Journal ArticleDOI
Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining
Girish Kant,Kuldip Singh Sangwan +1 more
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TLDR
In this article, a multi-objective predictive model for the minimization of power consumption and surface roughness in machining, using grey relational analysis coupled with principal component analysis and response surface methodology, to obtain the optimum machining parameters.About:
This article is published in Journal of Cleaner Production.The article was published on 2014-11-15. It has received 215 citations till now. The article focuses on the topics: Machining & Energy consumption.read more
Citations
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Cryogenic and minimum quantity lubrication for an eco-efficiency turning of AISI 304
Octavio Pereira,Octavio Pereira,Alejandro Rodríguez,A.I. Fernández-Abia,Joaquín Barreiro,L.N. López de Lacalle +5 more
TL;DR: In this article, the use of combined techniques based on cryogenic cooling and minimum quantity of lubrication is proposed and compared with other near-to-dry coolant alternatives to evaluate the success of the proposed technique, technical feasibility and ecological footprint on the other should be analyzed.
Journal ArticleDOI
A method integrating Taguchi, RSM and MOPSO to CNC machining parameters optimization for energy saving
TL;DR: This paper presents a method for complex optimization of cutting parameters with the objectives of energy efficiency and processing time, which integrates Taguchi method, response surface method (RSM), and multi-objective particle swarm optimization algorithm (MOPSO).
Journal ArticleDOI
Optimization of Machining Parameters to Minimize Surface Roughness using Integrated ANN-GA Approach
TL;DR: In this paper, an approach for determining the optimum machining parameters leading to minimum surface roughness by integrating Artificial Neural Network (ANN) and Genetic Algorithm (GA) is presented.
Journal ArticleDOI
Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm
Girish Kant,Kuldip Singh Sangwan +1 more
TL;DR: In this paper, a predictive and optimization model was developed by coupling the two artificial intelligence approaches (i.e., artificial neural network and genetic algorithm) as an alternative to conventional approaches in predicting the optimal value of machining parameters leading to minimum surface roughness.
Journal ArticleDOI
Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation
TL;DR: In this article, the effect of input parameters: nose radius, cutting speed, feed rate and depth of cut along with their interactions were studied on the response parameters viz. power factor (PF), active power consumed by the machine (APCM), active energy consumed by a machine (AECM), energy efficiency (EE), surface roughness (Ra) and material removal rate (MRR).
References
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Book
Principal Component Analysis
TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
Reference EntryDOI
Principal Component Analysis
TL;DR: Principal component analysis (PCA) as discussed by the authors replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables.
Journal ArticleDOI
The Application of Electronic Computers to Factor Analysis
TL;DR: A survey of available computer programs for factor analytic computations and a analysis of the problems of the application of computers to factor analysis.
Book
Empirical Model-Building and Response Surfaces
TL;DR: In this article, the authors present a Second-Order Response Surface Methodology (SRSM) for response surface design, which is based on Maxima and Ridge systems with second-order response surfaces.
Journal ArticleDOI
Empirical Model-Building and Response Surfaces
TL;DR: This work discusses the use of Graduating Functions, design Aspects of Variance, Bias, and Lack of Fit, and Practical Choice of a Response Surface Design in relation to Second--Order Response Surfaces.
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