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Micromachines
Special Issues
Intelligent Micro-Manufacturing and Applications
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Intelligent Micro-Manufacturing and Applications

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4019

Special Issue Editors

Prof. Dr. Panagiotis Kyratsis

E-Mail Website
Guest Editor
Department of Product and Systems Design Engineering, University of Western Macedonia, 50100 Kila Kozani, Greece
Interests: computational design; CAD/CAM/CAE; digital manufacturing; product design; FEA; industry 4.0; prototyping; reverse engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Konstantinos Salonitis

E-Mail Website
Guest Editor
Sustainable Manufacturing Systems Centre, School of Aerospace Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK
Interests: sustainable manufacturing systems; green manufacturing; environmental impact assessment; simulation; modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue entitled “Intelligent Micro-Manufacturing and Applications” to be published by the MDPI journal Micromachines. The aim of this Special Issue is to provide a collection of high-quality research related to applying intelligent tools in all aspects of manufacturing. All papers will be fully open access upon publication following peer review.

Potential topics include but are not limited to the following:

  • Machine learning (ML) in micro-manufacturing;
  • Artificial intelligence (AI) applications in micro-manufacturing;
  • Automation in design and micro-manufacturing;
  • Digital micro-manufacturing and cloud-based micro-manufacturing;
  • Intelligent machining microsystems and processes;
  • Smart micro-manufacturing processes and smart factories;
  • Additive manufacturing;
  • Smart technology and smart materials.

Prof. Dr. Panagiotis Kyratsis
Prof. Dr. Konstantinos Salonitis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

15 pages, 2244 KiB  
Article
Accelerated Computational Fluid Dynamics Simulations of Microfluidic Devices by Exploiting Higher Levels of Abstraction
by Michel Takken and Robert Wille
Micromachines 2024, 15(1), 129; https://doi.org/10.3390/mi15010129 - 12 Jan 2024
Viewed by 904
Abstract
The design of microfluidic devices is a cumbersome and tedious process that can be significantly improved by simulation. Methods based on Computational Fluid Dynamics (CFD) are considered state-of-the-art, but require extensive compute time—oftentimes limiting the size of microfluidic devices that can be simulated. [...] Read more.
The design of microfluidic devices is a cumbersome and tedious process that can be significantly improved by simulation. Methods based on Computational Fluid Dynamics (CFD) are considered state-of-the-art, but require extensive compute time—oftentimes limiting the size of microfluidic devices that can be simulated. Simulation methods that abstract the underlying physics on a higher level generally provide results instantly, but the fidelity of these methods is usually worse. In this work, a simulation method that accelerates CFD simulations by exploiting simulation methods on higher levels of abstraction is proposed. Case studies confirm that the proposed method accelerates CFD simulations by multiple factors (often several orders of magnitude) while maintaining the fidelity of CFD simulations. Full article
(This article belongs to the Special Issue Intelligent Micro-Manufacturing and Applications)
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19 pages, 5027 KiB  
Article
Material Extrusion Filament Width and Height Prediction via Design of Experiment and Machine Learning
by Xiaoquan Shi, Yazhou Sun, Haiying Tian, Puthanveettil Madathil Abhilash, Xichun Luo and Haitao Liu
Micromachines 2023, 14(11), 2091; https://doi.org/10.3390/mi14112091 - 12 Nov 2023
Cited by 1 | Viewed by 1024
Abstract
The dimensions of material extrusion 3D printing filaments play a pivotal role in determining processing resolution and efficiency and are influenced by processing parameters. This study focuses on four key process parameters, namely, nozzle diameter, nondimensional nozzle height, extrusion pressure, and printing speed. [...] Read more.
The dimensions of material extrusion 3D printing filaments play a pivotal role in determining processing resolution and efficiency and are influenced by processing parameters. This study focuses on four key process parameters, namely, nozzle diameter, nondimensional nozzle height, extrusion pressure, and printing speed. The design of experiment was carried out to determine the impact of various factors and interaction effects on filament width and height through variance analysis. Five machine learning models (support vector regression, backpropagation neural network, decision tree, random forest, and K-nearest neighbor) were built to predict the geometric dimension of filaments. The models exhibited good predictive performance. The coefficients of determination of the backpropagation neural network model for predicting line width and line height were 0.9025 and 0.9604, respectively. The effect of various process parameters on the geometric morphology based on the established prediction model was also studied. The order of influence on line width and height, ranked from highest to lowest, was as follows: nozzle diameter, printing speed, extrusion pressure, and nondimensional nozzle height. Different nondimensional nozzle height settings may cause the extruded material to be stretched or squeezed. The material being in a stretched state leads to a thin filament, and the regularity of processing parameters on the geometric size is not strong. Meanwhile, the nozzle diameter exhibits a significant impact on dimensions when the material is in a squeezing state. Thus, this study can be used to predict the size of printing filament structures, guide the selection of printing parameters, and determine the size of 3D printing layers. Full article
(This article belongs to the Special Issue Intelligent Micro-Manufacturing and Applications)
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23 pages, 8066 KiB  
Article
Research on Robot Grasping Based on Deep Learning for Real-Life Scenarios
by Jie Hu, Qin Li and Qiang Bai
Micromachines 2023, 14(7), 1392; https://doi.org/10.3390/mi14071392 - 08 Jul 2023
Cited by 1 | Viewed by 1425
Abstract
The application of robots, especially robotic arms, has been primarily focused on the industrial sector due to their relatively low level of intelligence. However, the rapid development of deep learning has provided a powerful tool for conducting research on highly intelligent robots, thereby [...] Read more.
The application of robots, especially robotic arms, has been primarily focused on the industrial sector due to their relatively low level of intelligence. However, the rapid development of deep learning has provided a powerful tool for conducting research on highly intelligent robots, thereby offering tremendous potential for the application of robotic arms in daily life scenarios. This paper investigates multi-object grasping in real-life scenarios. We first analyzed and improved the structural advantages and disadvantages of convolutional neural networks and residual networks from a theoretical perspective. We then constructed a hybrid grasping strategy prediction model, combining both networks for predicting multi-object grasping strategies. Finally, we deployed the trained model in the robot control system to validate its performance. The results demonstrate that both the model prediction accuracy and the success rate of robot grasping achieved by this study are leading in terms of performance. Full article
(This article belongs to the Special Issue Intelligent Micro-Manufacturing and Applications)
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