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In the past five years, several industrial initiatives such as “Industry 4.0”, “Industrial Internet of Things”, “Factories of the Future” and “Made in China 2025”, have been announced by different governments and industrial leaders. These initiatives lead to an urgent need to advance current manufacturing systems into a high level of intelligence and autonomy. As the main component of any manufacturing system, machine tools have evolved from manually operated machines into the current computer numerically controlled (CNC) machine tools. It is predicted that current CNC machine tools are not intelligent and autonomous enough to support the smart manufacturing systems envisioned by the aforementioned initiatives. Inspired by recent advances in ICT such as Cyber-Physical Systems (CPS) and Internet of Things (IoT), this paper proposes a new generation of machine tools, i.e. Machine Tool 4.0, as a future development trend of machine tools. Machine Tool 4.0, otherwise known as Cyber-Physical Machine Tool (CPMT), is the integration of machine tool, machining processes, computation and networking, where embedded computers and networks can monitor and control the machining processes, with feedback loops in which machining processes can affect computations and vice versa. The main components and functions of a CPMT are presented. The key research issues related to the development of CPMT are identified and discussed. A three-layer CPMT-centered Cyber Physical Production System (CPPS) is proposed to illustrate both the vertical integration of various smart systems at different hierarchical levels and the horizontal integration of field-level manufacturing facilities and resources.
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2212-8271 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
Peer-review under responsibility of the scientifi c committee of The 50th CIRP Conference on Manufacturing Systems
doi: 10.1016/j.procir.2017.03.078
Procedia CIRP 63 ( 2017 ) 70 75
The 50th CIRP Conference on Manufacturing Systems
Cyber-Physical Machine Tool – the Era of Machine Tool 4.0
Chao Liu, Xun Xu*
Department of Mechani cal Engineering, Unive rsity of Auckland, Auckland, 1010, New Zealand
* Corresponding author. Tel.: +64 9 373 7599; fax: +64 9 373 7599. E-mail address:
In the past five years, several industrial initiatives such as “Industry 4.0”, “Industrial Internet of Things”, “Factories of the Future” and “Made
in China 2025”, have been announced by different governments and industrial leaders. These initiatives lead to an urgent need to advance
current manufacturing systems into a high level of intelligence and autonomy. As the main component of any manufacturing system, machine
tools have evolved from manually operated machines into the current computer numerically controlled (CNC) machine tools. It is predicted that
current CNC machine tools are not intelligent and autonomous enough to support the smart manufacturing systems envisioned by the
aforementioned initiatives. Inspired by recent advances in ICT such as Cyber-Physical Systems (CPS) and Internet of Things (IoT), this paper
proposes a new generation of machine tools, i.e. Machine Tool 4.0, as a future development trend of machine tools. Machine Tool 4.0,
otherwise known as Cyber-Physical Machine Tool (CPMT), is the integration of machine tool, machining processes, computation and
networking, where embedded computers and networks can monitor and control the machining processes, with feedback loops in which
machining processes can affect computations and vice versa. The main components and functions of a CPMT are presented. The key research
issues related to the development of CPMT are identified and discussed. A three-layer CPMT-centered Cyber Physical Production System
(CPPS) is proposed to illustrate both the vertical integration of various smart systems at different hierarchical levels and the horizontal
integration of field-level manufacturing facilities and resources.
© 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of The 50th CIRP Conference on Manufacturing Systems.
Keywords: Machine tool 4.0; Cyber-Physical Machine Tool; Cyber Physical Production System;
1. Introduction
In the past five years, several industrial initiatives such as
“Industry 4.0”, “Industrial Internet of Things”, “Factories of
the Future” and “Made in China 2025”, have been announced
by different governments and industrial leaders. Although the
technological focus and implementation strategy of these
initiatives may differ from one another, they have indicated
that a new industrial revolution is currently happening. While
previous industrial revolutions were triggered by the invention
of mechanical manufacturing facilities (end of 18th century),
the introduction of electrically-powered mass production
(start of 20th century) and the intensive utilization of
electronics and Information Technology (IT) (start of 1970s),
respectively, the new industrial revolution that we are
experiencing is predicted to be based on the advances of
Information and Communication Technology (ICT) such as
Cyber-Physical Systems (CPS), Internet of Things (IoT) and
cloud computing [1].
