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Sensor Applications for Smart Manufacturing Technology and Systems

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

Deadline for manuscript submissions: closed (15 June 2019) | Viewed by 39321

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Special Issue Editors

Prof. Dr. Roberto Teti

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Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh J_LEAPT Naples), Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, P.le Tecchio 80, 80125 Naples, Italy
Interests: intelligent sensor systems for manufacturing process monitoring; big data analytics for cloud manufacturing; cyberphysical production systems; nondestructive inspection; artificial intelligence and machine learning for industrial automation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Alessandro Simeone

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Department of Mechatronic Engineering, Shantou University, Shantou 515063, China
Interests: sensing technologies; intelligent decision making; sustainable manufacturing
Special Issues, Collections and Topics in MDPI journals

Dr. Alessandra Caggiano

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Guest Editor

Manufacturing Technology and Systems, Department of Industrial Engineering, University of Naples Federico II, 80125 Napoli NA, Italy
Interests: digital factory technologies; intelligent sensor monitoring of manufacturing processes; 3D metrology and reverse engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern manufacturing systems are ever more developing as cyber–physical systems that are able to collect data from the surrounding physical environment and to use them in order to make autonomous decisions using smart functionalities.

Such systems increasingly rely on the employment of heterogeneous sensors to collect data from the manufacturing process, product and system that can be utilized for different purposes such as the condition monitoring of machines, processes and tools; predictive maintenance; quality control; resource management; etc.

To effectively take advantage of these sensors in view of the realization of smart manufacturing systems, artificial intelligence techniques such as knowledge-based systems, machine learning and other cognitive approaches can be applied.

In this Special Issue, original and review articles on the application of “Sensor Applications for Smart Manufacturing Technology and Systems” are solicited with reference to the various sectors of advanced manufacturing technology and systems.

Topics of interest include, but are not limited to, the smart application of sensors for the following areas:

Smart Manufacturing Technologies
Manufacturing Process Monitoring
Tool Condition Monitoring
Quality Control
Manufacturing System Maintenance
Manufacturing System Modelling, Design, Planning and Control
Energy and Resource Efficiency
Cognitive Systems for Production Engineering
Digital Factory
Cyber–Physical Production Systems
Cloud-Based Manufacturing
Industry 4.0

Prof. Dr. Roberto Teti
Prof. Dr. Alessandro Simeone
Prof. Dr. Alessandra Caggiano
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

Sensors
Manufacturing systems
Advanced Manufacturing Technology
Sensor signal processing
Artificial intelligence
Cyber–physical systems
Cognitive systems
Industry 4.0

Published Papers (7 papers)

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Research

18 pages, 4895 KiB

Open AccessArticle

A Multi-Sensor System for Silkworm Cocoon Gender Classification via Image Processing and Support Vector Machine

by Alex Noel Joseph Raj, Rahul Sundaram, Vijayalakshmi G.V. Mahesh, Zhemin Zhuang and Alessandro Simeone

Sensors 2019, 19(12), 2656; https://doi.org/10.3390/s19122656 - 12 Jun 2019

Cited by 19 | Viewed by 9664

Abstract

Sericulture is traditionally a labor-intensive rural-based industry. In modern contexts, the development of process automation faces new challenges related to quality and efficiency. During the silkworm farming life cycle, a common issue is represented by the gender classification of the cocoons. Improper cocoon separation negatively affects quantity and quality of the yield resulting in disruptive bottlenecks for the productivity. To tackle this issue, this paper proposes a multi sensor system for silkworm cocoons gender classification and separation. Utilizing a load sensor and a digital camera, the system acquires weight and digital images from individual silkworm cocoons. An image processing procedure is then applied to extract significant shape-related features from each image instance, which, combined with the weight data, are provided as inputs to train a Support Vector Machine-based pattern classifier for gender classification. Subsequently, an air blower mechanism and a conveyor system sort the cocoons into their respective bins. The developed system was trained and tested on two different types of silkworm cocoons breeds, respectively CSR2 and Pure Mysore. The system performances are finally discussed in terms of accuracy, robustness and computation time. Full article

(This article belongs to the Special Issue Sensor Applications for Smart Manufacturing Technology and Systems)

