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Advanced Sensing Technology and Data Analytics in Smart Manufacturing

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 59865

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Special Issue Editors

Dr. Chao Liu

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Guest Editor

College of Engineering and Physical Sciences, Aston University, Birmingham B47ET, UK
Interests: smart manufacturing; Industry 4.0; digital twin; cyber-physical production system; advanced data analytics; machine tool; AR
Special Issues, Collections and Topics in MDPI journals

Dr. Pai Zheng

E-Mail Website
Guest Editor

Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: intelligent product design; intelligent product manufacturing; multi-objective optimization; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Tao Peng

E-Mail Website
Guest Editor

State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
Interests: smart technologies for manufacturing and services; big data-driven production management; cognitive intelligence-enabled design; manufacturing and supply chains
Special Issues, Collections and Topics in MDPI journals

Dr. Xi Wang

E-Mail Website
Guest Editor

Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
Interests: cloud-based manufacturing; sustainable manufacturing; robotics; digital twins; computer-aided design; manufacturing systems

Prof. Dr. Yuchun Xu

E-Mail Website
Guest Editor

School of Engineering and Technology, Aston University, Birmingham B4 7ET, UK
Interests: smart and sustainable manufacturing; life cycle engineering and optimisation; digital product development and manufacturing; cost modelling & engineering economic analysis; circular economy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traditional manufacturing is undergoing a dramatic evolution towards smart manufacturing, which deeply integrates various emerging smart technologies (IoT, CPS, digital twin, cloud/edge computing, AI, AR, etc.) into manufacturing equipment and production processes. Smart manufacturing aims to orchestrate both physical and digital processes within factories and across the supply chain, accelerate the development process, improve productivity and efficiency, increase transparency, reduce cost and waste, and hence meet the increasing demands of mass customisation. With the rapid advancements in information and communications technology, huge amounts of manufacturing data have been made available and accessible. However, taking full advantage of these manufacturing data becomes a critical challenge. Fortunately, advanced data analytics has shown great potential in addressing this challenge. Broadly, advanced data analytics refers to various types of sophisticated data analysis methods, techniques, and tools, including data mining, machine learning, deep learning, visualization, semantic analysis, computer vision, etc. In smart manufacturing, advanced data analytics has the potential to obtain deeper insights into the manufacturing data in order to achieve the prediction and optimization of manufacturing and production processes, and hence plays a vital role in decision making across multiple levels of manufacturing systems.

This Special Issue aims to publish original and visionary research works and review articles on advanced data analytics in smart manufacturing, including theoretical methods and algorithms, conceptual models, technologies, case studies, and industrial applications. Topics to be covered include, but are not limited to, the following:

Advanced sensing technology for smart manufacturing
Advanced data analytics for smart manufacturing;
Intelligent decision making in manufacturing;
Machine learning and deep learning in manufacturing;
Intelligent prognostics and health management in manufacturing;
Digitalization and servitisation of manufacturing systems;
Digital twins in smart manufacturing;
Intelligent human–machine collaboration in manufacturing.

Dr. Chao Liu
Dr. Pai Zheng
Dr. Tao Peng
Dr. Xi Wang
Prof. Dr. Yuchun Xu
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

smart manufacturing
advanced data analytics
manufacturing systems
machine learning
deep learning
digital twins
prediction
optimization

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Published Papers (13 papers)

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21 pages, 1752 KiB

Open AccessArticle

Semi-Supervised Deep Kernel Active Learning for Material Removal Rate Prediction in Chemical Mechanical Planarization

by Chunpu Lv, Jingwei Huang, Ming Zhang, Huangang Wang and Tao Zhang

Sensors 2023, 23(9), 4392; https://doi.org/10.3390/s23094392 - 29 Apr 2023

Cited by 3 | Viewed by 2054

Abstract

The material removal rate (MRR) is an important variable but difficult to measure in the chemical–mechanical planarization (CMP) process. Most data-based virtual metrology (VM) methods ignore the large number of unlabeled samples, resulting in a waste of information. In this paper, the semi-supervised deep kernel active learning (SSDKAL) model is proposed. Clustering-based phase partition and phase-matching algorithms are used for the initial feature extraction, and a deep network is used to replace the kernel of Gaussian process regression so as to extract hidden deep features. Semi-supervised regression and active learning sample selection strategies are applied to make full use of information on the unlabeled samples. The experimental results of the CMP process dataset validate the effectiveness of the proposed method. Compared with supervised regression and co-training-based semi-supervised regression algorithms, the proposed model has a lower mean square error with different labeled sample proportions. Compared with other frameworks proposed in the literature, such as physics-based VM models, Gaussian-process-based regression models, and stacking models, the proposed method achieves better prediction results without using all the labeled samples. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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23 pages, 3550 KiB