Recently, Reference Architectural Model Industry 4.0
(RAMI 4.0) [2] was developed to provide a guideline for the
interdisciplinary Industry 4.0 technologies. RAMI 4.0
illustrates the connection between IT, manufacturers/plants
and product life cycle through a three-dimensional space. A
new concept named “Industry 4.0 component” [3] was also
proposed to complement RAMI 4.0 in connecting products,
equipment and processes. Industry 4.0 component comprises
physical objects and the Administration Shell (a virtual
representation of the physical object and describes its
functionalities). The integration of RAMI 4.0 and Industry 4.0
component bridges the gap between standards and Industry
4.0 technologies at the production level, giving rise to Cyber-
Physical Production Systems (CPPS). CPPS are envisioned as
the next generation production systems in which CPS monitor
© 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
Peer-review under responsibility of the scientifi c committee of The 50th CIRP Conference on Manufacturing Systems
Chao Liu and Xun Xu / Procedia CIRP 63 ( 2017 ) 70 – 75
the physical processes, create virtual counterparts of the
physical objects, make decentralized decisions with embedded
intelligence and autonomously communicate and cooperate
with each other as well as with humans in real time [4].
Machine tools have a hand in virtually everything that is
manufactured and they no doubt play a unique and essential
role in any CPPS. The development of machine tool
technologies from the late 19th century up to the present time
has, to a large extent, mirrored that of industrial revolution.
The significance of such development affirms the claim that
machine tools are the ubiquitous instruments of modern
manufacturing. They have long occupied an iconic position in
debates about industrial modernization. In light of the new era
of industrialization and the important role that machine tools
play, the need to advance machine tools to a new level that
accords to the concept of Industry 4.0 has to be recognized
and addressed urgently. In fact, like the industrializations as
described above, machine tools have also gone through three
stages of technological advancements. As we step into the era
of Industry 4.0, so do we to the age of Machine Tool 4.0 (or
MT 4.0).
This paper proposes a new generation of machine tools as a
future development trend. MT 4.0 gives rise to Cyber-
Physical Machine Tool (CPMT), which serves as a key
enabler for CPPS. The rest of this paper is organized as
follows: Section 2 gives a review of the historical evolution of
machine tools from MT 1.0 to MT 4.0; Section 3 introduces
the proposed CPMT, including the definition, main
components and characteristics; In Section 4, the key research
issues related to the development of CPMT are identified and
discussed; A three-layer CPMT-centered CPPS is proposed in
Section 5; Section 6 concludes the paper.
2. From Machine Tool 1.0 to Machine Tool 4.0
Machine tools came into being when the tool-path first
became guided by the machine itself, in replacement of direct
and freehand human guidance of the tool-path. Figure 1
shows the evolution of machine tools from MT 1.0 to MT 4.0.
Fig. 1. From Machine Tool 1 .0 to Machine Tool 4.0
2.1. Machine Tool 1.0 (1775-1945)
While it is true that certain machine tools existed long
before the Industrial Revolution, there is no doubt that the
development of machine tools as we know them today is
closely linked to the first several decades of the Industrial
Revolution, namely from the late 1700s to early 1800s. The
first machine tools offered for sale (i.e. commercially
available) were constructed in England around 1800. During
this time, both Maudslay and Whitworth were responsible for
dramatically advancing the accuracy of the machine tools [5].
With these new machine tools, the decades-old objective of
producing interchangeable parts was finally realized. In the
early 20th century, automobile manufacture dominated
machine tool development in that the automotive industry
pushed for a rapid progress of standardization and the
advances of machine tool design and construction. Machine
tools up till this time were mostly manually operated with
some form of mechanical assistance for high precision
machining. These tools still required a great deal of skill and
experience from the operator.
