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AI
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Intelligent Systems for Industry 4.0
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Intelligent Systems for Industry 4.0

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI in Autonomous Systems".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 3361

Special Issue Editors

Prof. Dr. Sharifu Ura

E-Mail Website
Guest Editor
Division of Mechanical and Electrical Engineering, Kitami Institute of Technology, Kitami 090-8507, Japan
Interests: Industry 4.0; 3D Printing; sustainable product development; engineering education
Special Issues, Collections and Topics in MDPI journals
Dr. Angkush Kumar Ghosh

E-Mail Website
Guest Editor
Division of Mechanical and Electrical Engineering, Kitami Institute of Technology, Kitami 090-8507, Japan
Interests: Industry 4.0; digital twins; smart systems; signal processing; semantic annotation; data analytics

Special Issue Information

Dear Colleagues,

The advent of information and communication technology has been shaping new realities in many fields, including manufacturing. Consequently, a concept of manufacturing called the fourth industrial revolution (known as Industry 4.0, Smart Manufacturing, and Connected Factory) has emerged. Industry 4.0 does not mean achieving mere automation among product realization enablers (e.g., machine tools, robots, assembly lines, CAD/CAM/CAE systems, ERP systems, and SCM systems), as was the case for its predecessor. It, instead, embarks on achieving autonomy and harmony among the enablers. Thus, the enablers must be capable of performing high-level intellectual tasks, such as understanding (i.e., why is it happening), prediction (what will happen), and adaptation (what decisions should be taken and implemented to choose the right course of action). Therefore, artificially intelligent systems must empower the enablers. The intelligent systems create a vast ecosystem that integrates human learning, machine learning, logical inferences (deduction, induction, and abduction), experimental data, sensor signals, analytical results, simulations, creative thinking, cognitive reflections, and big data analytics. Policymakers, practitioners, and researchers around the globe have been acting in a coordinated manner yet remaining independent to achieve the goals of Industry 4.0 with the aid of various intelligent systems. This Special Issue showcases some of the relevant studies. Thus, the Special Issue solicits original articles, reviews, and perspectives on the following topics (but not limited to):

  • Artificial Narrow Intelligence for Industry 4.0;
  • Artificial Super Intelligent for Industry 4.0;
  • Semantically Annotated Linked Data for Industry 4.0;
  • Intelligent Systems for Digital Twins;
  • Intelligent Systems for Learning Factory;
  • Intelligent System for Cyber-Physical Systems;
  • Intelligent Systems for Big Data Analytics;
  • Intelligent Systems for Sensor Signal Processing;
  • Knowledge Engineering for Industry 4.0;
  • Intelligent Systems for Digital Manufacturing Commons;
  • Intelligent Systems for Implementing Industry 4.0 in SMEs;
  • Intelligent Systems for Mitigating Big Data Inequalities.

Prof. Dr. Sharifu Ura
Dr. Angkush Kumar Ghosh
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. AI is an international peer-reviewed open access quarterly 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 1600 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

  • artificial narrow intelligence
  • artificial super intelligent
  • semantic annotation
  • linked data
  • big data
  • data analytics
  • digital twins
  • cyber-physical systems
  • sensor signals
  • digital manufacturing commons
  • knowledge-based systems
  • SMEs
  • big data inequalities
  • learning factory

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Published Papers (2 papers)

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20 pages, 14487 KiB  
Article
Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques
by Satish Kumar, Sameer Sayyad and Arunkumar Bongale
AI 2024, 5(4), 1759-1778; https://doi.org/10.3390/ai5040087 - 27 Sep 2024
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Abstract
Fused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects since it is affordable, effective, [...] Read more.
Fused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects since it is affordable, effective, and easy to use. Some defects such as poor infill, elephant foot, layer shift, and poor surface finish arise in the FDM components at the printing stage due to variations in printing parameters such as printing speed, change in nozzle, or bed temperature. Proper fault classification is required to identify the cause of faulty products. In this work, the multi-sensory data are gathered using different sensors such as vibration, current, temperature, and sound sensors. The data acquisition is performed by using the National Instrumentation (NI) Data Acquisition System (DAQ) which provides the synchronous multi-sensory data for the model training. To induce the faults, the data are captured under different conditions such as variations in printing speed, temperate, and jerk during the printing. The collected data are used to train the machine learning (ML) and deep learning (DL) classification models to classify the variation in printing parameters. The ML models such as k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), and random forest (RF) with convolutional neural network (CNN) as a DL model are used to classify the variable operation printing parameters. Out of the available models, in ML models, the RF classifier shows a classification accuracy of around 91% whereas, in the DL model, the CNN model shows good classification performance with accuracy ranging from 92 to 94% under variable operating conditions. Full article
(This article belongs to the Special Issue Intelligent Systems for Industry 4.0)
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12 pages, 999 KiB  
Perspective
Collaborative Robots with Cognitive Capabilities for Industry 4.0 and Beyond
by Giulio Sandini, Alessandra Sciutti and Pietro Morasso
AI 2024, 5(4), 1858-1869; https://doi.org/10.3390/ai5040092 - 9 Oct 2024
Viewed by 818
Abstract
The robots that entered the manufacturing sector in the second and third Industrial Revolutions (IR2 and IR3) were designed for carrying out predefined routines without physical interaction with humans. In contrast, IR4* robots (i.e., robots since IR4 and beyond) are supposed to interact [...] Read more.
The robots that entered the manufacturing sector in the second and third Industrial Revolutions (IR2 and IR3) were designed for carrying out predefined routines without physical interaction with humans. In contrast, IR4* robots (i.e., robots since IR4 and beyond) are supposed to interact with humans in a cooperative way for enhancing flexibility, autonomy, and adaptability, thus dramatically improving productivity. However, human–robot cooperation implies cognitive capabilities that the cooperative robots (CoBots) in the market do not have. The common wisdom is that such a cognitive lack can be filled in a straightforward way by integrating well-established ICT technologies with new AI technologies. This short paper expresses the view that this approach is not promising and suggests a different one based on artificial cognition rather than artificial intelligence, founded on concepts of embodied cognition, developmental robotics, and social robotics. We suggest giving these IR4* robots designed according to such principles the name CoCoBots. The paper also addresses the ethical problems that can be raised in cases of critical emergencies. In normal operating conditions, CoCoBots and human partners, starting from individual evaluations, will routinely develop joint decisions on the course of action to be taken through mutual understanding and explanation. In case a joint decision cannot be reached and/or in the limited case that an emergency is detected and declared by top security levels, we suggest that the ultimate decision-making power, with the associated responsibility, should rest on the human side, at the different levels of the organized structure. Full article
(This article belongs to the Special Issue Intelligent Systems for Industry 4.0)
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