scholarly journals Sustainable Manufacturing in Industry 4.0: Cross-Sector Networks of Multiple Supply Chains, Cyber-Physical Production Systems, and AI-driven Decision-Making

2019 ◽  
Vol 7 (2) ◽  
pp. 31
Keyword(s):  
Decision Making ◽  
Supply Chains ◽  
Industry 4.0 ◽  
Production Systems ◽  
Sustainable Manufacturing ◽  
Cyber Physical Production Systems

scholarly journals Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

2021 ◽  
Vol 129 ◽  
pp. 04003
Author(s):  
Elvira Nica ◽  
Gheorghe H. Popescu ◽  
George Lăzăroiu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Industry 4.0 ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Cyber Physical Production Systems

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

One-of-a-kind Production in Cyber-Physical Production Systems Considering Machine Failures

2021 ◽  
pp. 1-20
Author(s):  
Guido Vinci Carlavan ◽  
Daniel Alejandro Rossit
Keyword(s):  
Industry 4.0 ◽  
Information Technologies ◽  
Job Shop ◽  
Production Systems ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Due Dates ◽  
Efficient Manner ◽  
Large Variability ◽  
Cyber Physical Production Systems

Industry 4.0 proposes the incorporation of information technologies at all levels of the production process. By incorporating these technologies, Industry 4.0 provides new tools for production planning processes, allowing to address problems in an innovative and efficient manner. From these technologies and tools, it is that in this work a One-of-a-Kind Production (OKP) process is approached, where the products tend to be highly customized. OKP implies working with a very large variability within production, demanding very efficient planning systems. For this, a planning model based on CONWIP-type strategies was proposed, which seeks to level the production of a shop floor configured in the form of a job shop. Even more, for having a more realistic shop-floor representation, machine failures have been included in the model. In turn, different dispatching rules were proposed to study the performance and analyze the behaviour of the system. From the results obtained, it is observed that, when the production demand is very exigent in relation with the capacity of the system, the dispatching rules that analyze the workload generated by each job tend to perform better. However, when the demand on the capacity of the production system is less intense, the rules associated with due dates are the ones that obtain the best results.

scholarly journals Simulation von Cyber-Physischen Produktionssystemen in einer Forschungsinfrastruktur

2020 ◽  
Author(s):  
Iris Gräßler
Keyword(s):  
Communication Technology ◽  
Information Acquisition ◽  
Industry 4.0 ◽  
Production Control ◽  
Production Systems ◽  
General Concept ◽  
Radio Network ◽  
Systems Research ◽  
Digital Twins ◽  
Cyber Physical Production Systems

The article describes the setup of an experimentation and validation environment by extending a production laboratory: All relevant elements of the production laboratory were equipped with computer systems, so-called "industry 4.0 boxes", and interconnected via a peer-to-peer radio network. The "industry 4.0 boxes" are used to upgrade dedicated sensors for recording machine behaviour and communication technology to be integrated into decentralized production control. In addition, digital twins were implemented to map machine and user behaviour, enable control and support information acquisition and processing. Thereby, a research infrastructure is created for research on potentials of cyber-physical production systems. Research outcomes will be used as a decision basis for companies and for validation of production optimizations. This paper describes the concept and implementation of industry 4.0 functionalities and derives a general concept of simulation platforms for CPPS.

Integration of Cutting-Edge Interoperability Approaches in Cyber-Physical Production Systems and Industry 4.0

2021 ◽  
pp. 144-172
Author(s):  
Luis Alberto Estrada-Jimenez ◽  
Terrin Pulikottil ◽  
Nguyen Ngoc Hien ◽  
Agajan Torayev ◽  
Hamood Ur Rehman ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Production Systems ◽  
Heterogeneous Systems ◽  
Smart Manufacturing ◽  
Use Case ◽  
Integrative Framework ◽  
Data Formats ◽  
Exchange Information ◽  
Cyber Physical Production Systems ◽  
Physical Components

Interoperability in smart manufacturing refers to how interconnected cyber-physical components exchange information and interact. This is still an exploratory topic, and despite the increasing number of applications, many challenges remain open. This chapter presents an integrative framework to understand common practices, concepts, and technologies used in trending research to achieve interoperability in production systems. The chapter starts with the question of what interoperability is and provides an alternative answer based on influential works in the field, followed by the presentation of important reference models and their relation to smart manufacturing. It continues by discussing different types of interoperability, data formats, and common ontologies necessary for the integration of heterogeneous systems and the contribution of emerging technologies in achieving interoperability. This chapter ends with a discussion of a recent use case and final remarks.

scholarly journals Energy Efficiency in Industry 4.0: The Case of Batch Production Processes

2020 ◽  
Vol 12 (16) ◽  
pp. 6631 ◽  
Author(s):  
Giancarlo Nota ◽  
Francesco David Nota ◽  
Domenico Peluso ◽  
Alonso Toro Lazo
Keyword(s):  
Energy Consumption ◽  
Industry 4.0 ◽  
Production Systems ◽  
Decision Makers ◽  
Batch Processes ◽  
Energy Losses ◽  
Manufacturing Processes ◽  
Batch Production ◽  
Production Processes ◽  
Cyber Physical Production Systems

We derived a promising approach to reducing the energy consumption necessary in manufacturing processes from the combination of management methodologies and Industry 4.0 technologies. Based on a literature review and experts’ opinions, this work contributes to the efficient use of energy in batch production processes combining the analysis of the overall equipment effectiveness with the study of variables managed by cyber-physical production systems. Starting from the analysis of loss cause identification, we propose a method that obtains quantitative data about energy losses during the execution of batch processes. The contributions of this research include the acquisition of precise information about energy losses and the improvement of value co-creation practices so that energy consumption can be reduced in manufacturing processes. Decision-makers can use the findings to start a virtuous process aiming at carbon footprint and energy costs reductions while ensuring production goals are met.

scholarly journals TEACHING NETWORK TECHNOLOGIES THAT SUPPORT INDUSTRY 4.0

2015 ◽  
Author(s):  
Ishwar Singh ◽  
Nafia Al-Mutawaly ◽  
Tom Wanyama
Keyword(s):  
Industry 4.0 ◽  
Production Systems ◽  
Manufacturing Plants ◽  
Electrical Grid ◽  
Distributed Intelligence ◽  
Industrial Networks ◽  
Manufacturing Competitiveness ◽  
Manufacturing Productivity ◽  
Cyber Physical Production Systems ◽  
Network Technologies

Industry 4.0 is a combination of many elements, including distributed intelligence, network security, massive data, cloud computing, and analytics, among other things. Such elements are critical to the “Digital Factory”, a term that has been recently introduced by many companies indicating a comprehensive portfolio of seamlessly integrated hardware, software and technology-based services, with the aim to enhance manufacturing productivity and improving efficiency. Typically, industrial networks enable the gathering of extensive data from productionlines and plants, which are increasingly becoming distributed. The gathered data is transmitted to analysis centers where it is transformed into information and used to make better informed decisions. In addition, modern industrial networks allow plant data to be automatically filtered and transmitted to various production controllers. Ultimately, industrial networks enable Industry 4.0 to have the following benefits: improved safety, increase uptime, lower energy costs, and improved maintenance;all of which lead to manufacturing competitiveness in cyber-physical production systems supported by Smart Grid implementations. This paper presents the extent to which industrial networks are taught at the School ofEngineering Technology at McMaster University. Further, the paper covers teaching methods of industrial networks and their related applications within manufacturing plants and electrical grid.

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