scholarly journals Digital Twin for Automatic Transportation in Industry 4.0

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3344
Author(s):  
Alberto Martínez-Gutiérrez ◽  
Javier Díez-González ◽  
Rubén Ferrero-Guillén ◽  
Paula Verde ◽  
Rubén Álvarez ◽  
...  
Keyword(s):  
Value Chain ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Industrial Plant ◽  
Smart Manufacturing ◽  
Time Data ◽  
Digital Twin ◽  
Incremental Value ◽  
Material Requirement ◽  
Definition Of

Industry 4.0 is the fourth industrial revolution consisting of the digitalization of processes facilitating an incremental value chain. Smart Manufacturing (SM) is one of the branches of the Industry 4.0 regarding logistics, visual inspection of pieces, optimal organization of processes, machine sensorization, real-time data adquisition and treatment and virtualization of industrial activities. Among these tecniques, Digital Twin (DT) is attracting the research interest of the scientific community in the last few years due to the cost reduction through the simulation of the dynamic behaviour of the industrial plant predicting potential problems in the SM paradigm. In this paper, we propose a new DT design concept based on external service for the transportation of the Automatic Guided Vehicles (AGVs) which are being recently introduced for the Material Requirement Planning satisfaction in the collaborative industrial plant. We have performed real experimentation in two different scenarios through the definition of an Industrial Ethernet platform for the real validation of the DT results obtained. Results show the correlation between the virtual and real experiments carried out in the two scenarios defined in this paper with an accuracy of 97.95% and 98.82% in the total time of the missions analysed in the DT. Therefore, these results validate the model created for the AGV navigation, thus fulfilling the objectives of this paper.

Digital Transformation and Industry 4.0

2021 ◽  
pp. 1-17
Author(s):  
Hanaa Abdulraheem Yamani ◽  
Waleed Tageldin Elsigini
Keyword(s):  
Customer Service ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Digital Transformation ◽  
Fourth Industrial Revolution ◽  
Industrial Companies ◽  
The Future ◽  
Information And Communication ◽  
Definition Of

The current era is witnessing many changes on various levels. The information and communication revolutions are considered one of the important changes which has cast a shadow over how different institutions in society work via the phenomenon of digitization. As some of the most important institutions of society, industrial companies have been responding to this phenomenon of digital transformation to improve products and customer service while achieving a significant profitable return. This response by these institutions to the digital transformation has resulted in the emergence of the so-called fourth industrial revolution. In this context, this chapter reviews the definition of digital transformation as well as its dimensions, benefits, and obstacles. It also comments on the future of digital transformation and its relationship with industry. Ultimately it presents the fourth industrial revolution in terms of its definition, history, criteria, benefits, and the challenges it faces moving into the future.

scholarly journals Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5504
Author(s):  
Hyang-A Park ◽  
Gilsung Byeon ◽  
Wanbin Son ◽  
Hyung-Chul Jo ◽  
Jongyul Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Industrial Revolution ◽  
Storage System ◽  
Research Field ◽  
Optimization Techniques ◽  
Virtual Space ◽  
Machine Learning Techniques ◽  
Time Data ◽  
Digital Twin ◽  
Learning Techniques

Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern.

scholarly journals A Gap Study between Employers’ Expectations in Thailand and Current Competence of Master’s Degree Students in Industrial Engineering under Industry 4.0

2021 ◽  
Vol 27 (1) ◽  
pp. 50-57
Author(s):  
Sirorat Pattanapairoj ◽  
Krisanarach Nitisiri ◽  
Kanchana Sethanan
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Master's Degree ◽  
Big Data Analytics ◽  
Industrial Engineering ◽  
Smart Manufacturing ◽  
Educational Institutions ◽  
Time Data ◽  
Analytic Hierarchy ◽  
Master’S Degree

Abstract Industry 4.0 is an era in which the manufacturing industry has adopted digital technologies and the Internet to enable smart manufacturing system, machines used in the production now can communicate with each other and exchange information between each other, and the machinery used in the manufacturing process is more modern and precise. Therefore, educational institutions should develop the curriculum to produce qualified graduates with the knowledge required for the Industry 4.0 era, especially Industrial Engineering graduates who are directly related to the industry sector. The purpose of this research is to collect the data for the Master of Industrial Engineering (MSIE) curriculum development. The Analytic Hierarchy Process (AHP) technique is used to rank the indicators of knowledge that is important to the employment of graduates with a master’s degree in Industrial Engineering, and study the gap between the expectations of employers and the ability of the current MSIE students of Khon Kaen University. The results of the study reveal that the first indicators that are most important to the employment of MSIE graduates is the knowledge of Industry 4.0 strategy and the knowledge that the students should have developed are the collaboration of humans and robots, big data analytics, real time data usage and databased decision making.