2.2. Machine Tool 2.0 (1945-1980)
Since the late 1940s, machine tools had experienced a
significant advancement in motion actuation and control, i.e.
the development and deployment of numerical control (NC),
and the gradual shift from mechanical to electronic actuations
in general. The first NC machines were designed for manual
or fixed cycle operations at the Massachusetts Institute of
Technology in the late 1940s [6]. These machines had
numerical control systems added, but only for numerical
control on positioning the workpiece relative to the tool.
Considerable time was saved, yet the operator had to select
the tools, speeds and feeds. Later, the enhanced NC machines
enabled material removal to occur at the same time as control
of the workpiece/tool movements. These NC machines were
also termed tape-controlled machines, because the
information was stored on either punched card/tape or
magnetic tape [7]. It was cumbersome to edit the programs at
the machine; the machines had only very limited memory
capacity. Nevertheless, in comparison with the conventional
manually-operated machine tools, the advantages of NC
machine tools are multiple. Savings were made in manpower,
machining time, cutting tools and some accessories. NC
machine tools also helped improve product quality and cut
down rejects.
2.3. Machine Tool 3.0 (1980-)
The advancement of computers, in particular around the
1970s, eventually resulted them being used in assisting NC
machines, hence the birth of Computer Numerical Control
(CNC) machine tools [8]. In a CNC machine, a
microcomputer stores machining programs that are prepared
beforehand and controls the operation of the machine.
Typically, a CNC system contains a machine-control unit
(MCU) and the machine tool itself. MCU is further divided
into two elements: the data-processing unit (DPU) and the
Machine Tool 4.0
machine tools, cloud-
based solutions
72 Chao Liu and Xun Xu / Procedia CIRP 63 ( 2017 ) 70 – 75
control-loop unit (CLU). The DPU processes the coded data
and passes information on position in each axis, direction of
motion, feed, and auxiliary function control signals to the
CLU. The CLU operates the drive mechanisms of the
machine, and receives feedback signals about the actual
position and velocity on each axis.
Introduction of CNC machines radically changed the
landscape of manufacturing. Curves are as easy to cut as
straight lines; complex 3-D structures are relatively easy to
produce; and the number of machining steps that require
human action is dramatically reduced. CNC automation also
allows for more flexibility in the way that part programs for
different components can be quickly produced and executed
on a single machine tool. The significance of CNC machines
in fact goes much further beyond machines themselves.
Several CNC machines can be tied together and/or controlled
via a central computer to perform coordinated machining
processes. This gives rise to Direct Numerical Control (DNC)
[9]. CNC machines also brought manufacturing much closer
to the component design processes (i.e. CAD) by tools such as
computer-aided process planning (CAPP) and computer-aided
manufacturing (CAM). For the first time, integration of
design and manufacturing became a possibility [10].
2.4. Machine Tool 4.0
Throughout the evolution process of machine tools from
MT 1.0 to MT 3.0, efforts to make machine tools faster, more
accurate, reliable, flexible and safer have never stopped.
Today’s machine tools have also become more economical
and resource-efficient [11]. Machining centers continue to
offer more functions [12]. Technologies for machine tool
components (e.g. bearing, spindle unit, control unit and
drivers) have also contributed to the continued technological
enhancement of machine tools [13,14]. Nevertheless, as we
step into the era of Industry 4.0, an urgent need to advance
current CNC machine tools to a higher level of connectivity,
intelligence and autonomy, has also been raised.
Machine Tool 4.0 defines a new generation of machine
tools that are smarter, well connected, widely accessible, more
adaptive and more autonomous. MT 4.0 features an extensive
implementation of CPS, IoT and cloud computing
technologies in CNC machine tools. With MT 4.0, vertical
and horizontal integration in production systems becomes
possible. Machine tools will no longer exist as a piece of
isolated manufacturing equipment; they are service and
solution providers. MT 4.0 gives rise to the Cyber-Physical
Machine Tool, which serves as a key enabler for the
envisioned CPPS.
