Machining Dynamics and Parameters Process Optimization

As we move further into the 21st century, despite the fact that new technologies have emerged, machining remains the key operation to achieve high productivity and precision for high-added value parts in several sectors, but recent advances in computer applications should close the gap between simul...

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Bibliographic Details
Other Authors: Urbicain, Gorka (Editor), Olvera Trejo, Daniel (Editor)
Format: Book Chapter
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
Online Access:Get Fullteks
DOAB: description of the publication
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020 |a books978-3-0365-0295-3 
020 |a 9783036502946 
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041 0 |a English 
042 |a dc 
072 7 |a TBX  |2 bicssc 
100 1 |a Urbicain, Gorka  |4 edt 
700 1 |a Olvera Trejo, Daniel  |4 edt 
700 1 |a Urbicain, Gorka  |4 oth 
700 1 |a Olvera Trejo, Daniel  |4 oth 
245 1 0 |a Machining Dynamics and Parameters Process Optimization 
260 |a Basel, Switzerland  |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2021 
300 |a 1 electronic resource (190 p.) 
506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a As we move further into the 21st century, despite the fact that new technologies have emerged, machining remains the key operation to achieve high productivity and precision for high-added value parts in several sectors, but recent advances in computer applications should close the gap between simulations and industrial practices. This book, "Machining Dynamics and Parameters Process Optimization", is oriented toward the different strategies and paths when it comes to increasing productivity and reliability in metal removal processes. The topics include the dynamic characterization of machine tools, experimental dampening techniques, and optimization algorithms combined with signal monitoring. 
540 |a Creative Commons  |f https://creativecommons.org/licenses/by/4.0/  |2 cc  |4 https://creativecommons.org/licenses/by/4.0/ 
546 |a English 
650 7 |a History of engineering & technology  |2 bicssc 
653 |a CNC parameters 
653 |a machining mode 
653 |a high speed 
653 |a high accuracy 
653 |a high surface quality 
653 |a five-axis linear-segment toolpath 
653 |a path smoothing 
653 |a B-spline curve-fitting 
653 |a path synchronization 
653 |a feedrate scheduling 
653 |a flute-grinding 
653 |a evolution algorithms 
653 |a wheel location and orientation 
653 |a thin-floor machining 
653 |a chatter 
653 |a magnetorheological damper 
653 |a bull-nose end mill 
653 |a tool wear monitoring 
653 |a milling 
653 |a complex part 
653 |a deep learning 
653 |a autoencoder 
653 |a deep multi-layer perceptron 
653 |a tool condition monitoring 
653 |a tool change policy 
653 |a Industry 4.0 
653 |a machine learning 
653 |a CNN 
653 |a AI 
653 |a additive manufacturing 
653 |a thin walled machining 
653 |a dynamics 
653 |a machining cycle optimization 
653 |a multivariable tool 
653 |a stable peninsula 
653 |a homotopy perturbation method 
653 |a machining robot 
653 |a natural frequency prediction 
653 |a model optimization 
653 |a dynamic performance 
653 |a stability 
653 |a machining 
653 |a grinding 
653 |a n/a 
856 4 0 |a www.oapen.org  |u https://mdpi.com/books/pdfview/book/3521  |7 0  |z Get Fullteks 
856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/68501  |7 0  |z DOAB: description of the publication