scholarly journals Integration of machine tools with company IT systems in scope of INDUSTRY 4.0 idea

Mechanik ◽  
2015 ◽  
pp. 193/246-193/252
Author(s):  
Łukasz Ślązak
Keyword(s):  
Machine Tools ◽  
Industry 4.0 ◽  
It Systems

Reinforcing the cyber resilience of a credit institution in Industry 4.0

2020 ◽  
Vol 16 (6) ◽  
pp. 998-1012
Author(s):  
G.V. Fedotova ◽  
D.D. Tkachenko
Keyword(s):  
Content Analysis ◽  
Public Administration ◽  
Data Security ◽  
Industry 4.0 ◽  
Credit Institution ◽  
Security Systems ◽  
Banking Systems ◽  
It Systems ◽  
Smart Technologies ◽  
Banking Development

Subject. The article discusses the modeling of preventive protection of IT systems and evaluates their cyber resilience. Objectives. The study evaluates the existing threats and determines how informatization processes may unfold in the credit segment. Methods. Research is based on methods of regulatory and legislative analysis. We evaluate today’s public administration of cybersecurity in the financial and credit sector. To give a view of the existing situation and sum up the sector’s performance for the recent years, we performed the content analysis of statistics on data hacking and leakages. Results. The article highlights new trends in the financial and credit sector and the growing complexity of data security systems. As proposed by the Bank of Russia, the integration of smart technologies is showed to reinforce the cybersecurity of banking systems. Conclusions and Relevance. The informatization of all banking operation systems, growing complexity of procedures and work logs require new robust resources to be integrated into financial technologies. Stronger cybersecurity should lay a trend in the financial and credit sector in the nearest future. The findings can be used to flag strategic milestones of the banking development in the information-driven society.

Industry 4.0

2019 ◽  
pp. 138-156 ◽  
Author(s):  
Krishnan Umachandran ◽  
Igor Jurčić ◽  
Valentina Della Corte ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Industry 4.0 ◽  
Data Science ◽  
Industrial Revolution ◽  
Interconnected Systems ◽  
Organizational Systems ◽  
It Systems ◽  
Skilled Workforce ◽  
New Form ◽  
Physical Assets ◽  
Functional Capacities

Industry 4.0 can be considered the 21st century's industrial revolution and will soon be the new form of manufacturing delight. The definitive customer would experience manufacturing requests determined by artificial intelligence, machine learning, and automated technologies linked with data science support for gauging customer necessities. Phenomenally, Industry 4.0 is rapidly changing the firm's management and organizational systems, and competencies, as well as making its environment much more explored, even if more complexed than in the past. This new industrial revolution would possess systems with transformative technologies for managing interconnected systems between its physical assets and computational capabilities. Such enterprises would require skilled workforce to improve and operate advanced manufacturing tools and systems, and investigate the machine data, clients, and global capitals, resulting in an escalating need for trained employees proficient in cross-functional capacities and with competencies to cope new processes and IT systems.

Industrie-4.0-Nachrüstkit für Werkzeugmaschinen/Modular solution for condition-based maintenance and process monitoring – Industry 4.0 retrofitting kit for machine tools

2020 ◽  
Vol 110 (07-08) ◽  
pp. 491-495 ◽  
Author(s):  
David Barton ◽  
Reinhard Stamm ◽  
Sebastian Mergler ◽  
Cedric Bardenhagen ◽  
Jürgen Fleischer
Keyword(s):  
Process Monitoring ◽  
Machine Tools ◽  
Industry 4.0 ◽  
Economic Benefit ◽  
Industrie 4.0 ◽  
High Potential ◽  
Condition Based Maintenance ◽  
Machining Processes ◽  
Software Modules ◽  
Roll Out

