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Multisensor Data Fusion Methods in Advanced Manufacturing

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

Deadline for manuscript submissions: closed (15 January 2021) | Viewed by 8437

Special Issue Editor

Politecnico di Milano, Department of Mechanical Engineering
Interests: statistical process monitoring; data mining; data modeling; big data analytics; video/image data analysis; signal processing; image processing; advanced manufacturing; additive manufacturing

Special Issue Information

Dear Colleagues,

In the current Industry 4.0 framework, the use of integrated and multiple sensors is becoming increasingly available for a wide range of applications. On the one hand, continuous technological advances are making real-time data processing and analysis from multiple sources a viable solution to achieving novel and smart manufacturing capabilities. On the other hand, new production paradigms—like additive manufacturing—are enabling novel in-process and multisensor data-gathering capabilities. Nevertheless, in most industrial applications, each single signal is commonly processed and treated as an individual source of information, and there are still few demonstrations of the actual potential enabled by merging and jointly exploiting the available multisource information content.

Therefore, we invite applicants to highlight the benefits of multisensor data fusion methods with respect to common industrial practices by presenting recent developments and innovative solutions applied to smart and advanced manufacturing processes.

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

  • Multisensor data modeling;
  • Multisensor data reduction;
  • Multisensor data fusion via machine learning and artificial intelligence;
  • Multisensor fusion of image and video-image data;
  • Statistical process monitoring and quality control via multisensor data;
  • Multisensor fault detection and diagnostics;
  • Multisensor health condition monitoring;
  • Multisensor data fusion for predictive maintenance;
  • Multisensor/multifidelity data fusion in metrology;
  • Multisensor alignment, synchronization, and calibration;
  • Data fusion with redundant data;
  • Reliability enhancement via multisensor data and sensor validation.

Dr. Marco Grasso
Guest Editor

Manuscript Submission Information

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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

  • multisensor data fusion
  • multisensor data modeling
  • multisensor data reduction
  • advanced manufacturing
  • additive manufacturing
  • image fusion
  • fault detection
  • diagnostics
  • predictive maintenance
  • metrology
  • synchronization
  • calibration

Published Papers (3 papers)

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Research

17 pages, 6229 KiB  
Article
Traceable Reference Full Metrology Chain for Innovative Aspheric and Freeform Optical Surfaces Accurate at the Nanometer Level
by Yassir Arezki, Rong Su, Ville Heikkinen, François Leprete, Pavel Posta, Youichi Bitou, Christian Schober, Charyar Mehdi-Souzani, Bandar Abdulrahman Mohammed Alzahrani, Xiangchao Zhang, Yohan Kondo, Christof Pruss, Vit Ledl, Nabil Anwer, Mohamed Lamjed Bouazizi, Richard Leach and Hichem Nouira
Sensors 2021, 21(4), 1103; https://doi.org/10.3390/s21041103 - 05 Feb 2021
Cited by 3 | Viewed by 3154
Abstract
The design of innovative reference aspheric and freeform optical elements was investigated with the aim of calibration and verification of ultra-high accurate measurement systems. The verification is dedicated to form error analysis of aspherical and freeform optical surfaces based on minimum zone fitting. [...] Read more.
The design of innovative reference aspheric and freeform optical elements was investigated with the aim of calibration and verification of ultra-high accurate measurement systems. The verification is dedicated to form error analysis of aspherical and freeform optical surfaces based on minimum zone fitting. Two thermo-invariant material measures were designed, manufactured using a magnetorheological finishing process and selected for the evaluation of a number of ultra-high-precision measurement machines. All collected data sets were analysed using the implemented robust reference minimum zone (Hybrid Trust Region) fitting algorithm to extract the values of form error. Agreement among the results of several partners was observed, which demonstrates the establishment of a traceable reference full metrology chain for aspherical and freeform optical surfaces with small amplitudes. Full article
(This article belongs to the Special Issue Multisensor Data Fusion Methods in Advanced Manufacturing)
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18 pages, 1649 KiB  
Article
MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing
by Lening Wang, Pang Du and Ran Jin
Sensors 2021, 21(1), 243; https://doi.org/10.3390/s21010243 - 01 Jan 2021
Cited by 3 | Viewed by 2104
Abstract
Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ [...] Read more.
Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ process variables from different sensor modalities to reflect the process variations in real-time. However, in practice, we usually do not have enough budget to equip too many sensors in each manufacturing process due to the cost consideration. Moreover, it is also important to better interpret the relationship between the sensing modalities and the quality variables based on the model. Therefore, it is necessary to model the quality-process relationship by selecting the most relevant sensor modalities with the specific quality measurement from the multi-modal sensing system in smart manufacturing. In this research, we adopted the concept of best subset variable selection and proposed a new model called Multi-mOdal beSt Subset modeling (MOSS). The proposed MOSS can effectively select the important sensor modalities and improve the modeling accuracy in quality-process modeling via functional norms that characterize the overall effects of individual modalities. The significance of sensor modalities can be used to determine the sensor placement strategy in smart manufacturing. Moreover, the selected modalities can better interpret the quality-process model by identifying the most correlated root cause of quality variations. The merits of the proposed model are illustrated by both simulations and a real case study in an additive manufacturing (i.e., fused deposition modeling) process. Full article
(This article belongs to the Special Issue Multisensor Data Fusion Methods in Advanced Manufacturing)
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21 pages, 1974 KiB  
Article
Fault Diagnosis by Multisensor Data: A Data-Driven Approach Based on Spectral Clustering and Pairwise Constraints
by Massimo Pacella and Gabriele Papadia
Sensors 2020, 20(24), 7065; https://doi.org/10.3390/s20247065 - 10 Dec 2020
Cited by 7 | Viewed by 2062
Abstract
This paper deals with clustering based on feature selection of multisensor data in high-dimensional space. Spectral clustering algorithms are efficient tools in signal processing for grouping datasets sampled by multisensor systems for fault diagnosis. The effectiveness of spectral clustering stems from constructing an [...] Read more.
This paper deals with clustering based on feature selection of multisensor data in high-dimensional space. Spectral clustering algorithms are efficient tools in signal processing for grouping datasets sampled by multisensor systems for fault diagnosis. The effectiveness of spectral clustering stems from constructing an embedding space based on an affinity matrix. This matrix shows the pairwise similarity of the data points. Clustering is then obtained by determining the spectral decomposition of the Laplacian graph. In the manufacturing field, clustering is an essential strategy for fault diagnosis. In this study, an enhanced spectral clustering approach is presented, which is augmented with pairwise constraints, and that results in efficient identification of fault scenarios. The effectiveness of the proposed approach is described using a real case study about a diesel injection control system for fault detection. Full article
(This article belongs to the Special Issue Multisensor Data Fusion Methods in Advanced Manufacturing)
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