scholarly journals Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

2021 ◽  
Vol 13 (18) ◽  
pp. 10139
Author(s):  
Vivek Warke ◽  
Satish Kumar ◽  
Arunkumar Bongale ◽  
Ketan Kotecha
Keyword(s):  
Bibliometric Analysis ◽  
Industrial Revolution ◽  
Cost Effective ◽  
Smart Manufacturing ◽  
Future Research ◽  
Digital Twin ◽  
Network Analyses ◽  
Research Areas ◽  
Enabling Technologies ◽  
Research Theme

The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies. In the current scenario of Industry 4.0, industries are shaping themselves towards the development of customized and cost-effective processes to satisfy customer needs with the aid of a digital twin framework, which enables the user to monitor, simulate, control, optimize, and identify defects and trends within, ongoing process, and reduces the chances of human prone errors. This paper intends to make an appraisal of the literature on the digital twin (DT) framework in the domain of smart manufacturing with the aid of critical enabling technologies such as data-driven systems, machine learning and artificial intelligence, and deep learning. This paper also focuses on the concept, evolution, and background of digital twin and the benefits and challenges involved in its implementation. The Scopus and Web of Science databases from 2016 to 2021 were considered for the bibliometric analysis and used to study and analyze the articles that fall within the research theme. For the systematic bibliometric analysis, a novel approach known as Proknow-C was employed, including a series of procedures for article selection and filtration from the existing databases to get the most appropriate articles aligned with the research theme. Additionally, the authors performed statistical and network analyses on the articles within the research theme to identify the most prominent research areas, journal/conference, and authors in the field of a digital twin. This study identifies the current scenarios, possible research gaps, challenges in implementing DT, case studies and future research goals within the research theme.

An Initial Framework for Implementation of Industry 4.0 in the High Technological Manufacturing Sector in Southern California

2019 ◽  
Author(s):  
Sagil James ◽  
Anupam Shetty
Keyword(s):  
Industry 4.0 ◽  
Southern California ◽  
Industrial Revolution ◽  
Manufacturing Sector ◽  
Smart Manufacturing ◽  
New Paradigm ◽  
Manufacturing Sectors ◽  
Southern California Region ◽  
The Internet Of Things ◽  
Initial Framework

Abstract The fourth industrial revolution, also known as Industry 4.0 is a new paradigm that is significantly influencing several manufacturing industries across the globe. Industry 4.0 synchronizes concepts such as Smart Manufacturing, Smart Factory, and the Internet of Things with existing factory automation technologies in order to improve value in manufacturing by monitoring key performance indicators and creates value in all manufacturing related aspects. Currently, several industries have started early initiatives of implementing these technologies. As the industries are evaluating their readiness for implementing the Industry 4.0 concepts, there are several challenges which need to be addressed including high initial investment, lack of standardization, data security and lack of skilled labor. A strategic roadmap towards implementing the Industry 4.0 paradigms is still unclear in the industry as well as in academia. This research develops an initial framework for the effective implementation of Industry 4.0 in the high technology manufacturing sectors in the Southern California region. The results of this study are expected to provide a platform to expand the opportunities of Industry 4.0 further and facilitate worldwide adoption.

The Application of the Positive Semi-Definite Kernel Space for SVM in Quality Prediction

2020 ◽  
Vol 13 (2) ◽  
pp. 228-233
Author(s):  
Wang Meng ◽  
Dui Hongyan ◽  
Zhou Shiyuan ◽  
Dong Zhankui ◽  
Wu Zige
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Industrial Revolution ◽  
Feature Space ◽  
Mixed Data ◽  
Smart Manufacturing ◽  
Additive Constant ◽  
Kernel Space ◽  
Data Set ◽  
Low Dimensional

