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1. Ultrasonic vibration-assisted high-resolution electrohydrodynamic (EHD) printing

   https://doi.org/10.1016/j.mfglet.2024.09.112  

   Jiang, Qingrui; Cao, Ruofan; Wang, Yi; Han, Yiwei (October 2024, Manufacturing Letters)

   Electrohydrodynamic (EHD) printing has become a promising and cost-effective technique for producing high-resolution and large-scale features. One widely recognized obstacle in EHD printing is nozzle clogging due to solvent evaporation or ink polymerization. Moreover, printing highly viscous materials often requires pressure or other external force to assist the ink flow during the printing, which increases the complexity of process control and the required energy. In this work, we developed a novel ultrasonic vibration-assisted EHD printhead and associated process to effectively eliminate the nozzle clogging for the printing of high-viscosity and high-evaporation-rate inks. A series of experimental tests were conducted to characterize the printhead design and process parameters (i.e., vibration frequency, vibration amplitude, and printing voltage). The results demonstrated that superimposing ultrasonic vibration on the EHD printing nozzle can effectively enhance current EHD printing capabilities, such as reducing required pressure, eliminating nozzle clogging, and providing stable and continuous printing for high viscosity and high solvent evaporation rate material. With the optimal parameters, a filament with a diameter of around 1 µm can be continuously printed. In the paper, we successfully applied this developed ultrasonic-assisted EHD process to print high-resolution 2D patterns. 

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2. Multi-sensor fusion and machine learning-driven sequence-to-sequence translation for interpretable process signature prediction in machining

   https://doi.org/10.1016/j.jmsy.2024.04.010  

   Cooper, Clayton; Zhang, Jianjing; Ragai, Ihab; Gao, Robert X (August 2024, Journal of Manufacturing Systems)

   During machining, kinetic energy is imparted to a workpiece to remove material. The integrity of the machined surface, which depends on the energy transfer, affects the quality and performance of the product, therefore needs to be quantified. Prior studies have indicated the potential of using machining power, or the power consumption at the tool-chip interface, as a process signature for predicting machined surface integrity. However, direct measurement of machining power is constrained by the availability of special equipment and the associated cost. To address this gap, this paper presents a machine learning-based method for machining power prediction through multi-sensor fusion and sequence-to-sequence translation from acoustic and vibration signals, which represent portions of the in-situ kinetic energy dissipation, to the machining power signal as a process signature. Specifically, a neural network architecture is developed to separately translate the acoustic and vibration signals to corresponding machining power signals. The two predicted power signals are subsequently fused to arrive at a unified power signal prediction. To check for spurious decision logic, the sensor fusion model is interpreted using integrated gradients to reveal temporal regions of the input data which have the most influence on the machining power prediction accuracy of the fusion model. Systematic cutting experiments performed on a lathe using 1018 steel have shown that the developed sensor fusion method for process signature prediction can successfully map machine acoustics to power consumption with 5.6% error, tool vibration to power consumption with 8.2% error, and acoustics and vibration, jointly, to power with 2.5% error. Model parameter interpretation reveals that the vibration signal is more influential on the machining power prediction result than the acoustic signal, but that overall model accuracy is diminished if only the vibration signal is used. 

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3. Direct Measurement of Tool-Chip Contact Stresses in Machining Using Full-Field Photoelasticity

   https://doi.org/10.1115/MSEC2024-124356  

   Mathews, Jobin T; Sagapuram, Dinakar (June 2024, American Society of Mechanical Engineers)

