Special Issue "Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities"
Deadline for manuscript submissions: 31 October 2021.
Interests: smart manufacturing; digital transformation; sustainability; circular economy; automation systems
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Interests: digital manufacturing; multi-criteria decision making; safety and human factors; smart manufacturing
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In recent years, the focus on smart manufacturing systems has been pushing companies toward a new variety of highly specific technical solutions. These solutions are characterized by an integrated approach to manufacturing termed “digital manufacturing”. In fact, digital manufacturing systems often incorporate optimization capabilities to reduce time and cost and improve the efficiency of most processes. The digital revolution is now our “present” and not the future. There are many different tooling processes that digital manufacturing utilizes, such as artificial intelligence, automation and robotics, additive technology, and human–machine interaction. These tools are unleashing innovations that will change the nature of manufacturing itself.
Industry and academic leaders agree that digital-manufacturing technologies will transform every link in the manufacturing value chain, from research and development, supply chain, and factory operations to marketing, sales, and service.
Furthermore, recently, some studies have identified several interlinks between smart manufacturing and sustainability. Emerging academic research is concerned with how the principles, practices, and enabling technologies of industry 4.0 might unlock the potentials of circular economy (CE) and sustainable manufacturing. Digitalization and the use of big data are seen as key enablers for increased sustainability and for the implementation of a circular economy.
Promoting research for innovation, sustainable solutions, and sustainable lifestyles in a new digitalized society and business sector, as well as facilitating them by financial measures and social measures, are the key tasks of this Special Issue.
This Special Issue is will collect a high-quality selection of contemporary research articles on the topic of “Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities”.
We are particularly interested in publishing articles not only from a traditional point of view but also from new emerging trends in order to meet practitioners’ needs and make theoretical contributions. This call is also aimed at collecting contributions that explore policies and practices adopted in different countries/regions in the field of smart manufacturing, as well as on the results obtained.Prof. Dr. Antonella Petrillo
Prof. Fabio De Felice
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 papers will be 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 2000 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.
- Smart manufacturing: Economic and technology development
- Sustainable manufacturing and industrial policy
- Circular economy and blue economy (best practices for the transition toward a CE, CE policies impact assessments)
- Process digitalization, rethink robotics, cobot, and advanced manufacturing solution
- Enabling technology (IoT, simulation and digital twin, additive manufacturing, big data, cyber-physical systems, augmented reality, horizontal and vertical system integration, autonomous robot, virtual reality, machine learning, etc.)
- 5G and smart manufacturing
- Innovation and new business model
- Product life-cycle management to support industry 4.0
- Life cycle assessment and environmental impacts of industry 4.0
- Climate change
- Design for environment
- Innovative software
- Decision analysis
- Decision support systems applications
- Optimization and management in manufacturing
- Strategies for emerging technologies and strategic sectors
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Sentiment classification of e-commerce product quality reviews by deep learning algorithm of Bert-BiGRU with AP-LDA
Authors: Yi Liu, Feng Mao*, Jiahuan Lu
Affiliation: Management School, Hangzhou Dianzi University, Hangzhou, China
Abstract: In order to enhance the accuracy of sentiment classification for e-commerce product quality reviews, this paper propose the deep learning algorithm of Bert-BiGRU with AP-LDA which uses the AP-LDA model to extract text features of e-commerce product quality reviews, and then uses the Bert-BiGRU with full connection layer to classify the sentiment tendency. Compared the RNN, GRU,LSTM and other algorithms, the experiments of different data sets show that the Bert-BiGRU algorithm with AP-LDA can analyze and achieve the better sentiment classification accuracy, which has increased by 3% ~ 7% on the effectiveness of the e-commerce product quality reviews. Keywords: E-commerce Product Quality Review; Sentiment Classifier; Deep Learning ; Latent Dirichlet Allocation(LDA) ; Bert-Bidirectional GRU