications

(4 citation statements)

References 14 publications

(12 reference statements)

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“…The RSM predicted and experimental surface roughness values were found to be very close. Sahoo and Mohanty (2013) obtained the optimal values of cutting speed, feed and depth of cut to minimize cutting force and chip reduction coefficient during orthogonal turning using Taguchi quality loss function. The effectiveness of the proposed methodology is illustrated through an experimental investigation in turning mild steel workpiece using high speed steel tool.…”

Section: Review Of Literaturementioning

confidence: 99%

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Sahoo¹,

Rout²,

Das³

2015

10.5267/j.ijiec

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The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R 2 = 0.99 close to unity). It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

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Section: Review Of Literaturementioning

confidence: 99%

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Sahoo¹,

Rout²,

Das³

2015

10.5267/j.ijiec

Self Cite

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“…MRR, wheel wear rate and average SR were subsequently optimized. Applying Taguchi quality loss function approach, Sahoo and Mohanty (2013) determined the optimal settings of cutting speed, feed and depth of cut so as to minimize cutting force and chip reduction coefficient in an orthogonal turning process. Bhuyan and Yadava (2014) considered applied voltage, electrolyte concentration, wire feed velocity and workpiece thickness as the input parameters, and SR and MRR as the responses in a travelling wire electro-chemical spark machining process.…”

Section: Literature Reviewmentioning

confidence: 99%

A multivariate quality loss function approach for parametric optimization of non-traditional machining processes

Chakraborty¹,

Das²

2018

10.5267/j.msl

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Due to various added advantages over the conventional material removal processes, non-traditional machining (NTM) processes have now been widely applied in different manufacturing industries. To achieve the desired response values, it is always recommended to operate these NTM processes at their optimal parametric settings. Various single response optimization techniques are already available to determine the optimal combinations of NTM process parameters for achieving maximum or minimum value of a single response. In this paper, a multivariate quality loss function approach is adopted for simultaneous optimization of responses for three NTM processes. It is observed that this approach outperforms the other multi-response optimization techniques, like desirability function, distance function and mean squared error methods with respect to the achieved response values. With modification of the corresponding objective function and constraints of the developed non-linear programming problem, it can be effectively applied to any non-traditional as well as conventional machining process as a multi-objective optimization tool.

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“…RSM technique was found to be better than Taguchi's method. Sahoo and Mohanty (2013) utilized the application of Taguchi's parameter design to optimize the parameters for individual responses. For multi-response optimization, Taguchi's quality loss function approach is proposed.…”

Section: Introductionmentioning

confidence: 99%

Application of Taguchi and regression analysis on surface roughness in machining hardened AISI D2 steel

Sahoo¹

2014

10.5267/j.ijiec

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The objective of the study is to assess the performance of multilayer coated carbide insert in the machining of hardened AISI D2 steel (53 HRC) using Taguchi design of experiment. The experiment was designed based on Taguchi L27 orthogonal array to predict surface roughness. The S/N ratio and optimum parametric condition are analysed. The analysis of variance has also been carried out to predict the significant factors affecting surface roughness. Based on Taguchi S/N ratio and ANOVA, feed is the most influencing parameter for surface roughness followed by cutting speed whereas depth of cut has least significant from the experiments. In regression model, the value of R 2 being 0.98 indicates that 98 % of the total variations are explained by the model. It indicates that the developed model can be effectively used to predict the surface roughness on the machining of D2 steel with 95% confidence intervals.

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Optimization of multiple performance characteristics in turning using Taguchi’s quality loss function: An experimental investigation

Cited by 4 publications

References 14 publications

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

A multivariate quality loss function approach for parametric optimization of non-traditional machining processes

Application of Taguchi and regression analysis on surface roughness in machining hardened AISI D2 steel

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