   Tool-chip contact stresses are of major interest in developing a basic understanding of the mechanics of machining. The interfacial and sliding conditions along the tool-chip contact in machining differ significantly from that of conventional, lightly loaded, tribological contacts in two major aspects — the occurrence of plastic flow (in the chip) at the sliding interface and intimate nature of the contact where apparent and real contact areas are the same. In this study, we present an experimental method for direct measurement of the tool-chip contact stresses. This involves the use of sapphire as a cutting tool coupled with digital photoelasticity to obtain full-field principal stress difference (isochromatics) and principal stress directions (isoclinics). This enables direct full-field characterization of the tool-chip contact stresses, as well as stresses within the cutting tool, at a micron-scale resolution not achieved previously. Our results show that the shear stress exhibits a maximum at a small distance from the tool tip, while the normal stress decreases monotonically with increasing distance from the tool tip. The maximum shear stress shows a good correlation with the shear flow stress of the material that is being machined. We also briefly discuss applications of the method to derive the stress distribution at the tool flank face and quantify frictional dissipation at both the contacts — tool-chip contact and flank-machined surface contact. 

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4. Electric-Field and Mechanical Vibration-Assisted Atomic Force Microscope-Based Nanopatterning

   https://doi.org/10.1115/1.4056731  

   Zhou, Huimin; Jiang, Yingchun; Ke, Changhong; Deng, Jia (June 2022, Journal of Micro and Nano-Manufacturing)

   Abstract Atomic force microscope (AFM)-based nanolithography is a cost-effective nanopatterning technique that can fabricate nanostructures with arbitrary shapes. However, existing AFM-based nanopatterning approaches have limitations in the patterning resolution and efficiency. Minimum feature size and machining performance in the mechanical force-induced nanofabrication process are limited by the radius and sharpness of the AFM tip. Electric-field-assisted atomic force microscope (E-AFM) nanolithography can fabricate nanopatterns with features smaller than the tip radius, but it is very challenging to find the appropriate input parameter window. The tip bias range in E-AFM process is typically very small and varies for each AFM tip due to the variations in tip geometry, tip end diameter, and tip conductive coating thickness. This paper demonstrates a novel electric-field and mechanical vibration-assisted AFM-based nanofabrication approach, which enables high-resolution (sub-10 nm toward sub-5 nm) and high-efficiency nanopatterning processes. The integration of in-plane vibration with the electric field increases the patterning speed, broadens the selectable ranges of applied voltages, and reduces the minimum tip bias required for nanopatterning as compared with E-AFM process, which significantly increases the versatility and capability of AFM-based nanopatterning and effectively avoids the tip damage. 

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5. Face Turning of Single Crystal (111)Ge: Cutting Mechanics and Surface/Subsurface Characteristics

   https://doi.org/10.1115/1.4057054  

   Zare, A.; Tunesi, M.; Harriman, T. A.; Troutman, J. R.; Davies, M. A.; Lucca, D. A. (July 2023, Journal of Manufacturing Science and Engineering)

   Abstract Single crystal Ge is a semiconductor that has broad applications, especially in manipulation of infrared light. Diamond machining enables the efficient production of surfaces with tolerances required by the optical industry. During machining of anisotropic single crystals, the cutting direction with respect to the in-plane lattice orientation plays a fundamental role in the final quality of the surface and subsurface. In this study, on-axis face turning experiments were performed on an undoped (111)Ge wafer to investigate the effects of crystal anisotropy and feedrate on the surface and subsurface conditions. Atomic force microscopy and scanning white light interferometry were used to characterize the presence of brittle fracture on the machined surfaces and to evaluate the resultant surface roughness. Raman spectroscopy was performed to evaluate the residual stresses and lattice disorder induced by the tool during machining. Nanoindentation with Berkovich and cube corner indenter tips was performed to evaluate elastic modulus, hardness, and fracture toughness of the machined surfaces and to study their variations with feedrate and cutting direction. Post-indentation studies of selected indentations were also performed to characterize the corresponding quasi-plasticity mechanisms. It was found that an increase of feedrate produced a rotation of the resultant force imparted by the tool indicating a shift from indentation-dominant to cutting-dominant behavior. Fracture increased with the feedrate and showed a higher propensity when the cutting direction belonged to the <112¯> family. 

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