3. Cyber-Physical Machine Tool
Inspired by recent advances in ICT such as CPS, IoT and
cloud computing, we propose a new generation of machine
tools – Cyber-Physical Machine Tools – as a promising
development trend of machine tools in the era of MT 4.0.
3.1. Definition
CPMT is the integration of the machine tool, machining
processes, computation and networking, where embedded
computations monitor and control the machining processes,
with feedback loops in which machining processes can affect
computations and vice versa.
CPMT is an application of CPS in the manufacturing
environment. It shares the common features with typical CPS,
including network connectivity, adaptability, predictability,
intelligence, with real-time feedback loops and with humans
in the loop. More specifically, real-time data generated by
machine tool and machining processes are captured using
various sensors, data acquisition devices and cameras.
Together with the feedback from the CNC controller, these
real-time data from the physical world are transferred into the
cyber space through various networks to build a Cyber Twin
of the machine tool. The Machine Tool Cyber Twin (MTCT),
as a digital abstraction of the machine tool, has built-in
computation and decision-making which monitor and control
the physical components and processes and provide the data
to the cloud for further analysis.
3.2. Components and functions
As shown in Figure 2, the proposed CPMT consists of four
main components: (1) CNC Machine Tool, (2) Data
Acquisition Devices, (3) MTCT, and (4) Smart Human-
Machine Interfaces (HMIs).
Fig. 2. Components and functions of Cyber-Physical Machine Tool
(1) CNC Machine Tool: The CNC machine tool refers to
the physical CNC machine tool including all components and
subsystems, as well as the machining processes. The machine
tool receives machining tasks from the CNC controller and
performs machining operations.
(2) Data Acquisition Devices: Data acquisition devices
include various types of sensors (e.g. power meters,
dynamometers, accelerometers, AE sensors, temperature
sensors, etc.), cameras, RFID tags and readers, signal
processing devices, and so forth. Data acquisition devices are
responsible for collecting real-time field-level manufacturing
data from the critical components and machining processes
CNC Machine Tool DAQ Devices
Machine Tool Cyber Twin
Algorithms & Analytic s
PHM Algorithms
Optimization Algo rithms
High-fidelity Simulation
AR-assisted Visualization
Informat ion Mo del
Smart HMIs
M2M Interfaces
Emergency Stop
Linear X
Actual psition
Commanded position
Emergency Stop
Logic program
Path feedrate
Path position
Controller mode
Linear Z
Actual psition
Commanded position
Rotary C
Spindle speed
Chao Liu and Xun Xu / Procedia CIRP 63 ( 2017 ) 70 – 75
such that important real-time manufacturing data generated
during machining processes can be recorded and analyzed in
the next stages.
(3) Machine Tool Cyber Twin:The most significant
difference between a CPMT and a traditional CNC machine
tool lies in the Machine Tool Cyber Twin. MTCT is a digital
model of the physical machine tool with embedded
computational capabilities. It functions as the brain of the
machine tool, takes full advantage of the real-time data
collected from the physical world and endows the physical
machine tool with intelligent and autonomous functionalities.
MTCT comprises four main components: a) Information
Model, b) Database, c) Intelligent Algorithms and Analytics,
and d) Machine-to-Machine (M2M) Interfaces.
xThe Information Model comprehensively represents both
the structure of the machine tool and the real-time status of
each critical component by taking full advantage of the
real-time data coming from the Data Acquisition Devices.
xThe Database records all the important historical
information of the machine tool, making it available for
further analysis both locally and in the cloud.
