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Advanced Measurements for Industry 4.0 II
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Advanced Measurements for Industry 4.0 II

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

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 13833

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: Innovative solutions for sensing the motion of industrial robots or human supervisors;Robust pose determination solutions for augmented reality;Original sensing solutions to improve additive manufacturing;Sensors and systems for industrial infrastructure inspection;Sensing solutions adopted to increase the safety of human operators;Integrated sensing systems that exploit broadband wireless communications with reduced latency, such as 5G mobile networks;Smart sensors for industrial applications;Networked sensor systems to support innovative industrial processes;Internet of Things for industry applications;Innovative navigation solutions for autonomous transport platforms used in industrial production processes, including ground platforms and aerial drones;Innovative sensor data fusion solutions to improve manufacturing processes;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

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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

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Published Papers (5 papers)

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Research

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16 pages, 2856 KiB  
Article
Powder Bed Monitoring Using Semantic Image Segmentation to Detect Failures during 3D Metal Printing
by Anna-Maria Schmitt, Christian Sauer, Dennis Höfflin and Andreas Schiffler
Sensors 2023, 23(9), 4183; https://doi.org/10.3390/s23094183 - 22 Apr 2023
Cited by 4 | Viewed by 1926
Abstract
Monitoring the metal Additive Manufacturing (AM) process is an important task within the scope of quality assurance. This article presents a method to gain insights into process quality by comparing the actual and target layers. Images of the powder bed were captured and [...] Read more.
Monitoring the metal Additive Manufacturing (AM) process is an important task within the scope of quality assurance. This article presents a method to gain insights into process quality by comparing the actual and target layers. Images of the powder bed were captured and segmented using an Xception–style neural network to predict the powder and part areas. The segmentation result of every layer is compared to the reference layer regarding the area, centroids, and normalized area difference of each part. To evaluate the method, a print job with three parts was chosen where one of them broke off and another one had thermal deformations. The calculated metrics are useful for detecting if a part is damaged or for identifying thermal distortions. The method introduced by this work can be used to monitor the metal AM process for quality assurance. Due to the limited camera resolutions and inconsistent lighting conditions, the approach has some limitations, which are discussed at the end. Full article
(This article belongs to the Special Issue Advanced Measurements for Industry 4.0 II)
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19 pages, 4269 KiB  
Article
A Framework for Inclusion of Unmodelled Contact Tasks Dynamics in Industrial Robotics
by Zaviša Gordić and Kosta Jovanović
Sensors 2022, 22(19), 7650; https://doi.org/10.3390/s22197650 - 9 Oct 2022
Cited by 2 | Viewed by 1934
Abstract
This paper presents a method to include unmodeled dynamics of load or a robot’s end-effector into algorithms for collision detection or general understanding of a robot’s operation context. The approach relies on the application of a previously developed modification of the Dynamic Time [...] Read more.
This paper presents a method to include unmodeled dynamics of load or a robot’s end-effector into algorithms for collision detection or general understanding of a robot’s operation context. The approach relies on the application of a previously developed modification of the Dynamic Time Warping algorithm, as well as a universally applicable algorithm for identifying kinematic parameters. The entire process can be applied to arbitrary robot configuration, and it does not require identification of dynamic parameters. The paper addresses the two main categories of contact tasks with unmodelled dynamics, which are determined based on whether the external contact force has a consistent profile in the end effector or base coordinate. Conclusions for representative examples analysed in the paper are applicable to tasks such as load manipulation, press bending, and crimping for the first type of forces and applications such as drilling, screwdriving, snap-fit, bolting, and riveting assembly for the latter category. The results presented in the paper are based on realistic testing with measurements obtained from an industrial robot. Full article
(This article belongs to the Special Issue Advanced Measurements for Industry 4.0 II)
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15 pages, 7328 KiB  
Article
Design of the Automated Calibration Process for an Experimental Laser Inspection Stand
by Jaromír Klarák, Robert Andok, Jaroslav Hricko, Ivana Klačková and Hung-Yin Tsai
Sensors 2022, 22(14), 5306; https://doi.org/10.3390/s22145306 - 15 Jul 2022
Cited by 15 | Viewed by 2386
Abstract
This paper deals with the concept of the automated calibration design for inspection systems using laser sensors. The conceptual solution is based on using a laser sensor and its ability to scan 3D surfaces of inspected objects in order to create a representative [...] Read more.
This paper deals with the concept of the automated calibration design for inspection systems using laser sensors. The conceptual solution is based on using a laser sensor and its ability to scan 3D surfaces of inspected objects in order to create a representative point cloud. Problems of scanning are briefly discussed. The automated calibration procedure for solving problems of errors due to non-precise adjustment of the mechanical arrangement, possible tolerances in assembly, and their following elimination is proposed. The main goal is to develop a system able to measure and quantify the quality of produced objects in the environment of Industry 4.0. Laboratory measurements on the experimental stand, including the principal software solution for automated calibration of laser sensors suitable for gear wheel inspection systems are presented. There is described design of compensation eccentricity by Fourier transform and sinusoidal fitting to identify and suppress the first harmonic component in the data with high precision measuring. Full article
(This article belongs to the Special Issue Advanced Measurements for Industry 4.0 II)
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Review

