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Sensing for In-Process Monitoring of High-Value Manufacturing Processes

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

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 21419

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Special Issue Editors

Prof. Dr. Ashutosh Tiwari

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Guest Editor

Department of Automatic Control and Systems Engineering (ACSE), University of Sheffield, Portobello Street, Sheffield S1 3JD, UK
Interests: digital manufacturing; digitisation of skill-intensive manufacturing processes; autonomous manufacturing; multi-level optimisation; simulation; manufacturing informatics; machine learning; sensing and IoT
Special Issues, Collections and Topics in MDPI journals

Prof. Geraint Jewell

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Guest Editor

Department of Electronic and Electrical Engineering, Mappin Street, University of Sheffield, Sheffield S1 3JD, UK
Interests: power dense electrical machines for aerospace applications; linear electromagnetic actuators; electromagnetic modelling of novel devices; manufacturing of electrical machines

Dr. Divya Tiwari

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Guest Editor

Department of Automatic Control and Systems Engineering (ACSE), University of Sheffield, Portobello Street, Sheffield S1 3JD, UK
Interests: simulation; sensing and IoT; optical fibre sensing technologies; digital manufacturing

Dr. Michael Farnsworth

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Guest Editor

Department of Automatic Control and Systems Engineering (ACSE), University of Sheffield, Portobello Street, Sheffield S1 3JD, UK
Interests: evolutionary computation; digital manufacturing; robotics; autonomous systems; IoT

Special Issue Information

Dear Colleagues,

High-value manufacturing processes are reliant on strict quality outcomes which can have a significant influence on the operating behavior and lifetime of products. As an example, the strict safety regulations within aerospace bring with them the challenge of maintaining a high level of control and tolerance within the manufacturing process.

Detecting a defect at a late stage in the manufacturing process results in wasted time and effort and the need for extensive rework or scrapping. Precise and repeatable monitoring of products and processes during manufacturing can potentially track and trace the origin of arising defects and allow consistent product quality and process optimization. With the advent of IoT and other forms of digitization, online process monitoring, and control can be harnessed to improve production speed, quality, and reliability.

This Special Issue aims to bring together research relating to sensing for in-process monitoring of high-value manufacturing processes. We invite you to contribute to this issue by submitting both case studies and research articles focusing on high-value manufacturing processes. This Special Issue will cover papers from a range of manufacturing processes including (but not limited to): surface manufacturing, machining, assembly, and continuous manufacturing. We particularly welcome papers from high-value manufacturing application areas, such as electrical machine manufacture, aerostructure assembly, and surface manufacture.

Prof. Ashutosh Tiwari
Prof. Geraint Jewell
Dr. Divya Tiwari
Dr. Michael Farnsworth
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

digital manufacturing
sensing
Industry 4.0
in-process monitoring
process control
real-time simulation
machine learning
sensor fusion
digital twin
soft sensing
dynamic optimization

Published Papers (9 papers)

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Research

16 pages, 4636 KiB

Open AccessArticle

Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques

by Divya Tiwari, David Miller, Michael Farnsworth, Alexis Lambourne, Geraint W. Jewell and Ashutosh Tiwari

Sensors 2023, 23(8), 3977; https://0-doi-org.brum.beds.ac.uk/10.3390/s23083977 - 14 Apr 2023

Cited by 1 | Viewed by 1319

Abstract

Within aerospace and automotive manufacturing, the majority of quality assurance is through inspection or tests at various steps during manufacturing and assembly. Such tests do not tend to capture or make use of process data for in-process inspection and certification at the point of manufacture. Inspection of the product during manufacturing can potentially detect defects, thus allowing consistent product quality and reducing scrappage. However, a review of the literature has revealed a lack of any significant research in the area of inspection during the manufacturing of terminations. This work utilises infrared thermal imaging and machine learning techniques for inspection of the enamel removal process on Litz wire, typically used for aerospace and automotive applications. Infrared thermal imaging was utilised to inspect bundles of Litz wire containing those with and without enamel. The temperature profiles of the wires with or without enamel were recorded and then machine learning techniques were utilised for automated inspection of enamel removal. The feasibility of various classifier models for identifying the remaining enamel on a set of enamelled copper wires was evaluated. A comparison of the performance of classifier models in terms of classification accuracy is presented. The best model for enamel classification accuracy was the Gaussian Mixture Model with expectation maximisation; it achieved a training accuracy of 85% and enamel classification accuracy of 100% with the fastest evaluation time of 1.05 s. The support vector classification model achieved both the training and enamel classification accuracy of more than 82%; however, it suffered the drawback of a higher evaluation time of 134 s. Full article

