Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process

Strojniski Vestnik/Journal of Mechanical Engineering - Slovenia
doi 10.5545/sv-jme.2019.6285
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Faculty of Mechanical Engineering

Related search

Feature Extraction Based on Stacked Auto Encoder for Protein Secondary Structure Prediction

2017English

Railway Traffic Accident Forecast Based on an Optimized Deep Auto-Encoder

Promet - Traffic - Traffico
CivilEngineeringOcean EngineeringStructural Engineering
2018English

Stacked Auto-Encoder Modeling of an Ultra-Supercritical Boiler-Turbine System

Energies
ControlElectronic EngineeringEnergy EngineeringRenewable EnergyEnergyFuel TechnologySustainabilityOptimizationElectricalPower Technologythe Environment
2019English

Estimation of Gait Normality Index Based on Point Clouds Through Deep Auto-Encoder

Eurasip Journal on Image and Video Processing
Electronic EngineeringSignal ProcessingInformation SystemsElectrical
2019English

In-Process Monitoring of Cutting Conditions and Tool Wear Using Acoustic Emission

1989English

A Neuro-Fuzzy Approach Based Tool Condition Monitoring in AISI H13 Milling

2018English

Multimodal Emotion Recognition Method Based on Convolutional Auto-Encoder

International Journal of Computational Intelligence Systems
Computational MathematicsComputer Science
2019English

A Deep Auto-Encoder Based Low-Dimensional Feature Extraction From FFT Spectral Envelopes for Statistical Parametric Speech Synthesis

2016English

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

Sensors
InstrumentationInformation SystemsElectronic EngineeringBiochemistryAnalytical ChemistryMolecular Physics,ElectricalAtomicMedicineOptics
2018English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy