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This study presents a robustness optimization method for rapid prototyping (RP) of functional artifacts based on visualized computing digital twins (VCDT). A generalized multiobjective robustness optimization model for RP of scheme design prototype was first built, where thermal, structural, and multidisciplinary knowledge could be integrated for visualization. To implement visualized computing, the membership function of fuzzy decision-making was optimized using a genetic algorithm. Transient thermodynamic, structural statics, and flow field analyses were conducted, especially for glass fiber composite materials, which have the characteristics of high strength, corrosion resistance, temperature resistance, dimensional stability, and electrical insulation. An electrothermal experiment was performed by measuring the temperature and changes in temperature during RP. Infrared thermographs were obtained using thermal field measurements to determine the temperature distribution. A numerical analysis of a lightweight ribbed ergonomic artifact is presented to illustrate the VCDT. Moreover, manufacturability was verified based on a thermal-solid coupled finite element analysis. The physical experiment and practice proved that the proposed VCDT provided a robust design paradigm for a layered RP between the steady balance of electrothermal regulation and manufacturing efficacy under hybrid uncertainties.
(© 2023. The Author(s).)
Electrons, Quantum Theory, and Software
Fanpy is a free and open-source Python library for developing and testing multideterminant wavefunctions and related ab initio methods in electronic structure theory. The main use of Fanpy is to quickly prototype new methods by making it easier to convert the mathematical formulation of a new wavefunction ansätze to a working implementation. Fanpy is designed based on our recently introduced Flexible Ansatz for N-electron Configuration Interaction (FANCI) framework, where multideterminant wavefunctions are represented by their overlaps with Slater determinants of orthonormal spin-orbitals. In the simplest case, a new wavefunction ansatz can be implemented by simply writing a function for evaluating its overlap with an arbitrary Slater determinant. Fanpy is modular in both implementation and theory: the wavefunction model, the system's Hamiltonian, and the choice of objective function are all independent modules. This modular structure makes it easy for users to mix and match different methods and for developers to quickly explore new ideas. Fanpy is written purely in Python with standard dependencies, making it accessible for various operating systems. In addition, it adheres to principles of modern software development, including comprehensive documentation, extensive testing, quality assurance, and continuous integration and delivery protocols. This article is considered to be the official release notes for the Fanpy library.
(© 2022 Wiley Periodicals LLC.)
Polydimethylsiloxane (PDMS) based microfluidic devices have found increasing utility for electrophoretic and electrokinetic assays because of their ease of fabrication using replica molding. However, the fabrication of high-resolution molds for replica molding still requires the resource-intensive and time-consuming photolithography process, which precludes quick design iterations and device optimization. We here demonstrate a low-cost, rapid microfabrication process, based on electrohydrodynamic jet printing (EJP), for fabricating non-sacrificial master molds for replica molding of PDMS microfluidic devices. The method is based on the precise deposition of an electrically stretched polymeric solution of polycaprolactone in acetic acid on a silicon wafer placed on a computer-controlled motion stage. This process offers the high-resolution (order 10 μ $\umu$ m) capability of photolithography and rapid prototyping capability of inkjet printing to print high-resolution templates for elastomeric microfluidic devices within a few minutes. Through proper selection of the operating parameters such as solution flow rate, applied electric field, and stage speed, we demonstrate microfabrication of intricate master molds and corresponding PDMS microfluidic devices for electrokinetic applications. We demonstrate the utility of the fabricated PDMS microchips for nonlinear electrokinetic processes such as electrokinetic instability and controlled sample splitting in ITP. The ability to rapid prototype customized reusable master molds with order 10 μ $\umu$ m resolution within a few minutes can help in designing and optimizing microfluidic devices for various electrokinetic applications.
(© 2023 Wiley-VCH GmbH.)
Intelligent sensor systems are essential for building modern Internet of Things applications. Embedding intelligence within or near sensors provides a strong case for analog neural computing. However, rapid prototyping of analog or mixed signal spiking neural computing is a non-trivial and time-consuming task. We introduce mixed-mode neural computing arrays for near-sensor-intelligent computing implemented with Field-Programmable Analog Arrays (FPAA) and Field-Programmable Gate Arrays (FPGA). The combinations of FPAA and FPGA pipelines ensure rapid prototyping and design optimization before finalizing the on-chip implementations. The proposed approach architecture ensures a scalable neural network testing framework along with sensor integration. The experimental set up of the proposed tactile sensing system in demonstrated. The initial simulations are carried out in SPICE, and the real-time implementation is validated on FPAA and FPGA hardware.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Mallan, Gopi, Reghuvaran, Radhakrishnan and James.)
We demonstrate a method to emulate the optical performance of silicon photonic devices fabricated using advanced deep-ultraviolet lithography (DUV) processes on a rapid-prototyping electron-beam lithography process. The method is enabled by a computational lithography predictive model generated by processing SEM image data of the DUV lithography process. We experimentally demonstrate the emulation method's accuracy on integrated silicon Bragg grating waveguides and grating-based, add-drop filter devices, two devices that are particularly susceptible to DUV lithography effects. The emulation method allows silicon photonic device and system designers to experimentally observe the effects of DUV lithography on device performance in a low-cost, rapid-prototyping, electron-beam lithography process to enable a first-time-right design flow.
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