Digitalisation and servitisation of machine tools in the era of Industry 4.0: a review

2021 ◽  
pp. 1-33
Author(s):  
Chao Liu ◽  
Pai Zheng ◽  
Xun Xu
Keyword(s):  
Machine Tools ◽  
Industry 4.0

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.

Machine Tool 4.0 in the Era of Digital Manufacturing

2020 ◽  
Author(s):  
Dimitris Mourtzis
Keyword(s):  
Vertical Integration ◽  
Machine Tools ◽  
Industry 4.0 ◽  
Digital Technologies ◽  
Point Of View ◽  
Practical Implementation ◽  
Digital Manufacturing ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Fully Connected

Under the Industry 4.0 framework, a plethora of digital technologies and techniques has been introduced in the Manufacturing domain. Machine tools must become more intelligent, in order to create a network of fully connected machines. By extension, this will lead to the creation of the Industrial Internet of Things (IIoT). Although these technologies provide for increased functionality of the manufacturing equipment, there are certain issues/implications, refraining engineers from integrating such technologies in the production. Therefore, in this paper, the results of a systematic literature review are presented and discussed, including the horizontal and vertical integration of such digital technologies. The contribution of this paper extends to the recognition of the opportunities emerging as well as the identified implications from a practical implementation point of view.

scholarly journals Modular smart controller for Industry 4.0 functions in machine tools

Procedia CIRP ◽  
2019 ◽  
Vol 81 ◽  
pp. 1331-1336 ◽  
Author(s):  
David Barton ◽  
Philipp Gönnheimer ◽  
Florian Schade ◽  
Christopher Ehrmann ◽  
Jürgen Becker ◽  
...  
Keyword(s):  
Machine Tools ◽  
Industry 4.0
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