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19 pages, 2580 KiB

Open AccessArticle

Risk-Oriented Product Assembly System Health Modeling and Predictive Maintenance Strategy

by Fengdi Liu, Yihai He, Yixiao Zhao, Anqi Zhang and Di Zhou

Sensors 2019, 19(9), 2086; https://doi.org/10.3390/s19092086 - 05 May 2019

Cited by 3 | Viewed by 2806

Abstract

Assembly quality is the barometer of assembly system health, and a healthy assembly system is an important physical guarantee for producing reliable products. Therefore, for ensuring the high reliability of products, the operational data of the assembly system should be analyzed to manage health states. Therefore, based on the operational data of the assembly system collected by intelligent sensors, from the perspective of quality control based on risk thinking, a risk-oriented health assessment method and predictive maintenance strategy for managing assembly system health are proposed. First, considering the loss of product reliability, the concept of assembly system health risk is proposed, and the risk formation mechanism is expounded. Second, the process variation data of key reliability characteristics (KRCs) collected by different sensors are used to measure and assess the health risk of the running assembly system to evaluate the health state. Third, the assembly system health risk is used as the maintenance threshold, the predictive maintenance decision model is established, and the optimal maintenance strategy is determined through stepwise optimization. Finally, the case study verifies the effectiveness and superiority of the proposed method. Results show that the proposed method saves 37.40% in costs compared with the traditional method. Full article

(This article belongs to the Special Issue Sensor Applications for Smart Manufacturing Technology and Systems)

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22 pages, 15425 KiB

Open AccessArticle

A Novel Surface Descriptor for Automated 3-D Object Recognition and Localization

by Liang-Chia Chen and Thanh-Hung Nguyen

Sensors 2019, 19(4), 764; https://doi.org/10.3390/s19040764 - 13 Feb 2019

Cited by 3 | Viewed by 2946

Abstract

This paper presents a novel approach to the automated recognition and localization of 3-D objects. The proposed approach uses 3-D object segmentation to segment randomly stacked objects in an unstructured point cloud. Each segmented object is then represented by a regional area-based descriptor, which measures the distribution of surface area in the oriented bounding box (OBB) of the segmented object. By comparing the estimated descriptor with the template descriptors stored in the database, the object can be recognized. With this approach, the detected object can be matched with the model using the iterative closest point (ICP) algorithm to detect its 3-D location and orientation. Experiments were performed to verify the feasibility and effectiveness of the approach. With the measured point clouds having a spatial resolution of 1.05 mm, the proposed method can achieve both a mean deviation and standard deviation below half of the spatial resolution. Full article

(This article belongs to the Special Issue Sensor Applications for Smart Manufacturing Technology and Systems)

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19 pages, 5540 KiB

Open AccessArticle

A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification

by Xi Chen, Fotis Kopsaftopoulos, Qi Wu, He Ren and Fu-Kuo Chang

Sensors 2019, 19(2), 275; https://doi.org/10.3390/s19020275 - 11 Jan 2019

Cited by 29 | Viewed by 10254

Abstract

The vibration of a wing structure in the air reflects coupled aerodynamic–mechanical responses under varying flight states that are defined by the angle of attack and airspeed. It is of great challenge to identify the flight state from the complex vibration signals. In this paper, a novel one-dimension convolutional neural network (CNN) is developed, which is able to automatically extract useful features from the structural vibration of a recently fabricated self-sensing wing through wind-tunnel experiments. The obtained signals are firstly decomposed into various subsignals with different frequency bands via dual-tree complex-wavelet packet transformation. Then, the reconstructed subsignals are selected to form the best combination for multichannel inputs of the CNN. A swarm-based evolutionary algorithm called grey-wolf optimizer is utilized to optimize a set of key parameters of the CNN, which saves considerable human efforts. Two case studies demonstrate the high identification accuracy and robustness of the proposed method over standard deep-learning methods in flight-state identification, thus providing new perspectives in self-awareness toward the next generation of intelligent air vehicles. Full article

(This article belongs to the Special Issue Sensor Applications for Smart Manufacturing Technology and Systems)

