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Electrochemical Machining Assisted U...
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Bradley, Curtis.
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Electrochemical Machining Assisted Using Pulsed Electric Fields, Ultrasonic Motion, and Magnetic Fields.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Electrochemical Machining Assisted Using Pulsed Electric Fields, Ultrasonic Motion, and Magnetic Fields./
作者:
Bradley, Curtis.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:
124 p.
附註:
Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.
Contained By:
Dissertation Abstracts International80-03B(E).
標題:
Mechanical engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10790404
ISBN:
9780438515628
Electrochemical Machining Assisted Using Pulsed Electric Fields, Ultrasonic Motion, and Magnetic Fields.
Bradley, Curtis.
Electrochemical Machining Assisted Using Pulsed Electric Fields, Ultrasonic Motion, and Magnetic Fields.
- Ann Arbor : ProQuest Dissertations & Theses, 2018 - 124 p.
Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.
Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018.
Electrochemical machining (ECM) is a non-traditional machining process that uses an anodic dissolution electrochemical cell to preferentially dissolve a positively charged metal workpiece using a negatively charged tool, thereby creating a precise shape with a good surface finish. ECM is currently used to manufacture superalloy turbine blisks and medical implants as well as other complex profiles requiring superb surface finishes.
ISBN: 9780438515628Subjects--Topical Terms:
649730
Mechanical engineering.
Electrochemical Machining Assisted Using Pulsed Electric Fields, Ultrasonic Motion, and Magnetic Fields.
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Electrochemical machining (ECM) is a non-traditional machining process that uses an anodic dissolution electrochemical cell to preferentially dissolve a positively charged metal workpiece using a negatively charged tool, thereby creating a precise shape with a good surface finish. ECM is currently used to manufacture superalloy turbine blisks and medical implants as well as other complex profiles requiring superb surface finishes.
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The research in this thesis presents a machining cell that combines pulsed electric waveforms, ultrasonic motion, and magnetic fields to form a novel, triad-assisted ECM cell. The pulsed electric field has a wide variety of asymmetric, bipolar configurations possible using two independent controlled power supplies. The workpiece is ultrasonically actuated from below the cell. Either a constant or sinusoidal magnetic field can be generated using an electromagnet or pair of permanent magnets, respectively.
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The electrochemical machining study presented in this thesis uses the novel testbed to reveal that phase-controlled waveform interactions between the three assistances affect both the material removal rate (MRR) and surface roughness (Ra) performance metrics. Experiments used a 7075 aluminum anode in an NaNO3 electrolyte with a 316 stainless steel cathode. The triad-assisted ECM case involving phase-specific combinations of all three high-frequency (15.625 kHz) assistance waveforms is found to be capable of achieving a 52% increase in MRR while also simultaneously yielding a 78% improvement in the surface roughness value over the baseline pulsed-ECM case. This result is encouraging because assisted ECM processes reported in literature typically improve only one of these performance metrics at the expense of the other.
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Finally, the thesis presents an ECM cell design methodology that spans two hierarchical time scales to effectively model magnetically assisted PECM. Total MRR is simulated in a large time scale model, on the order of the machining time duration (seconds). The diffusion layer thickness, simulated by a model on the order of the pulse duration, is then mapped to Ra by using an empirical function derived from the experimental data sets. The numerical models use characterizing minimum-order polynomials to estimate average conductivity, efficiency and peak current as functions of magnetic flux density and PECM voltage frequency. The models are then cross-validated by using 67% of the PECM test data for training the polynomial estimates. The average of the MRR and Ra model predictions are within 8% of the experimental value. The thesis concludes by presenting a microchannel use-case to demonstrate that capability of the proposed ECM cell design methodology to effectively navigate the complex ECM parameter space.
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