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The vision of smart factory is based on the notion of Industry 4.0 that denotes technologies and concepts related to cyber-physical systems and the Internet of Things (IoT). In smart factories cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the IoT, cyber-physical...
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The report explored the problem of digital testing of a robotic agricultural vehicle. The approach to representing the robotic chassis as a cyber-physical system is to build a complex of digital models of units and built-in measuring tools for monitoring physical processes. Based on a model-oriented approach, a digital twin of a robotic chassis for...
The term ‘Industry 4.0’ was coined to mark the fourth industrial revolution, a new paradigm enabled by the introduction of the Internet of Things (IoT) into the production and manufacturing environment. The vision of Industry 4.0 emphasizes the global networks of machines in a smart factory setting capable of autonomously exchanging information and...
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... Physical integration, application integration, and business integration are the three layers of integration. The procedure commences with machine integration and progresses to business-wide process integration [39,40]. The manufacturing system is the combination of materials and machines i.e., i) operation overview, ii) level of automation, and iii) flexibility [34]. ...
Manufacturing processes combine an array of procedures and machinery that operate in unison to convert raw ingredients into tangible goods. The methods used for the process have an impact on the entire structure of the production line as well as the effectiveness of the goods. Manufacturing is not a recent invention; it has origins in antiquity. The Australian manufacturing industry has several challenges due to the complexity of manufacturing resulting in a decline in the manufacturing economy. Additionally, Australian manufacturing output has dropped continuously over the last few years due to quality flaws. While countries are adopting Industry 4.0 (I4.0), smart sensors, IoT (Internet of Things), and cognitive sensing have become more eminent for quality enhancement. The use of vision systems comes into play to analyse the errors and prevent catastrophic failures. It is essentially a combination of sensors, cameras, and computer-based neural network techniques to analyse the data points and provide feedback, along with insights into the errors and mitigate those issues. It is essential to eliminate the problems without wasting resources and damaging the environment. This paper will go in-depth into distinctive vision technologies and neural network techniques to combine them in the loop for accurate visual interpretation of data. These vision technologies potentially be used to eliminate errors and promote production efficiency, A case study on cable manufacturing has been taken into consideration for the validation of technological aspects and implementation plan for vision systems.
... AGVs' autonomy was also aimed by some papers which proposed a graph-based analytical system with autonomous robots (Zug et al., 2015), the development of a CPSbased approach (Andersen et al., 2017;Lee et al., 2018); a DT-based mobile manipulator (Alhama Blanco et al. 2018) an open platform for AGV with Digital Twin (Seder et al., 2019), a Smart Factory based on the RoboCup Logistics League setup (Eltester et al., 2020) and on communication and computing layers (Tang et al., 2016), the use of behavior trees and Reinforcement Learning (Hu et al. 2020). These papers claim that their proposed systems had a successful implementation and demonstrated their viability. ...
Industry 4.0 (I4.0) brings a set of challenges that need to be settled to enable the expected industrial revolution. Cyber-Physical Systems (CPS) and Autonomous Robots are two of mainstream I4.0 technologies and can be combined with other Enabling Technologies to overcome main I4.0 challenges. This Systematic Literature Review (SLR) aimed to understand how these Enabling Technologies were combined with CPS and Autonomous Robots to achieve fully featured I4.0 solutions. The 5C is a CPS architecture which was used as a measurement to CPS compliance. After filtering and processing 15,144 papers from six different academic search engines, it was found that full 5C compliant solutions are rare to find; that not all enabling technologies were integrated; and most of existing research focused in implementing practical solutions, but comparison to conventional approaches is scarce. We conclude that there is a need for further research to accomplish full 5C I4.0.
... The general term refers to machines equipped with data-gathering devices that can interface with other machines and systems to achieve specific goals. Smart manufacturing, which focuses on integration, interoperability, and bridging the gap between the physical and virtual worlds, has gained significant attention in recent studies (Chen et al., 2008;Tang et al., 2016). ...
... It is a combination of technologies to create a connection between the digital, physical, and biological spheres. The amalgamation of a variety of technologies, such as Artificial Intelligence (AI) [1], Internet of Things (IoT) [2], [3], Cyber-Physical Systems (CPS) [4], big data [5], [6], etc., are utilized to establish automation and data exchange. Fig. 1. represents the revolution in healthcare. ...
