Investigation of novel sustainable concrete using optimization technique

Devi, V. Subathra; Gnanavel, B. K.; Murthi, P. Radha Krishna; Madhanagopal, M.

doi:10.5185/amp.2017/205

Cited by 3 publications

(1 citation statement)

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“…Some of the commonly applied methods for multiresponse optimization are Grey Relational Analysis (GRA), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Fuzzy logic, Principal Component Analysis (PCA), Simulated Annealing (SA), Bio-inspired algorithms, and Nelder-Mead Simplex method [2]. is sort of optimization study is used by researchers in various domains ranging from Civil Engineering [3] to advanced Additive Manufacturing techniques [4].…”

Section: Introductionmentioning

confidence: 99%

Statistical Modelling to Study the Implications of Coated Tools for Machining AA 2014 Using Grey Taguchi‐Based Response Surface Methodology

Manoharan

Kulandaivel²,

Arunagiri

et al. 2021

Advances in Materials Science and Engineering

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Milling is the surface machining process by removing material from the raw stock using revolving cutters. This process accounts for a major stake in most of the Original Equipment Manufacturing (OEM) industries. This paper discusses optimizing process parameters for machining the AA 2014 T 651 using a vertical milling machine with coated cutting tools. The process parameters such as cutting speed, depth of cut, and type of the cutting tool with all its levels are identified from the previous literature study and several trial experiments. The Taguchi L9 Orthogonal Array (OA) is used for the experimental order with the chosen input parameters. The commonly used cutting tools in the machining industry, such as High-Speed Steel (HSS) and its coated tools, are considered in this study. These tools are coated with Titanium Nitride (TiN) and Titanium Aluminum Nitride (TiAlN) by Physical Vapor Deposition (PVD) technique. The output responses such as cutting forces along the three-axis are measured using a milling tool dynamometer for the corresponding input factors. The input process parameters are optimized by considering the output responses such as MRR, machining torque, and thrust force. Grey Taguchi-based Response Surface Methodology (GTRSM) is used for multiobjective multiresponse optimization problems to find the optimum input process parameter combination for the desired response. Polynomial regression equations are generated to understand the mathematical relation between the input factor and output responses as well as Grey Relational Grade (GRG) values. The optimum process parameter combination from the desirability analysis is the HSS tool coated with TiAlN at a cutting speed of 270 rpm and a depth of cut value of 0.2 mm.

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Section: Introductionmentioning

confidence: 99%