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JMMP
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Advances in Micro and Nanomanufacturing
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Advances in Micro and Nanomanufacturing

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 16406

Special Issue Editor

Prof. Dr. Panagiotis Stavropoulos

E-Mail Website
Guest Editor
Laboratory for Manufacturing Systems and Automation, University of Patras, 26504 Patras, Greece
Interests: advanced manufacturing processes; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is an increasing interest in the potential of micro- and nanomanufacturing in numerous scientific and technological sectors. At the same time, micro- and nanomanufacturing present great challenges to engineers and researchers. From a manufacturing point of view, such processing technologies are at a relative early stage of development. Key enabling technologies, sourced from the Industry 4.0 KETs era, offer a great potential for mastering micro- and nanomanufacturing-related processes. In this Special Issue of JMMP, current research findings will be reported, focusing on micro- and nanomanufacturing and/or manufacturing parts with micro/nano features. More specifically, retrieval and utilization of information related to process modeling and monitoring is of great interest, as well as experimental works that build up knowledge regarding the process itself. The considered papers should support the achievement of significant improvements with clear regard to aspects of manufacturing attributes.

We are interested in contributions that focus on topics such as:

  • Precision and ultra-precision machining/finishing at micro and nano level;
  • Additive and hybrid processes at micro and nano level;
  • Material modifications/part properties due to process-induced material loads;
  • Sustainable/energy efficient manufacturing processes;
  • Digital twin methodology;
  • High-precision simulation methods;
  • Cognitive control/cognitive automation taking into account micro/nano features.

Dr. Panagiotis Stavropoulos
Guest Editor

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. Journal of Manufacturing and Materials Processing is an international peer-reviewed open access semimonthly 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 1800 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.

Keywords

  • Micro Manufacturing
  • Nano Manufacturing
  • Micro assembly
  • Digital Twin
  • Process Simulation
  • Process Control
  • Process Optimization
  • Quality Assessment