(This article belongs to the Special Issue Sensing for In-Process Monitoring of High-Value Manufacturing Processes)

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19 pages, 3498 KiB

Open AccessArticle

Reliability Improvement of Magnetic Corrosion Monitor for Long-Term Applications

by Rukhshinda Wasif, Mohammad Osman Tokhi, John Rudlin, Gholamhossein Shirkoohi and Fang Duan

Sensors 2023, 23(4), 2212; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042212 - 16 Feb 2023

Viewed by 1151

Abstract

Electromagnetic techniques are widely employed for corrosion detection, and their performance for inspection of corrosion is well established. However, limited work is carried out on the development and reliability of smart corrosion monitoring devices for tracking internal or buried thickness loss due to corrosion remotely. A novel smart magnetic corrosion transducer is developed for long-term monitoring of thickness loss due to corrosion at critical locations. The reliability of the transducer is enhanced by using a dissimilar active redundancy approach. The improved corrosion monitor has been tested in the ambient environment for seven months to evaluate the stability against environmental factors and degradation. The monitor is found to show great sensitivity to detect defects due to corrosion. Detection of anomalous patterns in the time series data received from the monitors is accomplished by using Pearson’s correlation coefficient. The critical component of the monitor is identified at the end of the test. Research findings reveal that, compared to the existing corrosion monitoring techniques in the industry, the detection and isolation of faulty sensor features introduced in this study can contribute to reliable monitoring of thickness loss due to corrosion in ferromagnetic structures over an extended period of time. Full article

(This article belongs to the Special Issue Sensing for In-Process Monitoring of High-Value Manufacturing Processes)

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17 pages, 19636 KiB

Open AccessArticle

Encoding Stability into Laser Powder Bed Fusion Monitoring Using Temporal Features and Pore Density Modelling

by Brian G. Booth, Rob Heylen, Mohsen Nourazar, Dries Verhees, Wilfried Philips and Abdellatif Bey-Temsamani

Sensors 2022, 22(10), 3740; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103740 - 14 May 2022

Cited by 7 | Viewed by 2383

Abstract

In laser powder bed fusion (LPBF), melt pool instability can lead to the development of pores in printed parts, reducing the part’s structural strength. While camera-based monitoring systems have been introduced to improve melt pool stability, these systems only measure melt pool stability in limited, indirect ways. We propose that melt pool stability can be improved by explicitly encoding stability into LPBF monitoring systems through the use of temporal features and pore density modelling. We introduce the temporal features, in the form of temporal variances of common LPBF monitoring features (e.g., melt pool area, intensity), to explicitly quantify printing stability. Furthermore, we introduce a neural network model trained to link these video features directly to pore densities estimated from the CT scans of previously printed parts. This model aims to reduce the number of online printer interventions to only those that are required to avoid porosity. These contributions are then implemented in a full LPBF monitoring system and tested on prints using 316L stainless steel. Results showed that our explicit stability quantification improved the correlation between our predicted pore densities and true pore densities by up to 42%. Full article

(This article belongs to the Special Issue Sensing for In-Process Monitoring of High-Value Manufacturing Processes)