Open AccessArticle

A Cost–Benefit Analysis Simulation for the Digitalisation of Cold Supply Chains

by Oliver Schiffmann, Ben Hicks, Aydin Nassehi, James Gopsill and Maria Valero

Sensors 2023, 23(8), 4147; https://doi.org/10.3390/s23084147 - 20 Apr 2023

Cited by 5 | Viewed by 12103

Abstract

This paper investigates using simulation to predict the benefits and costs of digitalising cold distribution chains. The study focuses on the distribution of refrigerated beef in the UK, where digitalisation was implemented to re-route cargo carriers. By comparing simulations of both digitalised and non-digitalised supply chains, the study found that digitalisation can reduce beef waste and decrease the number of miles driven per successful delivery, leading to potential cost savings. Note that this work is not attempting to prove that digitalisation is appropriate for the chosen scenario, only to justify a simulation approach as a decision making tool. The proposed modelling approach provides decision-makers with more accurate predictions of the cost–benefit of increased sensorisation in supply chains. By accounting for stochastic and variable parameters, such as weather and demand fluctuations, simulation can be used to identify potential challenges and estimate the economic benefits of digitalisation. Moreover, qualitative assessments of the impact on customer satisfaction and product quality can help decision-makers consider the broader impacts of digitalisation. Overall, the study suggests that simulation can play a crucial role in facilitating informed decisions about the implementation of digital technologies in the food supply chain. By providing a better understanding of the potential costs and benefits of digitalisation, simulation can help organisations make more strategic and effective decisions. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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15 pages, 2350 KiB

Open AccessArticle

Analysis of Training Deep Learning Models for PCB Defect Detection

by Joon-Hyung Park, Yeong-Seok Kim, Hwi Seo and Yeong-Jun Cho

Sensors 2023, 23(5), 2766; https://doi.org/10.3390/s23052766 - 2 Mar 2023

Cited by 37 | Viewed by 10821

Abstract

Recently, many companies have introduced automated defect detection methods for defect-free PCB manufacturing. In particular, deep learning-based image understanding methods are very widely used. In this study, we present an analysis of training deep learning models to perform PCB defect detection stably. To this end, we first summarize the characteristics of industrial images, such as PCB images. Then, the factors that can cause changes (contamination and quality degradation) to the image data in the industrial field are analyzed. Subsequently, we organize defect detection methods that can be applied according to the situation and purpose of PCB defect detection. In addition, we review the characteristics of each method in detail. Our experimental results demonstrated the impact of various degradation factors, such as defect detection methods, data quality, and image contamination. Based on our overview of PCB defect detection and experiment results, we present knowledge and guidelines for correct PCB defect detection. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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28 pages, 1661 KiB

Open AccessArticle

A Novel Simulated Annealing-Based Hyper-Heuristic Algorithm for Stochastic Parallel Disassembly Line Balancing in Smart Remanufacturing

