Modeling and optimization of high-grade compacted graphite iron milling force and surface roughness via response surface methodology

Lin, Yongchuan; Huang, Jing; Wei, Jueyu; Liao, Xiaoping; Xiao, Zeqing

doi:10.1080/14484846.2017.1296531

Cited by 8 publications

(1 citation statement)

References 17 publications

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“…RSM creates a second-order polynomial model for estimating the output responses. It is an important way to check the efficacy of mathematical models on the statistical backdrop [44][45][46]. While, ANN is a family of mathematical models inspired by the central nervous system of animals particularly brain.…”

Section: Predictive Modellingmentioning

confidence: 99%

Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface methodology

Singh

Hussain

Dash

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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Composite is an artificial multiphase material that constituent phases which are chemically dissimilar and separated by a distinct interface. As a result of such anisotropic and non-homogeneous formation machining of composites is a strenuous task. Therefore, the need arose for formulating a stable predictive model. The comprehensive intention of this study is to develop a highly robust and stable predictive soft-computing model for forecasting the machining performance of Al-5083 alloy reinforced with B 4 C particles (Al5083/B 4 C). The study mostly emphasize on selecting the best machining parameters among the conducted experiments and evaluating the optimal machining parameters for Al5083/B 4 C composite in wireelectro discharge machin. The paper is an integration of equally weighted experimental as well as computational study. In the experimental part of the study, 5 different specimen of Al5083/B 4 C is prepared by the ex situ technique through stir casting process. The experimental part includes design of experiment by Taguchi's method. The computational part of the study comprised of three different stages. The first stage involves the mathematical modelling of the performance measures and statistical scrutiny of the models. In the second stage, the best machining parameters are selected based on the fuzzy IFthen rules. The final stage of the manuscript is the trade-off analysis conducted to obtain the optimal machining parameters. In order to test the robustness of the formulated model an experimental validation is carried out at the optimal machining combination. The error calculated from the comparison is within the range of 2-5% which justifies the objective of the study.

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Section: Predictive Modellingmentioning

confidence: 99%