Editorial for the Special Issue on Advances in Micro and Nano Manufacturing: Process Modeling and Applications, Volume II
- Laser Texturing: Femtosecond laser ablation of metals is a precise method used to create microfeatures on a material’s surface with a minimized heat-affected zone [7]. Vanwersch et al. developed and validated a pulse-based two-temperature model to predict ablated geometry based on material and laser parameters [Contribution 2]. The model was implemented for grooves with varying hatch pitches, numbers of passes, and scanning directions. The model was corrected using experimental data and the profile shape, depth, and width were accurately predicted. Gao et al. used a femtosecond laser to texture metallic molds for micro-injection molding [Contribution 3]. Different hierarchical texture geometries were obtained using direct laser ablation to generate micro-scale features; then, the ultrashort pulsed laser was used to create laser-induced periodic surface structures (LIPSSs). The hierarchical texture designs were selected to achieve wetting functionalization while keeping a low aspect ratio for easier replication during molding [8]. Indeed, the aspect ratio is the main factor limiting the replication of textured mold surfaces [9].
- Micro- and Precision Injection Molding: Jung and Kim simulated the deformation of a hot runner manifold and nozzle assembly during operation to address potential leaks and premature failure for high-precision applications [Contribution 4]. The simulation results accurately predicted the gap between the manifold and the nozzle bushing. It was reported that the deformation as a result of the melt pressure did not exceed 12% of that achieved through thermal loading. The results are essential as hot runners play a significant role in precision molding applications [10] and sustainable plastics [11]. Gao et al. studied the replication of a textured surface with micro-molding of different polymers [Contribution 3]. The replication rate provided key information about the ability of the polymer to flow into the micro-scale cavities, creating the desired microfeatures on the plastic molded parts. The replication depended on the orientation of inserts for those with directional geometry. Higher hesitation and lower replication characterized the texture geometries that promoted air entrapment [12,13].
- Micro-Milling: Liu et al. established a mathematical model to predict chatter vibration and improve the accuracy of micro-milling [Contribution 5]. The model considered the centrifugal force induced by the rotational speed, the gyroscopic effect, and the tool runout, which are the main parameters affecting the process [14]. Comparison with the experiments demonstrated good accuracy and allowed the investigation of model performance under different milling conditions. Zhou et al. developed a 2D mesoscopic-based model to predict the effect of particle shape when cutting SiC P/Al composites [Contribution 6]. Experiments were conducted to evaluate the effect of different particle geometries on the modeled material removal prediction. The results highlight the deformation of the particles during machining and their impact on the cutting force.
- Grinding: Wang et al. conducted dry grinding experiments using an Fe-Cr-Co permanent magnet alloy to study the effect of processing conditions on surface roughness [Contribution 7]. The grinding force signals were correlated with the rotational speed of the grinding wheel and the surface roughness. The results indicated that increasing the grinding wheel speed could reduce the difference in grinding force between the peak and valley.
- Electro-Discharge Machining: Quarto et al. proposed the use of an Artificial Neural Network (ANN), together with Particle Swarm Optimization (PSO) and a Finite Element Model (FEM), to predict the process performances for the Micro-Electrical Discharge Machining (micro-EDM) drilling process [Contribution 8]. A comparison of the different models suggested that the integrated ANN-PSO methodology is more accurate in performance prediction. However, a large amount of historical data was required for the ANN training. The FEM model was also more complex to set up due to the need for accurate material characterization and the high computational time [15].
- Additive Manufacturing: Chen et al. studied ejection cycle time and droplet diameter prediction for E-jet printing [Contribution 9]. Different machine learning models were evaluated and compared using experiments to benchmark the performance for varying processing conditions. Mader et al. used Fused Deposition Modeling (FMD) 3D printing to manufacture PS microfluidic channels with dimensions as small as 300 µm and high transparency [Contribution 10]. Moreover, other functional chip designs were demonstrated. Cell culture experiments demonstrated cell adhesion and proliferation.
Conflicts of Interest
List of Contributions
- Yang, L.; Ding, B.; Liao, W.; Li, Y. Identification of preisach model parameters based on an improved particle swarm optimization method for piezoelectric actuators in micro-manufacturing stages. Micromachines 2022, 13, 698.
- Vanwersch, P.; Nagarajan, B.; Van Bael, A.; Castagne, S. Three-Dimensional Pulse-Based Modelling of Femtosecond Laser Ablation of Metals: Validation with Grooves. Micromachines 2023, 14, 593.
- Gao, P.; MacKay, I.; Gruber, A.; Krantz, J.; Piccolo, L.; Lucchetta, G.; Pelaccia, R.; Orazi, L.; Masato, D. Wetting characteristics of laser-ablated hierarchical textures replicated by micro injection molding. Micromachines 2023, 14, 863.
- Jung, J.S.; Kim, S.K. Numerical Simulation of Deformation in Hot Runner Manifold. Micromachines 2023, 14, 1337.
- Liu, X.; Liu, D.; Du, C.; Li, Y.; Wang, C.; Fu, Z. Dynamic Modeling for Chatter Analysis in Micro-Milling by Integrating Effects of Centrifugal Force, Gyroscopic Moment, and Tool Runout. Micromachines 2024, 15, 244.
- Zhou, J.; Lin, J.; Lu, M.; Jing, X.; Jin, Y.; Song, D. Analyzing the effect of particle shape on deformation mechanism during cutting simulation of SiC P/Al Composites. Micromachines 2021, 12, 953.
- Wang, N.; Jiang, F.; Zhu, J.; Xu, Y.; Shi, C.; Yan, H.; Gu, C. Experimental Study on the Grinding of an Fe-Cr-Co Permanent Magnet Alloy under a Small Cutting Depth. Micromachines 2022, 13, 1403.
- Quarto, M.; D’Urso, G.; Giardini, C.; Maccarini, G.; Carminati, M. A comparison between finite element model (FEM) simulation and an integrated artificial neural network (ANN)-particle swarm optimization (PSO) approach to forecast performances of micro electro discharge machining (micro-EDM) drilling. Micromachines 2021, 12, 667.
- Chen, Y.; Lao, Z.; Wang, R.; Li, J.; Gai, J.; You, H. Prediction of Both E-Jet Printing Ejection Cycle Time and Droplet Diameter Based on Random Forest Regression. Micromachines 2023, 14, 623.
- Mader, M.; Rein, C.; Konrat, E.; Meermeyer, S.L.; Lee-Thedieck, C.; Kotz-Helmer, F.; Rapp, B.E. Fused deposition modeling of microfluidic chips in transparent polystyrene. Micromachines 2021, 12, 1348.
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- Available online: https://www.mdpi.com/journal/micromachines/special_issues/LS77E2HJ2C (accessed on 1 May 2024).
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Masato, D. Editorial for the Special Issue on Advances in Micro and Nano Manufacturing: Process Modeling and Applications, Volume II. Micromachines 2024, 15, 687. https://doi.org/10.3390/mi15060687
Masato D. Editorial for the Special Issue on Advances in Micro and Nano Manufacturing: Process Modeling and Applications, Volume II. Micromachines. 2024; 15(6):687. https://doi.org/10.3390/mi15060687
Chicago/Turabian StyleMasato, Davide. 2024. "Editorial for the Special Issue on Advances in Micro and Nano Manufacturing: Process Modeling and Applications, Volume II" Micromachines 15, no. 6: 687. https://doi.org/10.3390/mi15060687