xIntelligent algorithms and analytics transform the data
coming from Data Acquisition Devices into meaningful
information and offer various intelligent and autonomous
functions, such as Prognostics and Health Management
(PHM), machining optimization and Augmented Reality
(AR)-assisted process visualization. Intelligent algorithms
and analytics make the machine tool more adaptive to the
changing machining conditions.
xM2M Interfaces allow the MTCT to semantically
communicate with the Cyber Twins of other field-level
devices (robots, AGVs, workpieces, etc.). Embedded
algorithms enable the physical objects in the
manufacturing system to monitor and control each other,
leading towards an autonomous-cooperative manufacturing
(4) Smart Human-Machine Interfaces: With extensive real-
time data and computations deeply integrated with machining
processes, CPMT requires Smart HMIs that allow users to
intuitively interact with the system and make efficient
decisions. Smart HMIs should provide users with ubiquitous
access to the data and functions offered by MTCT. PCs,
tablets, smart phones and wearable devices are all able to
become smart HMIs with the implementations of various
network and interaction technologies.
4. Key research issues
The key research issues related to the development of
CPMT are identified and discussed in this section, intending
to provide future research directions in this area.
4.1. Real-time manufacturing data acquisition
During machining processes, each component and
subsystem of the machine tool generates large amounts of
real-time data. Some of the data have significant influence on
the product quality, productivity and cost efficiency.
Collecting these data is the prerequisite for all the subsequent
functionalities of the CPMT.
Although modern CNC controllers could directly provide
some useful feedback data (e.g. spindle speed, axes position,
etc.), some critical data that severely affect the manufacturing
processes such as tool/workpiece/machine vibration,
temperature, cutting force, etc. can only be acquired by
deploying additional sensors [15]. With the rapid
development of sensing technology, various sensors (e.g.
force/torque, accelerometers, acoustic emission,motor power
and current sensors, etc.) are available for extracting different
data from the machine tool. However, identification of
appropriate data sources and implementation of reliable,
accurate and efficient sensing technologies in the real
manufacturing environment still remainsa great challenge. A
summary of real-time data acquisition technologies regarding
process monitoring can be found in [16].
4.2.Data integration and communication
Although different data acquisition technologies could be
implemented to acquire data from various data sources, the
meaning, wording, units and values of those data usually vary
from machine to machine and device to device. This
complexity makes the data integration, management and
exchange in the CPMT a challenging task. There is an urgent
need for a unified data exchange standard for the field-level
manufacturing devices.
Currently, MTConnect and OPC-UA are both striving to
address this issue. MTConnect is a lightweight, open, and
extensible protocol designed for the exchange of data between
shop floor equipment and software applications [17]. OPC-
UA is an open and royalty free set of standards designed as a
universal factory floor communication protocol developed by
the OPC Foundation [18]. MTConnect and OPC-UA both
have their pros and cons. MTConnect provides a bottom-up
strategy which makes it easy to be implemented. However, it
is currently a read-only standard, which means it is only able
to be used in reading data from the devices, but not writing to
them. On the other hand, OPC-UA is a bidirectional standard
which is able to be used for both monitoring and controlling.
However, a lot of effort has to be spent on building the
application-specific information models, which usually makes
the implementation of OPC-UA complex and inefficient.
Extensive efforts from both academia and industry still need
to be made to address this issue.
4.3. Intelligent algorithms and analytics
Even with extensive data gathered from field-level devices,
intelligent algorithms and data analytics methods have to be
developed to take full advantage of these data. These
intelligent algorithms and analytics need to be embedded into
the MTCT to endow the physical devices with advanced
autonomous functionalities and decision-making capabilities.
Research in this area has always been active. To shorten
machining time and increase product quality, Ridwan et al.
[19] developed a Fuzzy logic algorithm that allows in-process
feed-rate optimization. Kadir et al. [20] developed a System
Manager algorithm to achieve high-fidelity machining
simulation by the utilization of STEP, STEP-NC and real-time
74 Chao Liu and Xun Xu / Procedia CIRP 63 ( 2017 ) 70 – 75
monitoring data. A comprehensive review of PHM
methodologies and techniques can be found in [21]. Although
a lot of work has been done in this area, the industrial big data
generated by field-level devices still require a major research
effort in developing effective, accurate and reliable algorithms
and data analytics methods in order to provide the machine
tool with real intelligence.
