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Journal ArticleDOI

Tool failure diagnosis in milling using a neural network

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TLDR
In this paper, a multi-layer feed-forward neural network with the back-propagation algorithm is introduced for processing the characteristic patterns of the cutting force signal in milling operations.
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This article is published in Mechanical Systems and Signal Processing.The article was published on 1994-01-01. It has received 9 citations till now. The article focuses on the topics: Machining.

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Journal ArticleDOI

A neutral-network approach for the on-line monitoring of the electrical discharge machining process

TL;DR: In this paper, a feed-forward neural network is used to on-line monitor electrical discharge machining (EDM) processes, based on the neural network through the back-propagation learning algorithm.
Journal ArticleDOI

Prediction of tool breakage in face milling using support vector machine

TL;DR: In this paper, a support vector machine (SVM) was used to classify the feature of the cutting force signal for the prediction of tool breakage in face milling, which can be used to identify a milling cutter with or without tool breakages.
Journal ArticleDOI

Tool breakage diagnosis in face milling by support vector machine

TL;DR: In this paper, a support vector machine (SVM) is used to detect tool breakage in a wide range of face-milling operations, and the SVM can respond in real-time to automatically diagnose tool fracture under varying cutting conditions.
Journal ArticleDOI

Detection of Structural Damage in Medium Density Fiberboard Panels using Neural Network Method

TL;DR: In this paper, the authors used a neural network to detect low levels of damage in small samples of medium density fiberboard (MDF) using a three-layer back-propagation network.
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