by Youxi Hu, Chao Liu, Ming Zhang, Yu Jia and Yuchun Xu

Sensors 2023, 23(3), 1652; https://doi.org/10.3390/s23031652 - 2 Feb 2023

Cited by 16 | Viewed by 3177

Abstract

Remanufacturing prolongs the life cycle and increases the residual value of various end-of-life (EoL) products. As an inevitable process in remanufacturing, disassembly plays an essential role in retrieving the high-value and useable components of EoL products. To disassemble massive quantities and multi-types of EoL products, disassembly lines are introduced to improve the cost-effectiveness and efficiency of the disassembly processes. In this context, disassembly line balancing problem (DLBP) becomes a critical challenge that determines the overall performance of disassembly lines. Currently, the DLBP is mostly studied in straight disassembly lines using single-objective optimization methods, which cannot represent the actual disassembly environment. Therefore, in this paper, we extend the mathematical model of the basic DLBP to stochastic parallel complete disassembly line balancing problem (DLBP-SP). A novel simulated annealing-based hyper-heuristic algorithm (HH) is proposed for multi-objective optimization of the DLBP-SP, considering the number of workstations, working load index, and profits. The feasibility, superiority, stability, and robustness of the proposed HH algorithm are validated through computational experiments, including a set of comparison experiments and a case study of gearboxes disassembly. To the best of our knowledge, this research is the first to introduce gearboxes as a case study in DLBP which enriches the research on disassembly of industrial equipment. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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58 pages, 3551 KiB

Open AccessArticle

DSF Core: Integrated Decision Support for Optimal Scheduling of Lifetime Extension Strategies for Industrial Equipment

by Nikolaos Kolokas, Dimosthenis Ioannidis and Dimitrios Tzovaras

Sensors 2023, 23(3), 1332; https://doi.org/10.3390/s23031332 - 25 Jan 2023

Viewed by 2232

Abstract

This paper proposes a generic algorithm for industries with degrading and/or failing equipment with significant consequences. Based on the specifications and the real-time status of the production line, the algorithm provides decision support to machinery operators and manufacturers about the appropriate lifetime extension strategies to apply, the optimal time-frame for the implementation of each and the relevant machine components. The relevant recommendations of the algorithm are selected by comparing smartly chosen alternatives after simulation-based life cycle evaluation of Key Performance Indicators (KPIs), considering the short-term and long-term impact of decisions on these economic and environmental KPIs. This algorithm requires various inputs, some of which may be calculated by third-party algorithms, so it may be viewed as the ultimate algorithm of an overall Decision Support Framework (DSF). Thus, it is called “DSF Core”. The algorithm was applied successfully to three heterogeneous industrial pilots. The results indicate that compared to the lightest possible corrective strategy application policy, following the optimal preventive strategy application policy proposed by this algorithm can reduce the KPI penalties due to stops (i.e., failures and strategies) and production inefficiency by 30–40%. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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28 pages, 1140 KiB

Open AccessArticle

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

by Joel Lehmann, Stefan Schorz, Alessa Rache, Tim Häußermann, Matthias Rädle and Julian Reichwald

Sensors 2023, 23(1), 468; https://doi.org/10.3390/s23010468 - 1 Jan 2023

Cited by 15 | Viewed by 4193

Abstract

One of the main topics within research activities is the management of research data. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally be managed by Findable, Accessible, Interoperable, and Reusable (FAIR) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and digital twin technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management. This paper contributes a novel architectural approach for gathering and managing research data aligned with the FAIR principles. A reference implementation as well as a subsequent proof of concept is given, leveraging the utilization of digital twins to overcome common data management issues at equipment-intense research institutes. To facilitate implementation, a top-level knowledge graph has been developed to convey metadata from research devices along with the produced data. In addition, a reactive digital twin implementation of a specific measurement device was devised to facilitate reconfigurability and minimized design effort. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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15 pages, 5134 KiB

Open AccessArticle

Enhanced Frequency Stability of SAW Yarn Tension Sensor by Using the Dual Differential Channel Surface Acoustic Wave Oscillator

by Yang Feng, Wenbo Liu and Ben Wang

Sensors 2023, 23(1), 464; https://doi.org/10.3390/s23010464 - 1 Jan 2023

Cited by 9 | Viewed by 2191

Abstract

This paper presents a 60 MHz surface acoustic wave (SAW) yarn tension sensor incorporating a novel SAW oscillator with high-frequency stability. A SAW delay line was fabricated on ST-X quartz substrate using the unbalanced-split electrode and bi-directional engraving slots. The dual differential channel delay linear acoustic surface wave oscillator is designed and implemented to test yarn tension, which can effectively remove the interference of temperature, humidity, and other peripheral factors through differential design. The yarn tension sensor using the surface acoustic wave has high-precision characteristics, and the SAW delay line oscillator is designed to ensure the test system’s stable operation. The effect of time and tension on oscillator frequency stability is studied in detail, and the single oscillator and the dual differential channel system were tested, respectively. After using the dual differential channel system, the short-term frequency stability from is reduced from

