Temperature regulation for thermoplastic micro-forming of bulk metallic glass: Robust control design using buck converter

Dancholvichit, Nattasit; Kapoor, Shiv Gopal; Salapaka, Srinivasa M.

doi:10.1016/j.jmapro.2020.04.024

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Owing to their unique structural features and outstanding functional and mechanical properties, bulk metallic glasses (BMGs) are known as promising materials for a wide range of applications [1][2][3][4][5]. However, it is hard to fabricate them with thickness in centimeters, which is due to their glass forming ability (GFA) [6].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Effects of Cr Microalloying on Structural Evolution, Crystallization Behavior and Micromechanical Properties of ZrCoAlCr Bulk Metallic Glass

Supriyono

Aravindhan

Thangavelu

et al. 2021

Trans Indian Inst Met

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This paper deals with the role of minor addition of Cr on the atomic rearrangement and crystallization evolution in a Zr-based bulk metallic glass. For this purpose, Cr elements with 1-4% atomic percentage were added into the alloying composition. The X-ray diffraction results indicated that the trace element led to the increase in disordered structures in the glassy alloy and the decline in population of short and medium scale orders. This event was due to the intensification of structural heterogeneity which was manifested in the change of enthalpy relaxation. The differential scanning calorimetry analysis also showed that all ZrCoAlCr BMG samples included two distinct crystallization peaks during the heating process. However, the increase in Cr content led to the intensification of low temperature peak. This result was consistent with viscosity analysis, in which the viscosity decrement was intensified at the range of low temperature crystallization peak with the rise in Cr content. Moreover, it was found that although the plasticity improved in samples with Cr-content, the strength showed a slight decrease.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Effects of Cr Microalloying on Structural Evolution, Crystallization Behavior and Micromechanical Properties of ZrCoAlCr Bulk Metallic Glass

Supriyono

Aravindhan

Thangavelu

et al. 2021

Trans Indian Inst Met

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“…Here, a feedback-based control system is developed to make drawing velocity Vt track Vreft accurately within operating bandwidths, thus facilitating consistent good quality necking of the blade formation. The hybrid thermoplastic forming testbed is explained in Krejcie et al 3 , Zhu and Kapoor, 4 and Dancholvichit et al 10 and the drawing module of the machine in this study is illustrated in Figure 2(a). The system consists of VCAs, which provide the necessary force to move the drawing module, and pneumatic, linear encoders, and force sensors that provide measurements for feedback, and a control algorithm implemented on a digital signal processor (LABVIEW).…”

Section: Overview Of the Controller Architecture And Controller Designmentioning

confidence: 99%

“…Earlier studies have shown that Do can play a significant role in the assumptions and the model. The 90° blade edges are manufactured from the BMG sample with Do of 15, 20, and 25 µm at temperature, Td of 683 ± 0.03 K. 10 Blade edges manufactured from Do of larger than 30 µm are found to be inconsistent due to the significant change in the drawing force over the drawing distance behavior dFd/dLp which is a time-consuming process to determine the duration at which the extensional flow is fully developed. 14 The blade quality assessment of 15 samples per Do is then performed and shown in Table 1.…”

Section: Application To the Bmg Forming Processmentioning

confidence: 99%

Thermoplastic micro-forming process of bulk metallic glass surgical blades using drawing velocity regulation

Dancholvichit

Salapaka

Kapoor

2022

Journal of Micromanufacturing

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Drawing velocity is essential in the manufacturing of the multi-facet bulk metallic glass (BMG) knife edges used in corneal surgery since it is one of the factors that can determine good blade profiles in the thermoplastic forming process. The goal of this article is to determine and regulate drawing velocity based on the viscoelastic filament stretching and knowledge of the thermoplastic forming map of BMG. The generation of drawing velocity profile is done in two stages: the initial transient stage before the extensional viscosity stage is fully developed, and the extensional viscosity stage. The controller based on the system identification of the testbed is then optimized from the requirements of the drawing velocity and its implementation feasibility. The control objectives of regulation performance and robustness to modeling uncertainties are posed and solved in an optimal control ( H∞) framework. The proposed controller shows an improvement over other controllers including proportional-integral-derivative controllers in terms of robustness to uncertainties and tracking performance. BMG samples processed from this study result in good quality with improvement of 20% and 54% in X–Y ([Formula: see text] µm) and X–Z ([Formula: see text]µm) straightness and 25% more consistent edge radii ([Formula: see text]nm).

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“…To ensure the desired output of the boost converter, control techniques of a pulse width modulated signal (PWM) are used. Different control techniques have been used to control the desired signals for the boost converter and other converters, such as the PI [8,[13][14][15][16], PID [17][18][19], frequency-domain control techniques such as the method of sliding modes [6,20,21], fuzzy logic [22,23], and inverse optimal control [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Robust Inverse Optimal Control for a Boost Converter

2021

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The variability of renewable energies and their integration into the grid via power electronics demands the design of robust control algorithms. This work incorporates two techniques to ensure the stability of a boost converter through its state equations, implementing the inverse optimal control and the gain-scheduling technique for robust control settings. In such a way that, under a single adjustment, it is capable of damping different changes such as changes in the parameters, changes in the load, the input voltage, and the reference voltage. On the other hand, inverse optimal control is based on a discrete-time control Lyapunov function (CLF), and CLF candidate depends on fixed parameters that are selected to obtain the solution for inverse optimal control. Once these parameters have been found through heuristic or artificial intelligence methods, the new proposed methodology is capable of obtaining a robust optimal control scheme, without having to search for new parameters through other methods, since these are sometimes sensitive changes and many times the process of a new search is delayed. The results of the approach are simulated using Matlab, obtaining good performance of the proposed control under different operation conditions. Such simulations yielded errors of less than 1% based on the voltage reference, given the disturbances caused by changes in the input variables, system parameters, and changes in the reference. Thus, applying the new methodology, the stability of our system was preserved in all cases.

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Temperature regulation for thermoplastic micro-forming of bulk metallic glass: Robust control design using buck converter

Cited by 6 publications

References 9 publications

RETRACTED ARTICLE: Effects of Cr Microalloying on Structural Evolution, Crystallization Behavior and Micromechanical Properties of ZrCoAlCr Bulk Metallic Glass

RETRACTED ARTICLE: Effects of Cr Microalloying on Structural Evolution, Crystallization Behavior and Micromechanical Properties of ZrCoAlCr Bulk Metallic Glass

Thermoplastic micro-forming process of bulk metallic glass surgical blades using drawing velocity regulation

Robust Inverse Optimal Control for a Boost Converter

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