Additive Manufacturing of Micro-Electro-Mechanical Systems (MEMS)

3D-Printed MEMS in Italy

MEMS devices are more and more commonly used as sensors, actuators, and microfluidic devices in different fields like electronics, opto-electronics, and biomedical engineering. Traditional fabrication technologies cannot meet the growing demand for device miniaturisation and fabrication time reduction, especially when customised devices are required. That is why additive manufacturing technologies are increasingly applied to MEMS. In this review, attention is focused on the Italian scenario in regard to 3D-printed MEMS, studying the techniques and materials used for their fabrication. To this aim, research has been conducted as follows: first, the commonly applied 3D-printing technologies for MEMS manufacturing have been illustrated, then some examples of 3D-printed MEMS have been reported. After that, the typical materials for these technologies have been presented, and finally, some examples of their application in MEMS fabrication have been described. In conclusion, the application of 3D-printing techniques, instead of traditional processes, is a growing trend in Italy, where some exciting and promising results have already been obtained, due to these new selected technologies and the new materials involved.

A 3D-printed microhemispherical shell resonator with electrostatic tuning for a Coriolis vibratory gyroscope

The emergence of microhemispherical resonant gyroscopes, which integrate the advantages of exceptional stability and long lifetime with miniaturization, has afforded new possibilities for the development of whole-angle gyroscopes. However, existing methods used for manufacturing microhemispherical resonant gyroscopes based on MEMS technology face the primary drawback of intricate and costly processing. Here, we report the design, fabrication, and characterization of the first 3D-printable microhemispherical shell resonator for a Coriolis vibrating gyroscope. We remarkably achieve fabrication in just two steps bypassing the dozen or so steps required in traditional micromachining. By utilizing the intricate shaping capability and ultrahigh precision offered by projection microstereolithography, we fabricate 3D high-aspect-ratio resonant structures and controllable capacitive air gaps, both of which are extremely difficult to obtain via MEMS technology. In addition, the resonance frequency of the fabricated resonators can be tuned by electrostatic forces, and the fabricated resonators exhibit a higher quality factor in air than do typical MEMS microhemispherical resonators. This work demonstrates the feasibility of rapidly batch-manufacturing microhemispherical shell resonators, paving the way for the development of microhemispherical resonator gyroscopes for portable inertial navigation. Moreover, this particular design concept could be further applied to increase uptake of resonator tools in the MEMS community.

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.

A Printing Strategy for Embedding Conductor Paths into FFF Printed Parts

A novel approach to manufacture components with integrated conductor paths involves embedding and sintering an isotropic conductive adhesive (ICA) during fused filament fabrication (FFF). However, the molten plastic is deposited directly onto the adhesive path which causes an inhomogeneous displacement of the uncured ICA. This paper presents a 3D printing strategy to achieve a homogeneous cross-section of the conductor path. The approach involves embedding the ICA into a printed groove and sealing it with a wide extruded plastic strand. Three parameter studies are conducted to obtain a consistent cavity for uniform formation of the ICA path. Specimens made of polylactic acid (PLA) with embedded ICA paths are printed and evaluated. The optimal parameters include a groove printed with a layer height of 0.1 mm, depth of 0.4 mm, and sealed with a PLA strand of 700 µm diameter. This resulted in a conductor path with a homogeneous cross-section, measuring 660 µm ± 22 µm in width (relative standard deviation: 3.3%) and a cross-sectional area of 0.108 mm2 ± 0.008 mm2 (relative standard deviation 7.2%). This is the first study to demonstrate the successful implementation of a printing strategy for embedding conductive traces with a homogeneous cross-sectional area in FFF 3D printing.

Additive manufacturing of Zn with submicron resolution and its conversion into Zn/ZnO core-shell structures

Electrohydrodynamic redox 3D printing (EHD-RP) is an additive manufacturing (AM) technique with submicron resolution and multi-metal capabilities, offering the possibility to switch chemistry during deposition "on-the-fly". Despite the potential for synthesizing a large range of metals by electrochemical small-scale AM techniques, to date, only Cu and Ag have been reproducibly deposited by EHD-RP. Here, we extend the materials palette available to EHD-RP by using aqueous solvents instead of organic solvents, as used previously. We demonstrate deposition of Cu and Zn from sacrificial anodes immersed in acidic aqueous solvents. Mass spectrometry indicates that the choice of the solvent is important to the deposition of pure Zn. Additionally, we show that the deposited Zn structures, 250 nm in width, can be partially converted into semiconducting ZnO structures by oxidation at 325 °C in air.