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17 pages, 6122 KiB

Open AccessArticle

Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 1—PZT Diaphragm Transducer Response and EMI Sensing Technique

by Pedro Junior, Doriana M. D’Addona, Paulo R. Aguiar and Roberto Teti

Sensors 2018, 18(12), 4455; https://doi.org/10.3390/s18124455 - 16 Dec 2018

Cited by 15 | Viewed by 3817

Abstract

Low-cost piezoelectric lead zirconate titanate (PZT) diaphragm transducers have attracted increasing attention as effective sensing devices, based on the electromechanical impedance (EMI) principle, for applications in many engineering sectors. Due to the considerable potential of PZT diaphragm transducers in terms of excellent electromechanical coupling properties, low implementation cost and wide-band frequency response, this technique provides a new alternative approach for tool condition monitoring in grinding processes competing with the conventional and expensive indirect sensor monitoring methods. This paper aims at assessing the structural changes caused by wear in single-point dressers during their lifetime, in order to ensure the reliable monitoring of the tool condition during dressing operations. Experimental dressing tests were conducted on aluminum oxide grinding wheels, which are highly relevant for industrial grinding processes. From the results obtained, it was verified that the dresser tip diamond material and the position of the PZT diaphragm transducer mounted on the dressing tool holder have a significant effect on the sensitivity of damage detection. This paper contributes to the realization of an effective monitoring system of dressing operations capable to avoid catastrophic tool failures as the proposed sensing approach can identify different stages of the dressing tool lifetime based on representative damage indices. Full article

(This article belongs to the Special Issue Sensor Applications for Smart Manufacturing Technology and Systems)

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21 pages, 9410 KiB

Open AccessArticle

Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 2—Neural Networks and K-Nearest Neighbor Classifier Approach

by Pedro Junior, Doriana M. D’Addona, Paulo Aguiar and Roberto Teti

Sensors 2018, 18(12), 4453; https://doi.org/10.3390/s18124453 - 16 Dec 2018

Cited by 21 | Viewed by 3688

Abstract

This paper presents an approach for impedance-based sensor monitoring of dressing tool condition in grinding by using the electromechanical impedance (EMI) technique. This method was introduced in Part 1 of this work and the purpose of this paper (Part 2) is to achieve an optimal selection of the excitation frequency band based on multi-layer neural networks (MLNN) and k-nearest neighbor classifier (k-NN). The proposed approach was validated on the basis of dressing tool condition information obtained from the monitoring of experimental dressing tests with two industrial stationary single-point dressing tools. Moreover, representative damage indices for diverse damage cases, obtained from impedance signatures at different frequency bands, were taken into account for MLNN data processing. The intelligent system was able to select the most damage-sensitive features based on optimal frequency band. The best models showed a general overall error lower than 2%, thus robustly contributing to the efficient automation of grinding and dressing operations. The promising results of this study foster the EMI-based sensor monitoring approach to fault diagnosis in dressing operations and its effective implementation for industrial grinding process automation. Full article

(This article belongs to the Special Issue Sensor Applications for Smart Manufacturing Technology and Systems)

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17 pages, 3129 KiB

Open AccessArticle

Vision-Based Apple Classification for Smart Manufacturing

by Ahsiah Ismail, Mohd Yamani Idna Idris, Mohamad Nizam Ayub and Lip Yee Por

Sensors 2018, 18(12), 4353; https://doi.org/10.3390/s18124353 - 10 Dec 2018

Cited by 13 | Viewed by 4960

Abstract

Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN). The vision-based classifiers categorize the apple as defective and non-defective that can be used for automatic inspection, sorting and further analytics. A total of 550 apple images are collected to test the classifiers. The images consist of 275 non-defective and 275 defective apples. The defective category includes various types of defect and severity. The vision-based classifiers are trained and evaluated according to the K-fold cross-validation. The performances of the classifiers from 2-fold, 3-fold, 4-fold, 5-fold and 10-fold are compared. From the evaluation, SPM with SVM classifier attained 98.15% classification accuracy for 10-fold and outperformed the others. In terms of computational time, CNN with SVM classifier is the fastest. However, minimal time difference is observed between the computational time of CNN and SPM, which were separated by only 0.05 s. Full article

(This article belongs to the Special Issue Sensor Applications for Smart Manufacturing Technology and Systems)

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