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Advanced Measurements for Industry 4.0
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Advanced Measurements for Industry 4.0

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 44794

Special Issue Editors

Prof. Dr. Leopoldo Angrisani

E-Mail Website
Guest Editor
Department of Information Technology and Electrical Engineering, CeSMA—Center of Advanced Measurement and Technology Services, University of Napoli Federico II, Naples, Italy
Interests: communication systems and networks test and measurement; measurements for Internet of Things applications; compressive sampling based measurements; measurements for Industry 4.0; measurement uncertainty
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Domenico Accardo

E-Mail Website
Guest Editor
DII—Department of Industrial Engineering, University of Naples Federico II, Piazzale Vincenzo Tecchio, 80, Naples, Italy
Interests: unmanned aircraft system; unmanned underwater vehicles; autonomous vehicles; integrated navigation systems; mems inertial sensors; data fusion; sense and avoid; collision avoidance; air traffic management; unmanned traffic management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Fourth Industrial Revolution, i.e., Industry 4.0, has introduced new forms of industrial processes that benefit from innovative technologies, such as advanced robotics, machine learning, cloud computing, additive manufacturing, big data analytics, cybersecurity, and augmented reality. The main advantages are related to an overall reduction of costs and an increase in process reliability, robustness, maintainability, and re-use.

Industry 4.0 is a framework that requires high-automation of processes where the human support is more related to supervisor tasks than operator ones. Human supervisor can be in a remote position with respect to core processing activity to increase safety or to reduce costs by teleworking. Therefore, proper sensing solutions are required to provide adequate awareness about the status of each activity. Several cases can be considered, such as visual cameras for augmented reality, motion sensors for robotics, and chemical sensors for additive manufacturing. New systems need to be adopted for specific processes, such as drones for large infrastructure inspection, digital twins to assess risk and sources of failures, and avatars to guide maintenance operations by means of augmented reality solutions.

Sensors used for advanced industrial applications shall be smart-sensors, i.e., they need to have internal data processing capabilities to provide built-in functions such as digital noise filtering, bias and scale factor error compensation, and automatic detection of measurement anomalies. Sensors shall be networked to improve overall awareness by means of cross-sensor cueing and sensor data fusion. Sensors providing a very large quantity of output data, such as cameras and radars, can be exploited by applying big data analytics or machine learning solutions to provide synthetic information to support correct decision-making.

The Special Issue will deal with all innovative research solutions that fit the above-described framework. The following list reports some non-exhaustive examples:

  1. Innovative solutions for sensing the motion of industrial robots or human supervisors;
  2. Robust pose determination solutions for augmented reality;
  3. Original sensing solutions to improve additive manufacturing;
  4. Sensors and systems for industrial infrastructure inspection;
  5. Sensing solutions adopted to increase the safety of human operators;
  6. Integrated sensing systems that exploit broadband wireless communications with reduced latency, such as 5G mobile networks;
  7. Smart sensors for industrial applications;
  8. Networked sensor systems to support innovative industrial processes;
  9. Internet of Things for industry applications;
  10. Innovative navigation solutions for autonomous transport platforms used in industrial production processes, including ground platforms and aerial drones;
  11. Innovative sensor data fusion solutions to improve manufacturing processes;
  12. Novel big data analytics applications that improve the capability to handle sensor data output.

Prof. Dr. Leopoldo Angrisani
Prof. Dr. Domenico Accardo
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. Sensors 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 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

  • Industry 4.0
  • IoT for industry
  • industrial robots
  • additive manufacturing
  • augmented reality
  • big data analytics
  • motion sensing
  • sensor data fusion
  • networked sensors
  • smart sensors
  • industrial sensors

Published Papers (5 papers)

