Multi-response optimisation of hard milling process parameters based on integrated Box-Behnken design with desirability function approach

Tamilarasan, A.; Marimuthu, K.

doi:10.1504/ijmmm.2014.060555

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…From this comparison it is clear that the chosen technique succeeded in predicting the cutting temperature with a very low prediction error compared to the published works. Works that should be considered competitive with the literature are the RSM method of (Tamilarasan et al, 2016), the RSM method coupled with gray relational analysis of (Tamilarasan & Marimuthu, 2014) and Polynomial Regression by Ji et al 2021, and the ANN-GA method of (Kumar et al, 2018).…”

Section: Defuzzificationmentioning

confidence: 99%

Multi-input fuzzy inference system based model to predict the cutting temperature when milling AISI 1060 steel

ABDELKRIM,

BELLOUFI

et al. 2023

JART

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The increase in the cutting temperature during milling has harmful effects which negatively affect the technical and economic machining characteristics such as: residual stresses, dimensions of machined parts and tools life. The nature of milling operations and the tool geometry make it difficult to predict or measure the temperature during the machining process, which is why great attention has been paid to measurement and prediction methodologies of cutting temperature during milling. In this work, a new intelligent identification technique of the cutting temperature based on the fuzzy set theory has been proposed to replace the strategy based on the operator qualification. This technique uses a fuzzy multiple input inference system to determine the influence of the cutting parameters on the cutting temperature. The fuzzy modeling is based on an experimental database resulting from the non-contact measurement of cutting temperature using an infrared camera with an emissivity setting adapted to the material. The results of the fuzzy system show that the fuzzy model is able to specify results providing a very good correlation between the experimental data and those predicted. The average error of the model was approximately 2.242%. The parameters used for the validation of the model were different from the data used for the construction of the fuzzy rules. The results showed that the most important parameter on the cutting temperature is depth of cut. The results obtained in this paper show that the developed model can be applied to predict the cutting temperature with precision during the milling process.

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Section: Defuzzificationmentioning

confidence: 99%

Multi-input fuzzy inference system based model to predict the cutting temperature when milling AISI 1060 steel

ABDELKRIM,

BELLOUFI

et al. 2023

JART

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show abstract

“…RSM is an experimental methodology that is mostly used to evaluate and solve problems by examining relations between predictors and responses to identify variables that significantly influence the quality and properties of the product [6]. Box-Behnken design (BBD) investigates the relationships of multiple variables of description with one or more variables of response.…”

Section: Rsm-based Box-behnken Designmentioning

confidence: 99%

“…Box-Behnken design (BBD) investigates the relationships of multiple variables of description with one or more variables of response. The key BBD range should be limited to a situation where drastic responses are not expected [6]. This present analysis has chosen and assigned four FDM parameters as X 1 , X 2 , X 3 and X 4 , respectively.…”

Section: Rsm-based Box-behnken Designmentioning

confidence: 99%

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Moth-Flame Optimization Algorithm for Improving the Surface Roughness on FDM Processed Parts

Tamilarasan

Rajamani

Pranay

et al. 2020

Springer Proceedings in Materials

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This paper presents an optimal method for determining the best FDM processing parameters by minimizing the surface roughness using a nature-inspired moth-flame optimization algorithm. The experiment design is formulated using the Box-Behnken design. The quadratic model derived from ANOVA results shows that the model was significant and satisfactory. Moth-flame optimization algorithm is effectively implemented and found to be an excellent optimization tool for fast convergence and global optima location.

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“…The analysis was carried out using combination of response surface methodology (RSM) and evolutionary optimization techniques. Tamilsaran et al [9,10] also carried out multi objective optimization of work piece surface temperature, cutting forces and sound pressure level. The material used for this study was 100MnCrW4 tool steel.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Process Parameters During End Milling and Prediction of Work Piece Temperature Rise

Bhirud¹,

Gawande²

2017

Archive of Mechanical Engineering

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During the machining processes, heat gets generated as a result of plastic deformation of metal and friction along the tool-chip and tool-work piece interface. In materials having high thermal conductivity, like aluminium alloys, large amount of this heat is absorbed by the work piece. This results in the rise in the temperature of the work piece, which may lead to dimensional inaccuracies, surface damage and deformation. So, it is needed to control rise in the temperature of the work piece. This paper focuses on the measurement, analysis and prediction of work piece temperature rise during the dry end milling operation of Al 6063. The control factors used for experimentation were number of flutes, spindle speed, depth of cut and feed rate. The Taguchi method was employed for the planning of experimentation and L 18 orthogonal array was selected. The temperature rise of the work piece was measured with the help of K-type thermocouple embedded in the work piece. Signal to noise (S/N) ratio analysis was carried out using the lower-the-better quality characteristics. Depth of cut was identified as the most significant factor affecting the work piece temperature rise, followed by spindle speed. Analysis of variance (ANOVA) was employed to find out the significant parameters affecting the work piece temperature rise. ANOVA results were found to be in line with the S/N ratio analysis. Regression analysis was used for developing empirical equation of temperature rise. The temperature rise of the work piece was calculated using the regression equation and was found to be in good agreement with the measured values. Finally, confirmation tests were carried out to verify the results obtained. From the confirmation test it was found that the Taguchi method is an effective method to determine optimised parameters for minimization of work piece temperature.

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Multi-response optimisation of hard milling process parameters based on integrated Box-Behnken design with desirability function approach

Cited by 12 publications

References 25 publications

Multi-input fuzzy inference system based model to predict the cutting temperature when milling AISI 1060 steel

Multi-input fuzzy inference system based model to predict the cutting temperature when milling AISI 1060 steel

Moth-Flame Optimization Algorithm for Improving the Surface Roughness on FDM Processed Parts

Optimization of Process Parameters During End Milling and Prediction of Work Piece Temperature Rise

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