4.4. M2M communication
Generally, M2M refers to the communications among
computers, embedded processors, sensors, actuators, and
mobile terminal devices without or with limited human
intervention [22]. In the proposed CPMT, M2M
communications include the communications between the
machine tool and other field-level devices, for example
robots, AGVs, workpieces, and so forth. M2M interfaces
should allow the machine tool to exchange information with
other devices so that they can actively monitor and control
each other.
Research on M2M communication is still at the
preliminary stage. Developing M2M interfaces for the
proposed CPMT is a crucial and challenging task.
4.5. Advanced Human-Machine Interactions
Although it is important to endow the physical devices
with intelligence and autonomy, CPMT is not gravitating
towards an unmanned system. In effect, with extensive real-
time data and computations deeply integrated with machining
processes, CPMT allows advanced human-machine
interactions. Smart, mobile, networked and context-sensitive
HMIs need to be developed to provide users with: (1)
comprehensive and intuitive perception of the CPMT, (2)
ubiquitous access to the real-time information and
applications, as well as (3) instant and distributed decision-
making support. PCs, Smart phones, tablets and wearable
devices can play an important role in this situation.
Recently, AR, as a novel human-computer interaction
technology that overlays computer-generated virtual
information on the real world environment [23], is attracting
more and more attention in manufacturing. AR-enabled
process monitoring [24] and AR-assisted machining
simulation [25] have shown great advantages and potentials in
improving human-machine interactions. AR will be a key
enabling technology for human-machine interactions in the
proposed CPMT.
5. CPMT-centered CPPS
5.1.System architecture
The aim of developing CPMT is to promote the realization
of CPPS. To illustrate the feasibility and functionalities of the
proposed CPMT, a CPMT-centered CPPS is proposed as
shown in Figure 3. The CPPS comprises three layers: Physical
Level, Cyber Space and Service Cloud.
(1)Physical level:The Physical Level contains all the
physical elements involved in the manufacturing system
including the machine tools and its components, the cutting
tools, the workpieces, the industrial robots, the AGVs, and
various data acquisition devices. Critical objects which may
generate valuable data during the manufacturing processes
(such as the spindle of the machine tool, the cutting tools, the
workpieces, etc.) are equipped with sensors and actuators so
that real-time data from the Physical Level can be collected
and the assigned tasks can be executed.
(2) Cyber space: The Cyber Space is a networked space
comprised of interconnected Cyber Twins of the critical
objects. Similar to the MTCT, each Cyber Twin in the Cyber
Space represents a digital abstraction of its physical
counterpart. On the one hand, embedded algorithms and
analytics take advantage of the real-time data collected from
the Physical Level such that the Cyber Twins can monitor and
control its physical counterpart with intelligent and
autonomous functions. On the other hand, M2M interfaces
allow the Cyber Twins to communicate with each other, thus
enabling autonomous cooperation between field-level
manufacturing devices. In addition, the Cyber Twins record
the historical information of their physical counterparts and
provide them to the cloud through various networks so that
further analysis and value-added services can get direct access
to the field-level manufacturing data.
(3) Service cloud: The Service Cloud contains various
software applications provided by different equipment
manufacturers and third-party service providers. For example,
machine tool manufacturers may provide machining
optimization service for their machine tool users; cutting tool
manufacturers may offer tool wear prediction service for their
cutting tool users; third-party software developers may
provide Enterprise Resource Planning applications for
enterprise managers. These applications reside in the Service
Cloud. They are able to access the information in the Cyber
Space through the networks. Smart HMIs enable these
services to be accessed through various types of interactions
from anywhere. Users can request specific services based on
their requirements and pay based on usage.