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Research

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16 pages, 2826 KiB  
Article
Probabilistic Evaluation of 3D Surfaces Using Statistical Shape Models (SSM)
by Javier Pérez, Jose-Luis Guardiola, Alberto J. Perez and Juan-Carlos Perez-Cortes
Sensors 2020, 20(22), 6554; https://doi.org/10.3390/s20226554 - 17 Nov 2020
Cited by 1 | Viewed by 2288
Abstract
Inspecting a 3D object which shape has elastic manufacturing tolerances in order to find defects is a challenging and time-consuming task. This task usually involves humans, either in the specification stage followed by some automatic measurements, or in other points along the process. [...] Read more.
Inspecting a 3D object which shape has elastic manufacturing tolerances in order to find defects is a challenging and time-consuming task. This task usually involves humans, either in the specification stage followed by some automatic measurements, or in other points along the process. Even when a detailed inspection is performed, the measurements are limited to a few dimensions instead of a complete examination of the object. In this work, a probabilistic method to evaluate 3D surfaces is presented. This algorithm relies on a training stage to learn the shape of the object building a statistical shape model. Making use of this model, any inspected object can be evaluated obtaining a probability that the whole object or any of its dimensions are compatible with the model, thus allowing to easily find defective objects. Results in simulated and real environments are presented and compared to two different alternatives. Full article
(This article belongs to the Special Issue Advanced Measurements for Industry 4.0)
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25 pages, 6676 KiB  
Article
Switchable Glass Enabled Contextualization for a Cyber-Physical Safe and Interactive Spatial Augmented Reality PCBA Manufacturing Inspection System
by Joel Murithi Runji and Chyi-Yeu Lin
Sensors 2020, 20(15), 4286; https://doi.org/10.3390/s20154286 - 31 Jul 2020
Cited by 5 | Viewed by 2653
Abstract
Augmented reality (AR) has been demonstrated to improve efficiency by up to thrice the level of traditional methods. Specifically, the adoption of visual AR is performed widely using handheld and head-mount technologies. Despite spatial augmented reality (SAR) addressing several shortcomings of wearable AR, [...] Read more.
Augmented reality (AR) has been demonstrated to improve efficiency by up to thrice the level of traditional methods. Specifically, the adoption of visual AR is performed widely using handheld and head-mount technologies. Despite spatial augmented reality (SAR) addressing several shortcomings of wearable AR, its potential is yet to be fully explored. To date, it enhances the cooperation of users with its wide field of view and supports hands-free mobile operation, yet it has remained a challenge to provide references without relying on restrictive static empty surfaces of the same object or nearby objects for projection. Towards this end, we propose a novel approach that contextualizes projected references in real-time and on demand, onto and through the surface across a wireless network. To demonstrate the effectiveness of the approach, we apply the method to the safe inspection of printed circuit board assembly (PCBA) wirelessly networked to a remote automatic optical inspection (AOI) system. A defect detected and localized by the AOI system is wirelessly remitted to the proposed remote inspection system for prompt guidance to the inspector by augmenting a rectangular bracket and a reference image. The rectangular bracket transmitted through the switchable glass aids defect localization over the PCBA, whereas the image is projected over the opaque cells of the switchable glass to provide reference to a user. The developed system is evaluated in a user study for its robustness, precision and performance. Results indicate that the resulting contextualization from variability in occlusion levels not only positively affect inspection performance but also supersedes the state of the art in user preference. Furthermore, it supports a variety of complex visualization needs including varied sizes, contrast, online or offline tracking, with a simple robust integration requiring no additional calibration for registration. Full article
(This article belongs to the Special Issue Advanced Measurements for Industry 4.0)
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15 pages, 614 KiB  
Article
Performance of LoRaWAN for Handling Telemetry and Alarm Messages in Industrial Applications
by Francisco Helder C. dos Santos Filho, Plínio S. Dester, Elvis M. G. Stancanelli, Paulo Cardieri, Pedro H. J. Nardelli, Dick Carrillo and Hirley Alves
Sensors 2020, 20(11), 3061; https://doi.org/10.3390/s20113061 - 28 May 2020
Cited by 17 | Viewed by 6885
Abstract
This paper analyzes the feasibility of the coexistence of telemetry and alarm messages employing Long-Range Wide-Area Network (LoRaWAN) technology in industrial environments. The regular telemetry messages come from periodic measurements from the majority of sensors while the alarm messages come from sensors whose [...] Read more.
This paper analyzes the feasibility of the coexistence of telemetry and alarm messages employing Long-Range Wide-Area Network (LoRaWAN) technology in industrial environments. The regular telemetry messages come from periodic measurements from the majority of sensors while the alarm messages come from sensors whose transmissions are triggered by rarer (random) events that require highly reliable communication. To reach such a strict requirement, we propose here strategies of allocation of spreading factor, by treating alarm and regular (telemetry) messages differently. The potential of such allocation strategies has also been investigated under retransmission and diversity of gateways. Both indoor industrial plant and open-field scenarios are investigated. We compare the proposed solution with a benchmark scenario—where no alarm is considered—by using system level simulation. Our results show that it is possible to achieve high reliability with reasonably low delay for the alarm messages without significantly affecting the performance of the regular links. Full article
(This article belongs to the Special Issue Advanced Measurements for Industry 4.0)
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Review

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23 pages, 2038 KiB  
Review
Motion Capture Technology in Industrial Applications: A Systematic Review
by Matteo Menolotto, Dimitrios-Sokratis Komaris, Salvatore Tedesco, Brendan O’Flynn and Michael Walsh
Sensors 2020, 20(19), 5687; https://doi.org/10.3390/s20195687 - 05 Oct 2020
Cited by 123 | Viewed by 13570
Abstract
The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings [...] Read more.
The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition. Full article
(This article belongs to the Special Issue Advanced Measurements for Industry 4.0)
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25 pages, 1097 KiB  
Review
Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY
by Tamás Czimmermann, Gastone Ciuti, Mario Milazzo, Marcello Chiurazzi, Stefano Roccella, Calogero Maria Oddo and Paolo Dario
Sensors 2020, 20(5), 1459; https://doi.org/10.3390/s20051459 - 06 Mar 2020
Cited by 204 | Viewed by 18536
Abstract
This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape [...] Read more.
This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning. Full article
(This article belongs to the Special Issue Advanced Measurements for Industry 4.0)
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