Machine Learning in Micro Fabrication
A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".
Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 2622
Special Issue Editors
Interests: additive manufacturing; 4D printing; polymer; design for additive manufacturing; topology optimization; generative design; lattice structure
Special Issues, Collections and Topics in MDPI journals
Interests: additive manufacturing; bioinspired design; programmable material; biomanufacturing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid advancement of advanced manufacturing (AM) technologies, it is possible to rapidly fabricate complex physical objects in various scales. To monitor and control the manufacturing processes, there are different internal and external sensors producing numerous data in regard to the conditions of the machines. In recent decades, machine learning (ML) has been proved a suitable tool for analyzing large and complex datasets. Therefore, it is unsurprising that ML methods have been introduced for process planning and control. Smart manufacturing, i.e., Industry 4.0, refers to the manufacturing paradigm that makes use of sensors, cloud computing, machine learning, additive manufacturing, and/or advanced robotics to improve manufacturing productivity and cost efficiency. ML serves an important and necessary role in AM systems. Fundamental studies in ML will lead us to create more innovations in smart manufacturing and expand the manufacturing sectors. The objective of this Special Issue is to collect cutting-edge research works focused on the development of ML-based methods for microfabrication. Specific topics of interest include but are not limited to the following:
- ML-based product design and development;
- Data-driven process planning and control;
- Customization and personalization;
- Real-time monitoring and decision making;
- Manufacturing intelligence and informatics;
- ML-based topology optimization;
- Geometric deep learning methods for design and fabrication in AM.
Dr. Tsz Ho Kwok
Dr. Xiangjia Li
Dr. Jida Huang
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.
Keywords
- machine learning
- Industry 4.0
- smart manufacturing
- advanced manufacturing
- computer-intergrated manufacturing