Digital Twin (DT) in Healthcare 4.0 (H4.0) presents a digital model of the patient with all its biological properties and characteristics. One of the application areas is patient respiration monitoring for enhanced patient care and decision support to healthcare professionals. Obtrusive methods of patient monitoring create hindrances in the patient’s daily routine. This research presents a novel DT model (ResDT) based on Wi-Fi Carrier State Information (CSI), improved signal processing, and Machine Learning (ML) algorithms for monitoring and classification (binary and multi-class) of patient respiration. A Wi-Fi sensor ESP32 with Wi-Fi CSI was utilized for the collection of respiration data. This provides an added advantage of unobtrusive monitoring of patient vital signs. The Patient’s Breaths Per Minute (BPM) is estimated from raw sensor data through the integration of multiple signal processing methodologies for denoising (smoothing and filtering) and dimensionality reduction (PCA, SVM, EMD, EMD-PCA). Multiple filters and dimensionality reduction methodologies are compared for accurate BPM estimation. The elliptical filter provides a relatively better estimation of the BPM with 87.5% accurate estimation as compared to other bandpass filters such as Butterworth (BF), Chebyshev type 1 Filter (CH1), Chebyshev type 2 Filter (CH2), and wavelet Decomposition (62.5%, 75%, 68.75%, and 75% respectively). Principal Component Analysis (PCA) was performed to provide better dimensionality reduction with 87.5% accurate BPM values compared to EMD, SVD, and EMD-PCA (57%, 44%, and 44% respectively). Additionally, the fine tree algorithm, from the implemented 21 ML supervised classification algorithms with K-fold crossvalidation, was observed to be the optimal choice for multi-class and binary-class classification problems in the presented ResDT model with 96.9% and 95.8% accuracy respectively.
... The I4.0 concept utilizes many technologies, such as big data [1,2], the Internet of Things (IoT) [3], artificial intelligence (AI) [4], and cyber-physical systems (CPS) [5] to achieve this automation or connectivity through continuous data sharing. This amalgamation has had an impact on society and is expected to continue further as new technologies are developed and implemented. ...
The prevalence of chronic diseases and the rapid rise in the aging population are some of the major challenges in our society. The utilization of the latest and unique technologies to provide fast, accurate, and economical ways to collect and process data is inevitable. Industry 4.0 (I4.0) is a trend toward automation and data exchange. The utilization of the same concept of I4.0 in healthcare is termed Healthcare 4.0 (H4.0). Digital Twin (DT) technology is an exciting and open research field in healthcare. DT can provide better healthcare in terms of improved patient monitoring, better disease diagnosis, the detection of falls in stroke patients, and the analysis of abnormalities in breathing patterns, and it is suitable for pre-and post-surgery routines to reduce surgery complications and improve recovery. Accurate data collection is not only important in medical diagnoses and procedures but also in the creation of healthcare DT models. Health-related data acquisition by unobtrusive microwave sensing is considered a cornerstone of health informatics. This paper presents the 3D modeling and analysis of unobtrusive microwave sensors in a digital care-home model. The sensor is studied for its performance and data-collection capability with regards to patients in care-home environments.
... The literature regarding the i4.0 also presents practical applications related to the technologies inherent to the fourth industrial paradigm in the scope of operations management. Zheng et al., (2016) developed an architecture to simulate and test the Smart Factory concept by integrating process technologies (manufacturing cells, robots, AGVs, and automated storage), information (wireless and virtual platform), and computational logic, in order to optimize the decision-making process in a Small Factory. In this project, each productive resource was considered an intelligent logical unit allowing agile responses to control disturbances in the manufacturing environment. ...
This study aims to analyse the literature regarding the
characteristics of Industry 4.0 in the context of operations
management. The analysis covers the evolution of publications
over time, the countries involved, the most prolific journals,
the most cited authors, and the identification of the most
frequent words that can generate insights for the research
agenda. A total of 235 articles published between 2011 and
2017 were collected through an automated process from the
Scopus and Web of Science databases and later analysed using
data mining, bibliometric indicators analysis, clusters analysis,
networks analysis, and word cloud. The bibliographic analysis
explained the interaction between the various concepts and
techniques associated with the central theme. These concepts
and respective characteristics discussed allow an
understanding and the development of agenda with theoretical
possibilities to fill current research gaps.
... Such a model is based on knowledge representation by using domain models (Larman, 2005), a previous version of the class diagram. The process for developing the domain models includes a collection of published documents about AI (Acosta, Sánchez et al., 2018;Tang et al., 2016;Velásquez et al., 2019;Vo et al., 2020;Yamao & Lescano, 2020;Zamora et al., 2017) in Latin America based on a systematic literature review and an extraction of the terms related to the topics. Our domain models resemble the way AI and Industry 4.0 are used in Latin America, so they can also be consulted by the readers of this book in order to compare the proposed solutions with those resulting from our review and synthesis. ...
