An Auto-Regressive Exogenous-Based Temperature Controller for a Hybrid Thermoplastic Microforming of Surgical Blades From Bulk Metallic Glass

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Nattasit Dancholvichit ◽  
Shiv Kapoor

Abstract Temperature control is critical in manufacturing of the multifacet bulk metallic glass (BMG) knife edge. The temperature control in thermoplastic forming process could make a significant effect on the type of deformation, which ultimately results in the final blade edge shape. The controller selection is based on the knowledge of the model from system identification, the performance of the controllers, and the feasibility of the implementation to the testbed. In this study, temperature control, using fuzzy logic, is implemented along with auto-regressive exogenous, ARX model, which can maintain the steady-state temperature within the range of ±2.5 K. With this proposed controller, experiments have shown similar or better results of multifacet blade geometries than those manufactured using proportional–integral–derivative (PID) controller. The blade edge samples are successfully manufactured with the average straightness and the edge radius of the blade of 3.66 ± 0.5 μm and 25.7 ± 6 nm, respectively.

2017 ◽  
Vol 5 (1) ◽  
Author(s):  
James Zhu ◽  
Shiv G. Kapoor

A hybrid thermoplastic forming process involving sequential micromolding and microdrawing operations is developed to manufacture the multifacet/curvilinear geometries found on most surgical blades. This is accomplished through an oblique drawing technique, i.e., drawing with a nonzero inclination angle. By applying time-varying force profiles during the drawing operation, a wide range of complex blade geometries is possible. Experiments have exhibited positive results across several multifacet and curvilinear blade geometries. Manufacturing process capabilities are quantitatively evaluated and experimental results have measured the bulk metallic glass (BMG) blade cutting edge radii to be consistently less than 15 nm, rake face surface roughness Ra to be on the order of 20 nm, and edge straightness deviations to be less than 5 μm root-mean-square (RMS) while retaining an amorphous atomic structure.


2021 ◽  
Vol 198 ◽  
pp. 109368
Author(s):  
Maximilian Frey ◽  
Jan Wegner ◽  
Nico Neuber ◽  
Benedikt Reiplinger ◽  
Benedikt Bochtler ◽  
...  

2008 ◽  
Vol 10 (11) ◽  
pp. 1048-1052 ◽  
Author(s):  
J. S.-C. Jang ◽  
C.-T. Tseng ◽  
L.-J. Chang ◽  
J. C.-C. Huang ◽  
Y.-C. Yeh ◽  
...  

2007 ◽  
Vol 539-543 ◽  
pp. 2129-2134
Author(s):  
Young Sang Na ◽  
S.G. Kang ◽  
K.Y. Park ◽  
Jong Hoon Lee

Micro-forming is considered to be a suited technology to manufacture very small metallic parts (several μm~mm). Zr-based bulk metallic glass, Zr62Cu17Ni13Al8, has been expected to be a promising metallic material for micro-forming process due to their isotropy, low flow stress in a wide supercooled liquid region and good stability of amorphous matrix. Therefore, one can expect that micro-forming of Zr62Cu17Ni13Al8 might be feasible at a relatively low stress in the supercooled liquid state without any crystallization during hot deformation. In this study, micro-formability of Zr62Cu17Ni13Al8 bulk metallic glass was investigated for micro-forging of U-shape pattern. Microformability was estimated by comparing Rf values (=Af/Ag), where Ag is corss-sectional area of U groove, and Af the filled area by material. Micro-forging process was also simulated and analyzed by applying the finite element method. The micro-formability of Zr62Cu17Ni13Al8 was increased with increasing load and time in the temperature range of the supercooled liquid state. In spite of the similar trend in the variations of Rf values, FEM simulation results showed much higher Rf values than the experimental Rf values. This disagreement was analyzed based on the stress overshoot phenomena of bulk metallic glasses in the supercooled liquid region. FEM simulation of the microstamping process was applicable for the optimization of micro-forming process by carefully interpreting the simulation results.


Author(s):  
R. Arison Kung ◽  
M.-L. Ted Guo ◽  
K. Chang ◽  
Chi-Y. Tsao ◽  
J. Huang ◽  
...  

2013 ◽  
Vol 61 (6) ◽  
pp. 1921-1931 ◽  
Author(s):  
N. Li ◽  
Y. Chen ◽  
M.Q. Jiang ◽  
D.J. Li ◽  
J.J. He ◽  
...  

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