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21 pages, 2182 KiB  
Review
How Industry 4.0 and Sensors Can Leverage Product Design: Opportunities and Challenges
by Albérico Travassos Rosário and Joana Carmo Dias
Sensors 2023, 23(3), 1165; https://doi.org/10.3390/s23031165 - 19 Jan 2023
Cited by 38 | Viewed by 4810
Abstract
The fourth industrial revolution, also known as Industry 4.0, has led to an increased transition towards automation and reliance on data-driven innovations and strategies. The interconnected systems and processes have significantly increased operational efficiency, enhanced organizational capacity to monitor and control functions, reduced [...] Read more.
The fourth industrial revolution, also known as Industry 4.0, has led to an increased transition towards automation and reliance on data-driven innovations and strategies. The interconnected systems and processes have significantly increased operational efficiency, enhanced organizational capacity to monitor and control functions, reduced costs, and improved product quality. One significant way that companies have achieved these benefits is by integrating diverse sensor technologies within these innovations. Given the rapidly changing market conditions, Industry 4.0 requires new products and business models to ensure companies adjust to the current and future changes. These requirements call for the evolutions in product design processes to accommodate design features and principles applicable in the current dynamic business environment. Thus, it becomes imperative to understand how these innovations can leverage product design to maximize benefits and opportunities. This research paper employs a Systematic Literature Review with Bibliometric Analysis (SLBA) methodology to explore and synthesize data on how Industry 4.0 and sensors can leverage product design. The results show that various product design features create opportunities to be leveraged to guarantee the success of Industry 4.0 and sensor technologies. However, the research also identifies numerous challenges that undermine the ongoing transition towards intelligent factories and products. Full article
(This article belongs to the Special Issue Advanced Measurements for Industry 4.0 II)
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Other

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17 pages, 2103 KiB  
Perspective
Wrapper Functions for Integrating Mathematical Models into Digital Twin Event Processing
by Reiner Jedermann and Walter Lang
Sensors 2022, 22(20), 7964; https://doi.org/10.3390/s22207964 - 19 Oct 2022
Cited by 4 | Viewed by 1735
Abstract
Analog sensors often require complex mathematical models for data analysis. Digital twins (DTs) provide platforms to display sensor data in real time but still lack generic solutions regarding how mathematical models and algorithms can be integrated. Based on previous tests for monitoring and [...] Read more.
Analog sensors often require complex mathematical models for data analysis. Digital twins (DTs) provide platforms to display sensor data in real time but still lack generic solutions regarding how mathematical models and algorithms can be integrated. Based on previous tests for monitoring and predicting banana fruit quality along the cool chain, we demonstrate how a system of multiple models can be converted into a DT. Our new approach provides a set of generic “wrapper functions”, which largely simplify model integration. The wrappers connect the in- and outputs of models to the streaming platform and, thus, require only minor changes to the model software. Different scenarios for model linking structures are considered, including simultaneous processing of multiple models, sequential processing of life-cycle-specific models, and predictive models, based on data from the current and previous life cycles. The wrapper functions can be easily adapted to host models or microservices from various applications fields, to predict the future system behavior and to test what-if scenarios. Full article
(This article belongs to the Special Issue Advanced Measurements for Industry 4.0 II)
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