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Applied Sciences
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Advanced Applications of Industrial Informatic Technologies
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Advanced Applications of Industrial Informatic Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 20736

Special Issue Editor

Dr. Albert Smalcerz

E-Mail Website
Guest Editor
Department of Industrial Informatics, Faculty of Materials Science, Silesian University of Technology, Krasinskiego 8, 40-019 Katowice, Poland
Interests: industrial informatics; induction heating; electromagnetic field; numerical simulation; optimization; electromagnetic fields; alloys; electromagnetics; computational electromagnetics; electromagnetic engineering; refining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid technological changes are taking place today in modern industry. The Industry 4.0 revolution is based mainly on advanced information technology processes.

This Special Issue of the journal aims to support the dissemination of the latest research in the area of advanced information technologies and their implementation in practice. Original papers related, for instance, to production monitoring and control systems, the modelling and simulation of advanced technological processes, industrial data analyses, and the application of artificial intelligence methods in industry are kindly welcome. Both experimental and numerical approaches will be accepted.

Dr. Albert Smalcerz
Guest Editor

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. Applied Sciences 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 2400 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

  • Computer monitoring and production control
  • Computer modelling, simulation and the optimization of technological processes
  • Industrial data analysis
  • The design of industrial IT systems, including real-time systems and embedded systems
  • The use of artificial intelligence in industry, including expert systems
  • Designing the devices used in industry, including firmware
  • 3D scanning
  • The technologies of Industry 4.0

Published Papers (7 papers)

