Journal ArticleDOI
Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network
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In this article, a multilayer perceptron model was used with back-propagation algorithm using the input parameters of nose radius, cutting speed, feed and volume of material removed.About:
This article is published in Measurement.The article was published on 2014-05-01. It has received 120 citations till now. The article focuses on the topics: Tool wear & Surface roughness.read more
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Journal ArticleDOI
Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network
Mozammel Mia,Nikhil Ranjan Dhar +1 more
TL;DR: In this article, an ANN based predictive model of surface roughness in turning hardened EN 24T steel has been presented by using Neural Network Tool Box 7 of MATLAB R2015a for different levels of cutting speed, feed rate, material hardness and cutting conditions.
Journal ArticleDOI
Tool wear predictability estimation in milling based on multi-sensorial data
TL;DR: In this article, the limitations of tool wear prediction on the milling of CGI 450 plates, through the simultaneous detection of acceleration and spindle drive current sensor signals, have been investigated, by utilizing the experimental results that derived from third degree regression models and pattern recognition systems.
Journal ArticleDOI
Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM
TL;DR: Predictive models like response surface methodology, artificial neural network and support vector machine were used to predict the surface roughness and root mean square of work piece vibration.
Journal ArticleDOI
Artificial neural networks for vibration based inverse parametric identifications
TL;DR: This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems and the adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail.
Journal ArticleDOI
Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks
TL;DR: This demonstrative example proves the feasibility of retrofitting older machines, supported by the strongly growing field of computational intelligence and big data analysis, to enable older machines towards Industry 4.0.
References
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Book
Elements of artificial neural networks
TL;DR: This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of supervised learning of neural networks.
Journal ArticleDOI
Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments
TL;DR: In this article, a neural network modeling approach is presented for the prediction of surface roughness (Ra) in CNC face milling using the Taguchi design of experiments (DoE) method.
Journal ArticleDOI
The use of artificial neural networks in materials science based research
Wei Sha,K.L. Edwards +1 more
TL;DR: The authors bring the problem of misapplication of neural network methodologies to the attention of the materials research community in order to elaborate the issues and hopefully prevent others and potentially new researchers from continued misuse of neural networks in the future.
Journal ArticleDOI
An in-process surface recognition system based on neural networks in end milling cutting operations
TL;DR: In this paper, an in-process surface recognition system was developed to predict the surface roughness of machined parts in the end milling process to assure product quality and increase production rate by predicting the surface finish parameters in real time.
Journal ArticleDOI
Optimization of machining parameters in magnetic force assisted EDM based on Taguchi method
TL;DR: In this article, a versatile process of electrical discharge machining (EDM) using magnetic force assisted standard EDM machine has been developed, and the effects of magnetic force on EDM machining characteristics were explored.
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Tuğrul Özel,Yiğit Karpat +1 more