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

16 pages, 17317 KiB  
Article
Selective Laser Sintering Induced Residual Stresses: Precision Measurement and Prediction
by Susan Impey, Prateek Saxena and Konstantinos Salonitis
J. Manuf. Mater. Process. 2021, 5(3), 101; https://doi.org/10.3390/jmmp5030101 - 18 Sep 2021
Cited by 8 | Viewed by 2981
Abstract
Additive Manufacturing presents unique advantages over traditional manufacturing processes and has the potential to accelerate technical advancement across multiple sectors, permitting far greater freedom in design than conventional manufacturing. However, one barrier which blocks wide adoption is residual stresses, which could seriously affect [...] Read more.
Additive Manufacturing presents unique advantages over traditional manufacturing processes and has the potential to accelerate technical advancement across multiple sectors, permitting far greater freedom in design than conventional manufacturing. However, one barrier which blocks wide adoption is residual stresses, which could seriously affect the materials’ behaviour during and after production. Selective laser sintering (SLS), a process with high energy input to the workpiece material, induces high temperature gradients, further affecting the final residual stress distribution. Within the present paper, three different methods for the assessment of the residual stresses’ distribution are presented and compared: a non-destructive method based on neutron diffraction, a destructive method known as the contour method, and a theoretical approach based on Finite Element Analysis. The aim is to examine the suitability and reliability of the application of these methods in predicting residual stresses distribution in additive manufacturing-built parts. Full article
(This article belongs to the Special Issue Advances in Micro and Nanomanufacturing)
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12 pages, 2255 KiB  
Article
The Impact of Process Parameters on Surface Roughness and Dimensional Accuracy during CO2 Laser Cutting of PMMA Thin Sheets
by Konstantinos Ninikas, John Kechagias and Konstantinos Salonitis
J. Manuf. Mater. Process. 2021, 5(3), 74; https://doi.org/10.3390/jmmp5030074 - 7 Jul 2021
Cited by 21 | Viewed by 4774
Abstract
This study investigated the impact of the laser speed and power, and the position and orientation of the samples, on the average surface roughness (Ra) and dimensional accuracy (DA) during CO2 laser cutting of polymethyl methacrylate (PMMA) thin sheets. A mixed five-parameter [...] Read more.
This study investigated the impact of the laser speed and power, and the position and orientation of the samples, on the average surface roughness (Ra) and dimensional accuracy (DA) during CO2 laser cutting of polymethyl methacrylate (PMMA) thin sheets. A mixed five-parameter fractional factorial design was applied, and thirty-six measurements for the Ra and DA were obtained. The experimental results were analysed using ANOM diagrams, ANOVA analysis and interaction plots of all parameters. It was concluded that the laser speed is the critical parameter for both surface roughness and dimensional accuracy, resulting in strong interactions with laser power and positioning parameters. It was also shown that Ra values are affected by the orientation of the specimen and can be minimized when the samples are aligned in the laser travel direction. Finally, it was proved that lower laser speed improves the average roughness but reduces the dimensional accuracy. Full article
(This article belongs to the Special Issue Advances in Micro and Nanomanufacturing)
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21 pages, 68171 KiB  
Article
Machine Learning Driven Prediction of Residual Stresses for the Shot Peening Process Using a Finite Element Based Grey-Box Model Approach
by Benjamin James Ralph, Karin Hartl, Marcel Sorger, Andreas Schwarz-Gsaxner and Martin Stockinger
J. Manuf. Mater. Process. 2021, 5(2), 39; https://doi.org/10.3390/jmmp5020039 - 21 Apr 2021
Cited by 13 | Viewed by 4369
Abstract
The shot peening process is a common procedure to enhance fatigue strength on load-bearing components in the metal processing environment. The determination of optimal process parameters is often carried out by costly practical experiments. An efficient method to predict the resulting residual stress [...] Read more.
The shot peening process is a common procedure to enhance fatigue strength on load-bearing components in the metal processing environment. The determination of optimal process parameters is often carried out by costly practical experiments. An efficient method to predict the resulting residual stress profile using different parameters is finite element analysis. However, it is not possible to include all influencing factors of the materials’ physical behavior and the process conditions in a reasonable simulation. Therefore, data-driven models in combination with experimental data tend to generate a significant advantage for the accuracy of the resulting process model. For this reason, this paper describes the development of a grey-box model, using a two-dimensional geometry finite element modeling approach. Based on this model, a Python framework was developed, which is capable of predicting residual stresses for common shot peening scenarios. This white-box-based model serves as an initial state for the machine learning technique introduced in this work. The resulting algorithm is able to add input data from practical residual stress experiments by adapting the initial model, resulting in a steady increase of accuracy. To demonstrate the practical usage, a corresponding Graphical User Interface capable of recommending shot peening parameters based on user-required residual stresses was developed. Full article
(This article belongs to the Special Issue Advances in Micro and Nanomanufacturing)
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13 pages, 1793 KiB  
Article
Optimization of Abrasive Flow Nano-Finishing Processes by Adopting Artificial Viral Intelligence
by Nikolaos A. Fountas and Nikolaos M. Vaxevanidis
J. Manuf. Mater. Process. 2021, 5(1), 22; https://doi.org/10.3390/jmmp5010022 - 8 Mar 2021
Cited by 12 | Viewed by 3115
Abstract
This work deals with the optimization of crucial process parameters related to the abrasive flow machining applications at micro/nano-levels. The optimal combination of abrasive flow machining parameters for nano-finishing has been determined by applying a modified virus-evolutionary genetic algorithm. This algorithm implements two [...] Read more.
This work deals with the optimization of crucial process parameters related to the abrasive flow machining applications at micro/nano-levels. The optimal combination of abrasive flow machining parameters for nano-finishing has been determined by applying a modified virus-evolutionary genetic algorithm. This algorithm implements two populations: One comprising the hosts and one comprising the viruses. Viruses act as information carriers and thus they contribute to the algorithm by boosting efficient schemata in binary coding to facilitate both the arrival at global optimal solutions and rapid convergence speed. Three cases related to abrasive flow machining have been selected from the literature to implement the algorithm, and the results corresponding to them have been compared to those available by the selected contributions. It has been verified that the results obtained by the virus-evolutionary genetic algorithm are not only practically viable, but far more promising compared to others as well. The three cases selected are the traditional “abrasive flow finishing,” the “rotating workpiece” abrasive flow finishing, and the “rotational-magnetorheological” abrasive flow finishing. Full article
(This article belongs to the Special Issue Advances in Micro and Nanomanufacturing)
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