The surprise arrival of the COVID-19 pandemic produced an accelerated transition in all educational institutions, forcing them to take advantage of digital technologies and the Internet to ensure that their operation could keep going. In this document, a study of various scientific articles, reports, publications, and existing documentation on the digital transformation processes launched in the different Latin American universities was carried out, presenting the methodological proposals promoted toward the new modalities of remote education, the reinvention of administrative processes, and the support provided to the university community to reduce the digital divide. An online survey was designed to know the advances in the digital transformation (DT) of 20 universities in Latin America. Outcomes of the online survey supply insights in four key DT objectives: teaching and learning, student support, research, and administration. Also, a case study of the implementation and monitoring of the DT model at the Technological University of Panama and its projections was considered.
... Descriptive applications and adopted types of historical analyses. For instance, some studies perform a simple statistical analysis(Birglen & Schlicht, 2018;Lenz et al., 2018;Mozgova et al., 2018;Niño et al., 2015;Sanz et al., 2017;Stürmlinger et al., 2018;Tang et al., 2016;Ventura et al., 2019), while others use more sophisticated outlier detection (Y.-M.Lee et al., 2016;Trunzer et al., 2017) and clustering methods (Y.Wang et al., 2017). Some studies use data warehousing databases and dashboards(Kirchen et al., 2017;Neuböck & Schrefl, 2015;Vathoopan et al., 2018;Zheng & Wu, 2017), and other studies used Neural Networks(Kaupp et al., 2019;C.-J. ...
Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet‐of‐Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data‐based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.
... Over the last decades, several MCDM methods have been proposed, the most popular of which are AHP (Analytic Hierarchy Process) [14][15][16], ANP (Analytic Network Process) [5], TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) [17][18][19][20], ELECTRE (ELimination and Choice Expressing REality) [21][22][23], and PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) [24][25][26]. ...
The manufacturing industry is undergoing a major transformation based on the emerging industry 4.0 technologies, such as cloud computing, big data, internet of things and cyber-physical systems. These novelty technologies aim at providing central management for the user’s flexible manufacturing requirements and information. Also, the advent of these technologies has transformed the process planning and became crucial for the building of knowledge-based process planning environments. However, current praxis cannot deal with all semantic issues within this new paradigm, as requirements must be clear, consistent, measurable, stand-alone, testable, unambiguous, unique and verifiable. In this context, multicriteria decision analysis models have gained focus of the scientific and industrial communities as a support tool for the decision-making process in the product development and advanced manufacturing as these processes excel in environments with numerous and conflicting alternatives, providing the optimal alternative. Therefore, the main objective of this research is to highlight the current issues and research tendencies regarding ontology-based interoperability systems, multicriteria decision analysis and their integration. To achieve this goal, it will be applied a literature review on the targeted technologies, discussing the current tendencies of the field and the main issues regarding their implementation and integration. Finally, the paper points themes for further research and indicates viable concepts that can compose a solution for the gaps in a systematic manner.
... The broader concept here refers to the machines that are equipped with data capturing devices. These devices are designed so that they can communicate with other machines and systems, in order to fulfill certain predetermined objectives (Tang et al., 2016). The research carried out during the recent years has seen much emphasis on the advent of smart manufacturing. ...
The true potential of the industry 4.0, which is a byproduct of the fourth industrial revolution, cannot be actually realized. This is, of course true, until the smart factories in the supply chains get connected to each other, with their systems and the machines linked to a common networking system. The last few years have experienced an increase in the adoption and acceptance of the industry 4.0′s components. However, the next stage of smart factories, which will be the smart supply chains, is still in its period of infancy. Moreover, there is a simultaneous need to maintain a focus on the supply chain level implementation of the concept that industry 4.0 puts forth. This is important in order to gain the end to end benefits, while also avoiding the organization to organization compatibility issues that may follow later on. When considering this concept, limited research exists on the issues related to the implementation of industry 4.0, at the supply chain level. Hence, keeping in mind this lack of literature and research available, on a phenomenon that will define the future of business and industry, this study uses an exploratory approach to capture the implementation of industry 4.0 concepts across multiple tiers of the supply chain. Based on this research, the study proposes a multistage implementation framework that highlights the organizational enablers such as culture, cross-functional approach, and the continuous improvement activities. Furthermore, it also highlights the staged implementation of the advanced tools, starting from the focal organization with the subsequent integration with the partner organizations.