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19 pages, 6477 KiB

Open AccessArticle

Mobile Robots for In-Process Monitoring of Aircraft Systems Assemblies

by Marc Auledas-Noguera, Amer Liaqat and Ashutosh Tiwari

Sensors 2022, 22(9), 3362; https://doi.org/10.3390/s22093362 - 27 Apr 2022

Cited by 1 | Viewed by 1870

Abstract

Currently, systems installed on large-scale aerospace structures are manually equipped by trained operators. To improve current methods, an automated system that ensures quality control and process adherence could be used. This work presents a mobile robot capable of autonomously inspecting aircraft systems and providing feedback to workers. The mobile robot can follow operators and localise the position of the inspection using a thermal camera and 2D lidars. While moving, a depth camera collects 3D data about the system being installed. The in-process monitoring algorithm uses this information to check if the system has been correctly installed. Finally, based on these measurements, indications are shown on a screen to provide feedback to the workers. The performance of this solution has been validated in a laboratory environment, replicating a trailing edge equipping task. During testing, the tracking and localisation systems have proven to be reliable. The in-process monitoring system was also found to provide accurate feedback to the operators. Overall, the results show that the solution is promising for industrial applications. Full article

(This article belongs to the Special Issue Sensing for In-Process Monitoring of High-Value Manufacturing Processes)

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15 pages, 461 KiB

Open AccessArticle

NIR-Based Intelligent Sensing of Product Yield Stress for High-Value Bioresorbable Polymer Processing

by Konrad Mulrennan, Nimra Munir, Leo Creedon, John Donovan, John G. Lyons and Marion McAfee

Sensors 2022, 22(8), 2835; https://0-doi-org.brum.beds.ac.uk/10.3390/s22082835 - 07 Apr 2022

Cited by 3 | Viewed by 1804

Abstract

PLA (polylactide) is a bioresorbable polymer used in implantable medical and drug delivery devices. Like other bioresorbable polymers, PLA needs to be processed carefully to avoid degradation. In this work we combine in-process temperature, pressure, and NIR spectroscopy measurements with multivariate regression methods for prediction of the mechanical strength of an extruded PLA product. The potential to use such a method as an intelligent sensor for real-time quality analysis is evaluated based on regulatory guidelines for the medical device industry. It is shown that for the predictions to be robust to processing at different times and to slight changes in the processing conditions, the fusion of both NIR and conventional process sensor data is required. Partial least squares (PLS), which is the established ’soft sensing’ method in the industry, performs the best of the linear methods but demonstrates poor reliability over the full range of processing conditions. Conversely, both random forest (RF) and support vector regression (SVR) show excellent performance for all criteria when used with a prior principal component (PC) dimension reduction step. While linear methods currently dominate for soft sensing of mixture concentrations in highly conservative, regulated industries such as the medical device industry, this work indicates that nonlinear methods may outperform them in the prediction of mechanical properties from complex physicochemical sensor data. The nonlinear methods show the potential to meet industrial standards for robustness, despite the relatively small amount of training data typically available in high-value material processing. Full article

(This article belongs to the Special Issue Sensing for In-Process Monitoring of High-Value Manufacturing Processes)

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26 pages, 11441 KiB

Open AccessArticle

Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study

by Jinlin Zhu, Muyun Jiang and Zhong Liu

Sensors 2022, 22(1), 227; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010227 - 29 Dec 2021

Cited by 10 | Viewed by 2897

Abstract

This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks. Full article

(This article belongs to the Special Issue Sensing for In-Process Monitoring of High-Value Manufacturing Processes)

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15 pages, 3060 KiB

Open AccessArticle

Short Time Correlation Analysis of Melt Pool Behavior in Laser Metal Deposition Using Coaxial Optical Monitoring

by Yury N. Zavalov and Alexander V. Dubrov

Sensors 2021, 21(24), 8402; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248402 - 16 Dec 2021