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Research

17 pages, 2780 KiB  
Article
Improving the Consistency of the Failure Mode Effect Analysis (FMEA) Documents in Semiconductor Manufacturing
by Houssam Razouk and Roman Kern
Appl. Sci. 2022, 12(4), 1840; https://doi.org/10.3390/app12041840 - 10 Feb 2022
Cited by 6 | Viewed by 4032
Abstract
Digitalization of causal domain knowledge is crucial. Especially since the inclusion of causal domain knowledge in the data analysis processes helps to avoid biased results. To extract such knowledge, the Failure Mode Effect Analysis (FMEA) documents represent a valuable data source. Originally, FMEA [...] Read more.
Digitalization of causal domain knowledge is crucial. Especially since the inclusion of causal domain knowledge in the data analysis processes helps to avoid biased results. To extract such knowledge, the Failure Mode Effect Analysis (FMEA) documents represent a valuable data source. Originally, FMEA documents were designed to be exclusively produced and interpreted by human domain experts. As a consequence, these documents often suffer from data consistency issues. This paper argues that due to the transitive perception of the causal relations, discordant and merged information cases are likely to occur. Thus, we propose to improve the consistency of FMEA documents as a step towards more efficient use of causal domain knowledge. In contrast to other work, this paper focuses on the consistency of causal relations expressed in the FMEA documents. To this end, based on an explicit scheme of types of inconsistencies derived from the causal perspective, novel methods to enhance the data quality in FMEA documents are presented. Data quality improvement will significantly improve downstream tasks, such as root cause analysis and automatic process control. Full article
(This article belongs to the Special Issue Advanced Applications of Industrial Informatic Technologies)
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28 pages, 1782 KiB  
Article
Need-Based and Optimized Health Insurance Package Using Clustering Algorithm
by Irum Matloob, Shoab Ahmad Khan, Farhan Hussain, Wasi Haider Butt, Rukaiya Rukaiya and Fatima Khalique
Appl. Sci. 2021, 11(18), 8478; https://doi.org/10.3390/app11188478 - 13 Sep 2021
Cited by 6 | Viewed by 2411
Abstract
The paper presents a novel methodology based on machine learning to optimize medical benefits in healthcare settings, i.e., corporate, private, public or statutory. The optimization is applied to design healthcare insurance packages based on the employee healthcare record. Moreover, with the advancement in [...] Read more.
The paper presents a novel methodology based on machine learning to optimize medical benefits in healthcare settings, i.e., corporate, private, public or statutory. The optimization is applied to design healthcare insurance packages based on the employee healthcare record. Moreover, with the advancement in the insurance industry, it is rapidly adapting mathematical and machine learning models to enhance insurance services like funds prediction, customer management and get better revenue from their businesses. However, conventional computing insurance packages and premium methods are time-consuming, designation specific, and not cost-effective. During the design of insurance packages, an employee’s needs should be given more importance than his/her designation or position in an organization. The design of insurance packages in healthcare is a non-trivial task due to the employees’ changing healthcare needs; therefore, using the proposed technique employees can be moved from their existing package to another depending upon his/her need. This provides the motivation to propose a methodology in which we applied machine learning concepts for designing need-based health insurance packages rather than professional tagging. By the design of need-based packages, medical benefit optimization which is the core goal of our proposed methodology is effectively achieved. Our proposed methodology derives insurance packages that are need-based and optimal based on our defined criteria. We achieved this by first applying the clustering technique to historical medical records. Subsequently, medical benefit optimization is achieved from these packages by applying a probability distribution model on five years employees’ insurance records. The designed technique is validated on real employees’ insurance records from a large enterprise.The proposed design provides 25% optimization on medical benefit amount compared to current medical benefits amount therefore, gives better healthcare to all the employees. Full article
(This article belongs to the Special Issue Advanced Applications of Industrial Informatic Technologies)
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13 pages, 8221 KiB  
Article
Study on the Digital Intelligent Diagnosis of Miniature Machine Tools
by Tzu-Chi Chan, Ze-Kai Jian and Yu-Chuan Wang
Appl. Sci. 2021, 11(18), 8372; https://doi.org/10.3390/app11188372 - 9 Sep 2021
Cited by 4 | Viewed by 1435
Abstract
Several industries are currently focusing on smart technologies, high customization, and the integration of solutions. This study focuses on the intelligent diagnosis of digital small machine tools. Furthermore, the main technology processes and cases for smart manufacturing for machine tool applications are introduced. [...] Read more.
Several industries are currently focusing on smart technologies, high customization, and the integration of solutions. This study focuses on the intelligent diagnosis of digital small machine tools. Furthermore, the main technology processes and cases for smart manufacturing for machine tool applications are introduced. Owing to the requirements of automated processing to determine the quality of a process in advance, the health status of a machine should be monitored in real time, and machine abnormalities should be detected periodically. In this study, we captured the real-time signals of temperature, spindle current, and the vibration of three small five-axis machine tools. Moreover, we used a principal component analysis to diagnose and compare the health status of the spindles and machines. We developed a miniature machine tool health monitoring application to avoid time delays and loss from damage, and used the application to monitor the machine health online under an actual application. Therefore, the technology can also be used in an online diagnosis of machine tools through modeling technology, allowing the user to monitor trends in the machine health. This research provides a feasible method for monitoring machine health. We believe that the intelligent functions of machine tools will continue to increase in the future. Full article
(This article belongs to the Special Issue Advanced Applications of Industrial Informatic Technologies)
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12 pages, 2574 KiB  
Article
Industry 4.0 HUB: A Collaborative Knowledge Transfer Platform for Small and Medium-Sized Enterprises
by Alberto Cotrino, Miguel A. Sebastián and Cristina González-Gaya
Appl. Sci. 2021, 11(12), 5548; https://doi.org/10.3390/app11125548 - 15 Jun 2021
Cited by 12 | Viewed by 2800
Abstract
Industry 4.0 brings opportunities for small- and medium-sized enterprises (SMEs), but SMEs are lacking Industry 4.0 knowledge, and this might result in a challenge to support SMEs’ competitiveness and productivity. During recent years, the European Commission and some government initiatives have been fostering [...] Read more.