Industrie 4.0 verspricht ein hohes wirtschaftliches Potenzial für produzierende Unternehmen. Allerdings wird dieses in bestehenden Werkzeugmaschinen bisher nur wenig ausgeschöpft. Um das Ausrollen von Funktionen für die zustandsorientierte Instandhaltung und die Überwachung des Bearbeitungsprozesses zu ermöglichen, wurde ein modulares Nachrüstkit entwickelt. Mit dem Kit können Maschinen individuell um Hardware- und Softwarebausteine erweitert werden.   Industry 4.0 offers manufacturers a high potential for economic benefit. However, this potential is only rarely exploited in existing machine tools. To enable the roll-out of functions for condition-based maintenance and monitoring of machining processes, a modular retrofitting kit has been developed. This kit allows machines to be individually upgraded with hardware and software modules.

scholarly journals Smart retrofitting of machine tools in the context of industry 4.0

Procedia CIRP ◽  
2020 ◽  
Vol 88 ◽  
pp. 369-374
Author(s):  
Sara Salman Hassan Al-Maeeni ◽  
Christopher Kuhnhen ◽  
Bernd Engel ◽  
Michael Schiller
Keyword(s):  
Machine Tools ◽  
Industry 4.0

scholarly journals Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain

2020 ◽  
Vol 12 (20) ◽  
pp. 8629 ◽  
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Royo ◽  
Juan Carlos Sánchez ◽  
Lisbeth del Carmen Ng Corrales
Keyword(s):  
Supply Chain ◽  
Energy Consumption ◽  
Real Time ◽  
Physical System ◽  
Machine Tools ◽  
Industry 4.0 ◽  
Performance Indicator ◽  
Green Supply Chain ◽  
Cyber Physical System ◽  
Key Performance Indicator

This work investigates Industry 4.0 technologies by developing a new key performance indicator that can determine the energy consumption of machine tools for a more sustainable supply chain. To achieve this, we integrated the machine tool indicator into a cyber–physical system for easy and real-time capturing of data. We also developed software that can turn these data into relevant information (using Python): Using this software, we were able to view machine tool activities and energy consumption in real time, which allowed us to determine the activities with greater energy burdens. As such, we were able to improve the application of Industry 4.0 in machine tools by allowing informed real-time decisions that can reduce energy consumption. In this research, a new Key Performance Indicator (KPI) was been developed and calculated in real time. This KPI can be monitored, can measure the sustainability of machining processes in a green supply chain (GSC) using Nakajima’s six big losses from the perspective of energy consumption, and is able to detect what the biggest energy loss is. This research was implemented in a cyber–physical system typical of Industry 4.0 to demonstrate its applicability in real processes. Other productivity KPIs were implemented in order to compare efficiency and sustainability, highlighting the importance of paying attention to both terms at the same time, given that the improvement of one does not imply the improvement of the other, as our results show.

Frühzeitige Detektion von Oberflächenzerrüttungen/Premature detection of surface disruption – Deep Learning-based method for classification of damage on ball screw drives

2020 ◽  
Vol 110 (07-08) ◽  
pp. 501-506
Author(s):  
Peter Ruppelt ◽  
Tobias Schlagenhauf ◽  
Jürgen Fleischer
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Preventive Maintenance ◽  
Machine Tools ◽  
Industry 4.0 ◽  
Industrie 4.0 ◽  
Ball Screw ◽  
Camera System ◽  
Ball Screw Drives

Die Zustandsüberwachung von Anlagen, Maschinen und deren Bauteilen ist eine zentrale Thematik von Industrie 4.0. Unvorhergesehene Ausfälle von Werkzeugmaschinen sind häufig auf den Verschleiß und das daraus resultierende Versagen von Kugelgewindetrieben zurückzuführen. Aufgabe dieser Arbeit ist die frühzeitige Detektion von Oberflächenschäden auf der Kugelgewindetriebspindel mit einem elektromechanischen Kamerasystem in Kombination mit Deep-Learning-basierten Modellen, um entsprechende Wartungsmaßnahmen abzuleiten.   Condition monitoring of plants, machines and their components is a central topic of Industry 4.0. Unforeseeable failures of machine tools are often caused by wear, resulting in failure of ball screws and subsequent surface disruptions. This article describes how image-based monitoring of ball screws by an electronic camera system in combination with deep learning-based models enable the early detection of surface disruptions and to derive appropriate and preventive maintenance measures.

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