1.0163 ppm

0.17726 ppm

, the frequency accuracy of the tension sensor is improved from 134 Hz to 27 Hz, and the max frequency jump steady is reduced from

2.2395 ppm

0.45123 ppm

. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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16 pages, 7665 KiB

Open AccessArticle

Adaptive Bidirectional Gray-Scale Center of Gravity Extraction Algorithm of Laser Stripes

by Miaomiao Zhang, Zhengnan Li, Fuquan Zhang and Lidong Ma

Sensors 2022, 22(24), 9567; https://doi.org/10.3390/s22249567 - 7 Dec 2022

Cited by 5 | Viewed by 2569

Abstract

Aiming at the realization of fast and high-precision detection of the workpiece, an adaptive bidirectional gray-scale center of gravity extraction algorithm for laser stripes is proposed in this paper. The algorithm is processed in the following steps. Firstly, the initial image processing area is set according to the floating field of the camera’s light stripe, followed by setting the adaptive image processing area according to the actual position of the light stripe. Secondly, the center of light stripe is obtained by using the method of combining the upper contour with the barycenter of the bidirectional gray-scale. The obtained center of the light stripe is optimized by reducing the deviation from adjacent center points. Finally, the slope and intercept are used to complete the breakpoint. The experimental results show that the algorithm has the advantages of high speed and precision and has specific adaptability to the laser stripes of the complex environment. Compared with other conventional algorithms, it greatly improves and can be used in industrial detection. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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11 pages, 13938 KiB

Open AccessArticle

Fast Inline Microscopic Computational Imaging

by Laurin Ginner, Simon Breuss and Lukas Traxler

Sensors 2022, 22(18), 7038; https://doi.org/10.3390/s22187038 - 17 Sep 2022

Cited by 1 | Viewed by 2401

Abstract

Inline inspection is becoming an essential tool for industrial high-quality production. Unfortunately, the desired acquisition speeds and needs for high-precision imaging are often at the limit of what is physically possible, such as a large field of view at a high spatial resolution. In this paper, a novel light-field and photometry system is presented that addresses this trade off by combining microscopic imaging with special projection optics to generate a parallax effect. This inline microscopic system, together with an image processing pipeline, delivers high-resolution 3D images at high speeds, by using a lateral transport stage changing the optical perspective. Scanning speeds of up to 12 mm/s can be achieved at a depth resolution of 2.8 μm and a lateral sampling of 700 nm/pixel, suitable for inspection in high-quality manufacturing industry. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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15 pages, 3127 KiB

Open AccessArticle

Thermal Error Prediction and Compensation of Digital Twin Laser Cutting Based on T-XGBoost

by Chang Lu, Jiyou Fei, Xiangzhong Meng, Yanshu Li and Zhibo Liu

Sensors 2022, 22(18), 7022; https://doi.org/10.3390/s22187022 - 16 Sep 2022

Cited by 10 | Viewed by 2238

Abstract

Laser cutting belongs to non-contact processing, which is different from traditional turning and milling. In order to improve the machining accuracy of laser cutting, a thermal error prediction and dynamic compensation strategy for laser cutting is proposed. Based on the time-varying characteristics of the digital twin technology, a hybrid model combining the thermal elastic–plastic finite element (TEP-FEM) and T-XGBoost algorithms is established. The temperature field and thermal deformation under 12 common working conditions are simulated and analyzed with TEP-FEM. Real-time machining data obtained from TEP-FEM simulation is used in intelligent algorithms. Based on the XGBoost algorithm and the simulation data set as the training data set, a time-series-based segmentation algorithm (T-XGBoost) is proposed. This algorithm can reduce the maximum deformation at the slit by more than 45%. At the same time, by reducing the average volume strain under most working conditions, the lifting rate can reach 63% at the highest, and the machining result is obviously better than XGBoost. The strategy resolves the uncontrollable thermal deformation during cutting and provides theoretical solutions to the implementation of the intelligent operation strategies such as predictive machining and quality monitoring. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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22 pages, 12580 KiB

Open AccessArticle

An Augmented Reality-Assisted Prognostics and Health Management System Based on Deep Learning for IoT-Enabled Manufacturing

by Liping Wang, Dunbing Tang, Changchun Liu, Qingwei Nie, Zhen Wang and Linqi Zhang

