Surface roughness assessment on hole drilled through the identification and clustering of relevant external and internal signal statistical features-Reference-Cited by-同舟云学术

Surface roughness assessment on hole drilled through the identification and clustering of relevant external and internal signal statistical features

Published:2022-01 Issue: Volume:36 Page:143-157
ISSN:1755-5817
Container-title:CIRP Journal of Manufacturing Science and Technology
language:en
Short-container-title:CIRP Journal of Manufacturing Science and Technology

Author:

Duo Aitor,Basagoiti Rosa,Arrazola Pedro J.,Cuesta Mikel,Illarramendi Miren

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering

Reference41 articles.

1. The Capacity of Statistical Features Extracted from Multiple Signals to Predict Tool Wear in the Drilling Process;Duo;International Journal of Advanced Manufacturing Technology,2019

2. Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition;Caggiano;Sensors,2018

3. Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring;Jauregui;IEEE Access,2018

4. ANN Tool Wear Modelling in the Machining of Nickel Superalloy Industrial Products;D’Addona;CIRP Journal of Manufacturing Science and Technology,2011

5. Tool Wear Modelling through Regression Analysis and Intelligent Methods for Nickel Base Alloy Machining;Leone;CIRP Journal of Manufacturing Science and Technology,2011

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