| 000 | 05393nam a22006135i 4500 | ||
|---|---|---|---|
| 001 | 978-981-10-5583-6 | ||
| 003 | DE-He213 | ||
| 005 | 20210201191431.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 171230s2018 si | s |||| 0|eng d | ||
| 020 |
_a9789811055836 _9978-981-10-5583-6 |
||
| 050 | 4 | _aTS1-2301 | |
| 072 | 7 |
_aTGP _2bicssc |
|
| 072 | 7 |
_aTEC020000 _2bisacsh |
|
| 072 | 7 |
_aTGP _2thema |
|
| 082 | 0 | 4 |
_a670 _223 |
| 100 | 1 |
_aLu, Xinjiang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 245 | 1 | 0 |
_aModeling, Analysis and Control of Hydraulic Actuator for Forging _h[electronic resource] / _cby Xinjiang Lu, Minghui Huang. |
| 250 | _a1st ed. 2018. | ||
| 264 | 1 |
_aSingapore : _bSpringer Singapore : _bImprint: Springer, _c2018. |
|
| 300 |
_aX, 228 p. 115 illus., 93 illus. in color. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 347 |
_atext file _bPDF _2rda |
||
| 500 | _aAcceso multiusuario | ||
| 505 | 0 | _aPart One: Background and Fundamentals -- Introduction -- Literature Survey -- Part Two: Modelling for Forging Load and Processes -- Process/Shape-Decomposition Modeling for Deformation Force -- Online Probabilistic Extreme Learning Machine for Distribution Modeling of Batch Forging Processes -- Multi-level Parameter Identification Approach -- Multi-Experiment-Data Based SVD/NN Modeling Approach -- LS-SVM Modeling Method for a Hydraulic Press Forging Process with Multiple Localized Solutions -- Hybrid Model-Set/Data Online Modeling Approach for Time-Varying Forging -- Part Three: Dynamic Analysis for Forging Processes -- Model-based Dynamic Performance Analysis -- Closed-Loop Dynamic Performance Analysis -- Part Four: Intelligent Control for Complex Forging Processes -- System decomposition based multi-level control -- Intelligent integration control for Time-Varying Forging Process -- Conclusion. | |
| 520 | _aThis book aims to overcome the current shortcomings of modeling, analysis and control approaches, presenting contributions in three major areas: a) Several novel modeling approaches are proposed: a process/shape-decomposition modeling method to help estimate the deformation force, an online probabilistic learning machine for the modeling of batch forging processes, several data-driven identification and modeling approaches for unknown forging processes under different work conditions. b) The model-based dynamic analysis methods is developed to derive the conditions of stability and creep. c) Several novel intelligent control methods are proposed for complex forging processes. One of the most serious problems in forging forming involves the inaccurate forging conditions, velocity and position, offered by the hydraulic actuator due to the complexity of both the deformation process of the metal work piece and the motion process of the hydraulic actuator. The current weaknesses of modeling, analysis and control approaches are summarized as follows: a) With the current modeling approaches it is difficult to model complex forging processes with unknown parameters, as they only model the dynamics in local working areas but do not effectively model unknown nonlinear systems across multiple working areas; further, they do not take the batch forging process into account, let alone its distribution modeling. b) All previous dynamic analysis studies simplify the forging system to having a single-frequency pressure fluctuation and neglect the influences of non-linear load force. Further, they fail to take the flow equation in both valves and cylinders into account. c) Conventional control approaches only consider the linear deformation force and pay no attention to sudden changes and the motion synchronization for the multi-cylinder system, making them less effective for complex, nonlinear time-varying forging processes subject to sudden changes. | ||
| 541 |
_fUABC ; _cTemporal ; _d01/01/2021-12/31/2023. |
||
| 650 | 0 | _aManufactures. | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 0 | _aControl engineering. | |
| 650 | 0 | _aRobotics. | |
| 650 | 0 | _aMechatronics. | |
| 650 | 0 | _aElectronics. | |
| 650 | 0 | _aMicroelectronics. | |
| 650 | 1 | 4 |
_aManufacturing, Machines, Tools, Processes. _0https://scigraph.springernature.com/ontologies/product-market-codes/T22050 |
| 650 | 2 | 4 |
_aArtificial Intelligence. _0https://scigraph.springernature.com/ontologies/product-market-codes/I21000 |
| 650 | 2 | 4 |
_aControl, Robotics, Mechatronics. _0https://scigraph.springernature.com/ontologies/product-market-codes/T19000 |
| 650 | 2 | 4 |
_aElectronics and Microelectronics, Instrumentation. _0https://scigraph.springernature.com/ontologies/product-market-codes/T24027 |
| 700 | 1 |
_aHuang, Minghui. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811055829 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811055843 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811354342 |
| 856 | 4 | 0 |
_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-981-10-5583-6 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cLIBRO_ELEC | ||
| 999 |
_c243446 _d243445 |
||