Sensors 2022, 22(17), 6472; https://doi.org/10.3390/s22176472 - 28 Aug 2022

Cited by 14 | Viewed by 3150

Abstract

With increasingly advanced Internet of Things (IoT) technology, the composition of workshop equipment has become more and more complex. Based on this, the rate of system performance degradation and the probability of fault have both increased. Owing to this, not only has the difficulty of constructing the remaining useful life (RUL) model increased but also the improvement in speed of maintenance personnel cannot keep up with the speed of equipment replacement. Therefore, an augmented reality (AR)-assisted prognostics and health management system based on deep learning for IoT-enabled manufacturing is proposed in this paper. Firstly, the feature extraction model based on Convolutional Neural Network-Particle Swarm Optimization (PSO-CNN) is proposed with the purpose of excavating the internal associations in large amounts of production data. Based on this, the high-accuracy RUL prediction is accomplished by Gate Recurrent Unit (GRU)-attention, which can capture the long-term and short-term dependencies of time series and successfully solve the gradient disappearance problem of RNN. Moreover, more attention will be paid to important content with the help of the attention mechanism. Additionally, high-efficiency maintenance guidance and visible instructions can be accomplished by AR. On top of this, the remote expert can offer help when maintenance personnel encounters tough problems. Finally, a real case was implemented in a typical IoT-enabled workshop, which validated the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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20 pages, 9228 KiB

Open AccessArticle

Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers

by Chen Li, Shijie Bian, Tongzi Wu, Richard P. Donovan and Bingbing Li

Sensors 2022, 22(16), 6246; https://doi.org/10.3390/s22166246 - 19 Aug 2022

Cited by 12 | Viewed by 3370

Abstract

With the rapid concurrent advance of artificial intelligence (AI) and Internet of Things (IoT) technology, manufacturing environments are being upgraded or equipped with a smart and connected infrastructure that empowers workers and supervisors to optimize manufacturing workflow and processes for improved energy efficiency, equipment reliability, quality, safety, and productivity. This challenges capital cost and complexity for many small and medium-sized manufacturers (SMMs) who heavily rely on people to supervise manufacturing processes and facilities. This research aims to create an affordable, scalable, accessible, and portable (ASAP) solution to automate the supervision of manufacturing processes. The proposed approach seeks to reduce the cost and complexity of smart manufacturing deployment for SMMs through the deployment of consumer-grade electronics and a novel AI development methodology. The proposed system, AI-assisted Machine Supervision (AIMS), provides SMMs with two major subsystems: direct machine monitoring (DMM) and human-machine interaction monitoring (HIM). The AIMS system was evaluated and validated with a case study in 3D printing through the affordable AI accelerator solution of the vision processing unit (VPU). Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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Review

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16 pages, 1951 KiB

Open AccessReview

A Comprehensive Review of Blockchain Technology-Enabled Smart Manufacturing: A Framework, Challenges and Future Research Directions

by Xin Guo, Geng Zhang and Yingfeng Zhang

Sensors 2023, 23(1), 155; https://doi.org/10.3390/s23010155 - 23 Dec 2022

Cited by 35 | Viewed by 7259

Abstract

As a new generation of information technology, blockchain plays an important role in business and industrial innovation. The employment of blockchain technologies in industry has increased transparency, security and traceability, improved efficiency, and reduced costs of production activities. Many studies on blockchain technology-enabled system construction and performance optimization in Industry 4.0 have been carried out. However, blockchain technology and smart manufacturing have been individually researched in academia and industry, according to the literature. This survey aims to summarize the existing research to provide theoretical foundations for applying blockchain technology to smart manufacturing, thus creating a more reliable and authentic smart manufacturing system. In this regard, the literature related to four types of critical issues in smart manufacturing is introduced: data security, data sharing, trust mechanisms and system coordination issues. The corresponding blockchain solutions were reviewed and analyzed. Based on the insights obtained from the above analysis, a reference framework for blockchain technology-enabled smart manufacturing systems is put forward. The challenges and future research directions are also discussed to provide potential guides for achieving better utilization of this technology in smart manufacturing. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics in Smart Manufacturing)

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