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Micromachines
Special Issues
Applications of Artificial Intelligence in Micromachining
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Applications of Artificial Intelligence in Micromachining

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

Deadline for manuscript submissions: closed (10 October 2021) | Viewed by 3146

Special Issue Editor

Dr. Binayak Bhandari

E-Mail Website
Guest Editor
ARC Training Centre for Automated Manufacture of Advanced Composites, School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Interests: composite materials; micromachining; nanoparticle deposition; design and analysis of smart structures; renewable energy; AI

Special Issue Information

Dear Colleagues,

Micromachining is a technique for the fabrication of miniaturized 3D structures on a micrometer scale. Micromachining is also a powerful tool for precise control of precision machines and tools to generate mechanical, optical, and semiconductor parts with the required surface finish and properties.

The use of sensors in micromachining machines can collect machining data which can be analyzed using artificial intelligence (AI) to improve the machining process. This real-time data analysis can significantly improve productivity and machining quality. The use of machine learning (ML) and deep learning (DL) to select the optimal process parameters is of great interest, and enables fast, efficient production and lower manufacturing costs.

In this Special Issue, we seek papers concerned with all precision technologies assisted by artificial intelligence with a clear contribution to the advancement of micromachining. The research work can include, but is not limited to, any branch of micromachining, such as laser systems, microelectromechanical systems (MEMS), photolithography, micro-EDM, and micromechanical machining. Original research papers and review articles are both welcome.

Dr. Binayak Bhandari
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. 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.

Keywords

  • micromachining
  • microstructures
  • deep learning
  • machine learning
  • artificial intelligence
  • ultraprecision

Published Papers (1 paper)

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Research

18 pages, 16281 KiB  
Article
Comparative Study of Popular Deep Learning Models for Machining Roughness Classification Using Sound and Force Signals
by Binayak Bhandari
Micromachines 2021, 12(12), 1484; https://doi.org/10.3390/mi12121484 - 29 Nov 2021
Cited by 8 | Viewed by 2247
Abstract
This study compared popular Deep Learning (DL) architectures to classify machining surface roughness using sound and force data. The DL architectures considered in this study include Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), and transformer. The classification was performed [...] Read more.
This study compared popular Deep Learning (DL) architectures to classify machining surface roughness using sound and force data. The DL architectures considered in this study include Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), and transformer. The classification was performed on the sound and force data generated during machining aluminum sheets for different levels of spindle speed, feed rate, depth of cut, and end-mill diameter, and it was trained on 30 s machining data (10–40 s) of the machining experiments. Since a raw audio waveform is seldom used in DL models, Mel-Spectrogram and Mel Frequency Cepstral Coefficients (MFCCs) audio feature extraction techniques were used in the DL models. The results of DL models were compared for the training–validation accuracy, training epochs, and training parameters of each model. Although the roughness classification by all the DL models was satisfactory (except for CNN with Mel-Spectrogram), the transformer-based modes had the highest training (>96%) and validation accuracies (≈90%). The CNN model with Mel-Spectrogram exhibited the worst training and inference accuracy, which is influenced by limited training data. Confusion matrices were plotted to observe the classification accuracy visually. The confusion matrices showed that the transformer model trained on Mel-Spectrogram and the transformer model trained on MFCCs correctly predicted 366 (or 91.5%) and 371 (or 92.7%) out of 400 test samples. This study also highlights the suitability and superiority of the transformer model for time series sound and force data and over other DL models. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Micromachining)
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