Cited by 3 | Viewed by 2193

Abstract

The development and improvement of monitoring and process control systems is one of the important ways of advancing laser metal deposition (LMD). The control of hydrodynamic, heat and mass transfer processes in LMD is extremely important, since these processes directly affect the crystallization of the melt and, accordingly, the microstructural properties and the overall quality of the synthesized part. In this article, the data of coaxial video monitoring of the LMD process were used to assess the features of melt dynamics. The obtained images were used to calculate the time dependences of the characteristics of the melt pool (MP) (temperature, width, length and area), which were further processed using the short-time correlation (STC) method. This approach made it possible to reveal local features of the joint behavior of the MP characteristics, and to analyze the nature of the melt dynamics. It was found that the behavior of the melt in the LMD is characterized by the presence of many time periods (patterns), during which it retains a certain ordered character. The features of behavior that are important from the point of view of process control systems design are noted. The approach used for the analysis of melt dynamics based on STC distributions of MP characteristics, as well as the method for determining the moments of pattern termination through the calculation of the correlation power, can be used in processing the results of online LMD diagnostics, as well as in process control systems. Full article

(This article belongs to the Special Issue Sensing for In-Process Monitoring of High-Value Manufacturing Processes)

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35 pages, 35452 KiB

Open AccessArticle

Time Latency-Centric Signal Processing: A Perspective of Smart Manufacturing

by Sharifu Ura and Angkush Kumar Ghosh

Sensors 2021, 21(21), 7336; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217336 - 04 Nov 2021

Cited by 3 | Viewed by 2627

Abstract

Smart manufacturing employs embedded systems such as CNC machine tools, programable logic controllers, automated guided vehicles, robots, digital measuring instruments, cyber-physical systems, and digital twins. These systems collectively perform high-level cognitive tasks (monitoring, understanding, deciding, and adapting) by making sense of sensor signals. When sensor signals are exchanged through the abovementioned embedded systems, a phenomenon called time latency or delay occurs. As a result, the signal at its origin (e.g., machine tools) and signal received at the receiver end (e.g., digital twin) differ. The time and frequency domain-based conventional signal processing cannot adequately address the delay-centric issues. Instead, these issues can be addressed by the delay domain, as suggested in the literature. Based on this consideration, this study first processes arbitrary signals in time, frequency, and delay domains and elucidates the significance of delay domain over time and frequency domains. Afterward, real-life signals collected while machining different materials are analyzed using frequency and delay domains to reconfirm its (the delay domain’s) significance in real-life settings. In both cases, it is found that the delay domain is more informative and reliable than the time and frequency domains when the delay is unavoidable. Moreover, the delay domain can act as a signature of a machining situation, distinguishing it (the situation) from others. Therefore, computational arrangements enabling delay domain-based signal processing must be enacted to effectively functionalize the smart manufacturing-centric embedded systems. Full article

(This article belongs to the Special Issue Sensing for In-Process Monitoring of High-Value Manufacturing Processes)

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20 pages, 1077 KiB

Open AccessArticle

A Cost-Effective CNN-LSTM-Based Solution for Predicting Faulty Remote Water Meter Reading Devices in AMI Systems

by Jaeseung Lee, Woojin Choi and Jibum Kim

Sensors 2021, 21(18), 6229; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186229 - 17 Sep 2021

Cited by 5 | Viewed by 3589

Abstract

Automatic meter infrastructure (AMI) systems using remote metering are being widely used to utilize water resources efficiently and minimize non-revenue water. We propose a convolutional neural network-long short-term memory network (CNN-LSTM)-based solution that can predict faulty remote water meter reading (RWMR) devices by analyzing approximately 2,850,000 AMI data collected from 2762 customers over 360 days in a small-sized city in South Korea. The AMI data used in this study is a challenging, highly unbalanced real-world dataset with limited features. First, we perform extensive preprocessing steps and extract meaningful features for handling this challenging dataset with limited features. Next, we select important features that have a higher influence on the classifier using a recursive feature elimination method. Finally, we apply the CNN-LSTM model for predicting faulty RWMR devices. We also propose an efficient training method for ML models to learn the unbalanced real-world AMI dataset. A cost-effective threshold for evaluating the performance of ML models is proposed by considering the mispredictions of ML models as well as the cost. Our experimental results show that an F-measure of 0.82 and MCC of 0.83 are obtained when the CNN-LSTM model is used for prediction. Full article

(This article belongs to the Special Issue Sensing for In-Process Monitoring of High-Value Manufacturing Processes)

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