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Applied Sciences
Special Issues
Application of Data Science and Artificial Intelligence in Smart Manufacturing
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Application of Data Science and Artificial Intelligence in Smart Manufacturing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 1921

Special Issue Editor

Dr. Lazhar Homri

E-Mail Website
Guest Editor
Arts et Métiers Institute of Technology, Université de Lorraine, LCFC, HESAM Université, 57070 Metz, France
Interests: artificial intelligence; industrial engineering; product development; quality management

Special Issue Information

Dear Colleagues,

Industry 4.0 promises comprehensive support for production and product development using data science and machine learning (ML) algorithms, driven by the digitization of products and services as one of its fundamental aspects. Increased processing power and data collection would allow for the addition of intelligence into the manufacturing processes and data-based decision-making approaches, enabling the real-time prediction of process performance, optimization and redefinition of products ensuring customers’ needs, and reduction in costs and energy waste, all leading towards smart manufacturing (SM).

I invite colleagues to contribute research dealing with recent developments and concerning the “application of data science approaches and ML techniques in smart manufacturing”.

Dr. Lazhar Homri
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. Applied Sciences 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 2400 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

  • Industry 4.0
  • smart manufacturing
  • data
  • machine learning

Published Papers (1 paper)

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Research

14 pages, 1910 KiB  
Article
A Momentum Contrastive Learning Framework for Low-Data Wafer Defect Classification in Semiconductor Manufacturing
by Yi Wang, Dong Ni and Zhenyu Huang
Appl. Sci. 2023, 13(10), 5894; https://doi.org/10.3390/app13105894 - 10 May 2023
Cited by 2 | Viewed by 1484
Abstract
Wafer bin maps (WBMs) are essential test data in semiconductor manufacturing. WBM defect classification can provide critical information for the improvement of manufacturing processes and yield. Although deep-learning-based automatic defect classification models have demonstrated promising results in recent years, they require a substantial [...] Read more.
Wafer bin maps (WBMs) are essential test data in semiconductor manufacturing. WBM defect classification can provide critical information for the improvement of manufacturing processes and yield. Although deep-learning-based automatic defect classification models have demonstrated promising results in recent years, they require a substantial amount of labeled data for training, and manual labeling is time-consuming. Such limitations impede the practical application of existing algorithms. This study introduces a low-data defect classification algorithm based on contrastive learning. By employing momentum contrastive learning, the network extracts effective representations from large-scale unlabeled WBMs. Subsequently, a prototypical network is utilized for fine-tuning with only a minimal amount of labeled data to achieve low-data classification. Experimental results reveal that the momentum contrastive learning method improves the defect classification performance by learning feature representation from large-scale unlabeled data. The proposed method attains satisfactory classification accuracy using a limited amount of labeled data and surpasses other comparative methods in performance. This approach allows for the exploitation of information derived from large-scale unlabeled data, significantly reducing the reliance on labeled data. Full article
(This article belongs to the Special Issue Application of Data Science and Artificial Intelligence in Smart Manufacturing)
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