Background: A transformation toward 4th Generation Industrial Revolution (Industry 4.0) is being led by Germany based on Cyber-Physical System-enabled manufacturing and service innovation. Smart manufacturing is an important feature of Industry 4.0 which uses the networked manufacturing systems for smart production. Current manufacturing systems (5M1E systems) require deeper mining of the data which is generated from manufacturing process. Objective: To map low-dimensional embedding into the input space would meet the requirement of “kernel trick” to solve a problem in feature space. On the other hand, the distance can be calculated more precisely. Methods: In this research, we proposed a positive semi-definite kernel space by using a constant additive method based on a kernel view of ISOMAP. There were 6 steps in the algorithm. Results: The classification precision of KMLSVM was better than SVM in the enterprise data set, in which SVM selected the RBF kernel and optimized its parameters. Conclusion: We adopted the additive constant method in kernel space construction and the positive semi-definite kernel was built. The typical mixed data set of an enterprise was used in simulation. We compared the SVM and KMLSVM in this data set and optimized the SVM kernel function parameters. The simulation results demonstrated the KMLSVM was a better algorithm in mix type data set than SVM.

Industry 4.0 Adoption in Manufacturing Industries Using Technology-Organization-Environment Framework

2021 ◽  
Vol 14 (1) ◽  
pp. 123-146
Author(s):  
Kanchan Pranay Patil
Keyword(s):  
Value Chain ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Manufacturing Industries ◽  
It Services ◽  
Factors Affecting ◽  
Technology Competence ◽  
A Value ◽  
Governance Practices ◽  
Trading Partners

This paper investigated the determinant factors affecting the Industry 4.0 ecosystem needed for the digitization and automation of manufacturing industries. The 4th industrial revolution implements a value chain by interfacing internet of things devices and robotics, data processing in the cloud using artificial intelligence-based analytics. The study was conducted in Pune, India, a manufacturing and IT services hub. It sought to identify Industry 4.0 facilitators and inhibitors by framing empirical data collected from 320 manufacturing facilities and analyzed using PLS-SEM within a model based on technology-organization-environment (TOE) theory and motivation-threat-ability (MTA) theory. The results confirmed that technology competence, organization scope, consumer readiness, competitive pressure, trading partners' readiness, and governance practices are the facilitators, whereas organization resistance inhibits Industry 4.0 adoption intentions. The outcome of this study shall provide guidelines to manufacturing industries management as well as technology solution providers.

scholarly journals Algorithm for Designing Reconfigurable Equipment to Enable Industry 4.0 and Circular Economy-Driven Manufacturing Systems

2021 ◽  
Vol 11 (10) ◽  
pp. 4446
Author(s):  
Emilia Brad ◽  
Stelian Brad
Keyword(s):  
Circular Economy ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Optimal Solution ◽  
Smart Manufacturing ◽  
Manufacturing Enterprises ◽  
Design Engineers ◽  
Definition Of ◽  
High Level ◽  
Reconfigurable Machine Tool

In the paradigm of industry 4.0, manufacturing enterprises need a high level of agility to adapt fast and with low costs to small batches of diversified products. They also need to reduce the environmental impact and adopt the paradigm of the circular economy. In the configuration space defined by this duality, manufacturing systems must embed a high level of reconfigurability at the level of their equipment. Finding the most appropriate concept of each reconfigurable equipment that composes an eco-smart manufacturing system is challenging because every system is unique in the context of an enterprise’s business model and technological focus. To reduce the entropy and to minimize the loss function in the design process of reconfigurable equipment, an evolutionary algorithm is proposed in this paper. It combines the particle swarm optimization (PSO) method with the theory of inventive problem-solving (TRIZ) to systematically guide the creative potential of design engineers towards the definition of the optimal concept over equipment’s lifecycle: what and when you need, no more, no less. The algorithm reduces the number of iterations in designing the optimal solution. An example for configuration design of a reconfigurable machine tool with adjustable functionality is included to demonstrate the effectiveness of the proposed algorithm.

scholarly journals Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions

2021 ◽  
Vol 11 (12) ◽  
pp. 5725
Author(s):  
Anbesh Jamwal ◽  
Rajeev Agrawal ◽  
Monica Sharma ◽  
Antonio Giallanza
Keyword(s):  
Industry 4.0 ◽  
Industrial Revolution ◽  
Big Data Analytics ◽  
Research Progress ◽  
Smart Manufacturing ◽  
Future Research ◽  
Business Practices ◽  
Research Directions ◽  
Research Areas ◽  
Future Research Directions

Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies.

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