Industry 4.0 brings opportunities for small- and medium-sized enterprises (SMEs), but SMEs are lacking Industry 4.0 knowledge, and this might result in a challenge to support SMEs’ competitiveness and productivity. During recent years, the European Commission and some government initiatives have been fostering the transition toward Industry 4.0 for SMEs through the creation of Digital Innovation Hubs, the Plattform Industrie 4.0, and some other initiatives. Nonetheless, the authors consider that the lack of knowledge is still a risk toward Industry 4.0 transformation for SMEs. New ways to improve Industry 4.0 knowledge management and especially the knowledge transfer must be developed. When SMEs start the transition to Industry 4.0, first of all, they do not want to start from scratch, and secondly, it can be easy to get lost in the multitude of technologies and tools that are available in today’s market. There is a gap in which to provide a collaborative Industry 4.0 knowledge transfer platform or hub designed for SMEs. Therefore, this research aims to enhance Industry 4.0 knowledge transfer through the development of a collaborative, web-based knowledge transfer Industry 4.0 platform. The outcome of this research is a developed platform that will be referred to as Industry 4.0 HUB. Full article
(This article belongs to the Special Issue Advanced Applications of Industrial Informatic Technologies)
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20 pages, 1575 KiB  
Article
Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
by Cosmas Ifeanyi Nwakanma, Fabliha Bushra Islam, Mareska Pratiwi Maharani, Jae-Min Lee and Dong-Seong Kim
Appl. Sci. 2021, 11(8), 3662; https://doi.org/10.3390/app11083662 - 19 Apr 2021
Cited by 28 | Viewed by 3804
Abstract
Factory shop floor workers are exposed to threats and accidents due to their encounters with tools, equipment, and toxic materials. There are cases of occupational accidents resulting in injuries to workers and precipitating lawsuits, which on the other hand affect company’s operational cost. [...] Read more.
Factory shop floor workers are exposed to threats and accidents due to their encounters with tools, equipment, and toxic materials. There are cases of occupational accidents resulting in injuries to workers and precipitating lawsuits, which on the other hand affect company’s operational cost. To ensure the safety of workers within the shop floor, there is a need for proactive activity monitoring. Such activities include detection of falling objects, abnormal vibration, and movement of humans within an acceptable area of the factory floor. Breathing sensor-based monitoring of workers in the smart factory shop floor can also be implemented. This is for the detection of human activity, especially in cases where workers are in isolation with no available emergency assistance. Internet of Things (IoT), Industrial Internet of Things (IIoT), and machine learning (ML) have enabled so many possibilities in this area. In this study, we present a simple test-bed, which is made up of a vibration sensor, a breathing and movement sensor, and a Light Detection and Ranging (LIDAR) sensor. These sensors were used to gather normal and abnormal data of human activities at the factory. We developed a dataset based on possible real-life situations and it is made up of about 10,000 data points. The data was split with a ratio of 75:25 for training and testing the model. We investigated the performance of different ML algorithms, including support vector machine (SVM), linear regression, naive Bayes (NB), K-nearest neighbor (KNN), and convolutional neural network (CNN). From our experiments, the CNN model outperformed other algorithms with an accuracy of 99.45%, 99.78%,100%, and 100%, respectively, for vibration, movement, breathing, and distance. We have also successfully developed a dataset to assist the research community in this field. Full article
(This article belongs to the Special Issue Advanced Applications of Industrial Informatic Technologies)
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23 pages, 10289 KiB  
Article
Identifying the Primary Odor Perception Descriptors by Multi-Output Linear Regression Models
by Xin Li, Dehan Luo, Yu Cheng, Kin-Yeung Wong and Kevin Hung
Appl. Sci. 2021, 11(8), 3320; https://doi.org/10.3390/app11083320 - 7 Apr 2021
Cited by 6 | Viewed by 1918
Abstract
Semantic odor perception descriptors, such as “sweet”, are widely used for product quality assessment in food, beverage, and fragrance industries to profile the odor perceptions. The current literature focuses on developing as many as possible odor perception descriptors. A large number of odor [...] Read more.
Semantic odor perception descriptors, such as “sweet”, are widely used for product quality assessment in food, beverage, and fragrance industries to profile the odor perceptions. The current literature focuses on developing as many as possible odor perception descriptors. A large number of odor descriptors poses challenges for odor sensory assessment. In this paper, we propose the task of narrowing down the number of odor perception descriptors. To this end, we contrive a novel selection mechanism based on machine learning to identify the primary odor perceptual descriptors (POPDs). The perceptual ratings of non-primary odor perception descriptors (NPOPDs) could be predicted precisely from those of the POPDs. Therefore, the NPOPDs are redundant and could be disregarded from the odor vocabulary. The experimental results indicate that dozens of odor perceptual descriptors are redundant. It is also observed that the sparsity of the data has a negative correlation coefficient with the model performance, while the Pearson correlation between odor perceptions plays an active role. Reducing the odor vocabulary size could simplify the odor sensory assessment and is auxiliary to understand human odor perceptual space. Full article
(This article belongs to the Special Issue Advanced Applications of Industrial Informatic Technologies)
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22 pages, 8165 KiB  
Article
Detection and Classification of Bearing Surface Defects Based on Machine Vision
by Manhuai Lu and Chin-Ling Chen
Appl. Sci. 2021, 11(4), 1825; https://doi.org/10.3390/app11041825 - 18 Feb 2021
Cited by 12 | Viewed by 3349
Abstract
Surface defects on bearings can directly affect the service life and reduce the performance of equipment. At present, the detection of bearing surface defects is mostly done manually, which is labor-intensive and results in poor stability. To improve the inspection speed and the [...] Read more.
Surface defects on bearings can directly affect the service life and reduce the performance of equipment. At present, the detection of bearing surface defects is mostly done manually, which is labor-intensive and results in poor stability. To improve the inspection speed and the defect recognition rate, we proposed a bearing surface defect detection and classification method using machine vision technology. The method makes two main contributions. It proposes a local multi-neural network (Lc-MNN) image segmentation algorithm with the wavelet transform as the classification feature. The precision segmentation of the defect image is accomplished in three steps: wavelet feature extraction, Lc-MNN region division, and Lc-MNN classification. It also proposes a feature selection algorithm (SCV) that makes comprehensive use of scalar feature selection, correlation analysis, and vector feature selection to first remove similar features through correlation analysis, further screen the results with a scalar feature selection algorithm, and finally select the classification features using a feature vector selection algorithm. Using 600 test samples with three types of defect in the experiment, an identification rate of 99.5% was achieved without the need for large-scale calculation. The comparison tests indicated that the proposed method can achieve efficient feature selection and defect classification. Full article
(This article belongs to the Special Issue Advanced Applications of Industrial Informatic Technologies)
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