applsci-logo

Journal Browser

Journal Browser

Artificial Intelligence Technologies and Applications for Industry 4.0 and Smart Manufacturing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 2698

Special Issue Editors


E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: intelligent manufacturing; robotics and automation; application of artificial intelligence; structural optimization; mechanism innovation; solid mechanics

Special Issue Information

Dear Colleagues,

We are thrilled to announce the call for papers for our upcoming Special Issue, which will focus on Artificial Intelligence Technologies and Applications for Industry 4.0 and Smart Manufacturing.

In today's rapidly evolving technological landscape, Industry 4.0 and smart manufacturing stand at the forefront of innovation, reshaping traditional industrial processes and revolutionizing how goods are produced and serviced. Artificial Intelligence (AI) technologies are central to this transformation and hold immense potential for optimizing production processes, enhancing efficiency, and driving unprecedented levels of automation.

This Special Issue aims to explore the intersection of AI technologies and smart manufacturing, showcasing groundbreaking research, innovative methodologies, and practical applications poised to redefine the industrial landscape. From predictive maintenance and quality control to virtual metrology and autonomous robotics, we invite contributions highlighting the diverse array of AI-driven solutions shaping the future of manufacturing.

Topics of interest include, but are not limited to, the following:

  • AI-driven predictive maintenance and condition monitoring;
  • Autonomous robotic systems for manufacturing and logistics;
  • Intelligent process optimization and control;
  • Data-driven quality assurance and defect detection;
  • Adaptive scheduling and resource allocation;
  • Smart sensors and IoT integration for real-time monitoring and decision-making;
  • AI-enabled supply chain management and logistics optimization;
  • Human–robot collaboration and augmented reality interfaces;
  • Ethical considerations and societal implications of AI in manufacturing.

We welcome original research articles, review papers, case studies, and perspectives that offer valuable insights into the application of AI technologies in Industry 4.0 and smart manufacturing.

Warm regards,

Prof. Dr. Chih-Hung Li
Prof. Dr. Antonella Petrillo
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. 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

  • AI-driven manufacturing
  • Industry 4.0 innovation
  • smart factory solutions
  • automation and AI integration
  • intelligent process optimization

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

19 pages, 3556 KiB  
Article
Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production Efficiency
by Jose Isidro Hernández-Vega, Luis Alejandro Reynoso-Guajardo, Mario Carlos Gallardo-Morales, María Ernestina Macias-Arias, Amadeo Hernández, Nain de la Cruz, Jesús E. Soto-Soto and Carlos Hernández-Santos
Appl. Sci. 2024, 14(22), 10279; https://doi.org/10.3390/app142210279 - 8 Nov 2024
Viewed by 633
Abstract
This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at [...] Read more.
This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at the beginning of a shift change. These data were subjected to exploratory analysis to identify correlations between important variables, such as injection time, cycle time, and mold pressures. Additionally, classification models, including Random Forest and Logistic Regression, were constructed to predict and classify the process state based on these variables. The model results demonstrated high predictive performance, with 99.5% accuracy for Random Forest and 97% for Logistic Regression. These results provide a strong foundation for the early identification of potential problems and informed decision making to improve the efficiency of the plastic injection molding process. This study contributes to the advancement of the integration of intelligent technologies in industrial process optimization, aligned with the principles of Industry 4.0. Full article
Show Figures

Figure 1

34 pages, 5941 KiB  
Article
Digital and Sustainable Transition in Textile Industry through Internet of Things Technologies: A Pakistani Case Study
by Antonella Petrillo, Mizna Rehman and Illaria Baffo
Appl. Sci. 2024, 14(13), 5380; https://doi.org/10.3390/app14135380 - 21 Jun 2024
Cited by 1 | Viewed by 1772
Abstract
The textile industry, a vital contributor to Pakistan’s economy, faces pressing challenges in transitioning towards sustainability amid global environmental concerns. This manuscript presents a comprehensive case study on the implementation of IoT-driven strategies in the Pakistani textile sector to achieve digital and sustainable [...] Read more.
The textile industry, a vital contributor to Pakistan’s economy, faces pressing challenges in transitioning towards sustainability amid global environmental concerns. This manuscript presents a comprehensive case study on the implementation of IoT-driven strategies in the Pakistani textile sector to achieve digital and sustainable transformation. The findings reveal that the implementation of IoT technologies facilitated real-time environmental monitoring, enabling compliance with regulatory standards, and fostering sustainable manufacturing practices. Ultimately, this manuscript offers valuable insights into the transformative potential of IoT technologies in driving sustainable practices in the textile industry. The case study serves as a benchmark for other textile-producing regions aiming to embark on a digital and sustainable journey. These findings hold significant implications for the ongoing dialogue on sustainable industrial development, providing valuable direction for policymakers and stakeholders in shaping a more resilient and ecologically conscious future. Future research should prioritize addressing issues like data confidentiality and interoperability while adhering to standard requirements. Additionally, exploring analytics and machine learning methods for predictive maintenance, optimized performance, and operational improvement is crucial. Full article
Show Figures

Figure 1

Back to TopTop