Fig. 3. System architecture of CPMT-centered CPPS
Chao Liu and Xun Xu / Procedia CIRP 63 ( 2017 ) 70 – 75
5.2.Vertical integration and horizontal integration
The proposed system architecture allows the CPMT-
centered CPPS to realize two dimensional integrations of a
manufacturing system, i.e. vertical integration and horizontal
integration. Vertical integration refers to the integration of the
various smart systems at different hierarchical levels of a
manufacturing system (e.g. components, actuators and sensors
in the device level, controllers and PLCs in the control level,
and data analytics and production planning applications in the
ERP level). Horizontal integration refers to the integration of
machine tools with other field-level manufacturing facilities
and resources (e.g. robots, AGVs and other machine tools).
The vertical integration endows the CPPS with
adaptiveness. The Cyber Twins acquire real-time data of their
physical counterparts, monitor and control them with the aid
of various algorithms and data analytics tools, making the
field-level devices semi-autonomous. In the meantime, real-
time data from the shop floors can be effectively accessed by
the applications in the service cloud so that high-level
business planning activities can be integrated with field-level
production activities. The horizontal integration, on the other
hand, endows the CPPS with autonomous cooperation
capabilities. The Cyber Twins communicate with each other
through M2M interfaces, monitor and control each other
based on specific algorithms and the real-time data they
obtained from the real world. In this way, the field-level
devices autonomously cooperate with each other, thus a
significant reduction of the required human effort can be
The combination of vertical integration and horizontal
integration in the CPPS leads to improved product quality,
increased productivity and reduced production cost.
6. Conclusions
At the dawn of the new industrial revolution,several
industrial initiatives have clearly indicated an urgent need to
advance current manufacturing systems into a high level of
intelligence and autonomy, i.e. CPPS. As the key element of
any production system, machine tools are expected to make
step-changes to the so-called Machine Tool 4.0. MT 4.0
defines a new generation of machine tools that are smarter,
well connected, widely accessible, more adaptive and more
autonomous. Inspired by recent advances in ICT such as CPS,
IoT and cloud computing, a new generation of machine tools,
i.e. Cyber-Physical Machine Tools, is proposed as a
promising development trend of machine tools in the era of
MT 4.0. A three-layer CPMT-centered CPPS is proposed to
illustrate both the vertical integration of various smart systems
at different hierarchical levels and the horizontal integration
of field-level manufacturing facilities and resources.
Machine Tool 4.0 is set to transform a machine tool from a
physical production commodity to a product service system
and cloud resource. This new generation of machine tools
requiresa collective effort from the machine tool
manufacturers, users and researchers to define a roadmap for
technology development and a strategy for industry
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... Of all manufacturing sectors, machine tools have been identified as key to this sustainability transition due to their high energy usage, which has prompted their inclusion in an indicative list of product groups with significant improvement potential in the context of the European Ecodesign Directive [22]. Additionally, machine tools are increasingly becoming enmeshed in cyber-physical production systems [23]. A rather conservative industry with tight margins is thus put under transformative pressure [1] to make a double transition which is both smart and sustainable. ...
... Studies on the link between Industry 4.0 and sustainability are plagued by the "double disease" [42] of a predominance of practitioner's reports on the one hand and a bias towards technological studies on the other. For instance, studies have dealt with recycling of, e.g., machine tool coolants [43] and industrial symbiosis [44,45], the digitization of machine tools [18] and sustainable, energy efficient machine tools [23,46] from a purely technological angle. Economic studies of the peculiarities of the smart sustainability transition in manufacturing are needed [11,42,47,48], and have a particularly crucial role as concerns smart machine tools [49]. ...
... The latest digitization wave of Industry 4.0 ( Figure 1) could see machine tools become a set of fully-fledged general purpose technologies [57,58], giving rise to productivity gains in a wide range of sectors [28,59] comparable to nanotechnology [60]. [23,53]; timeline indicates major watershed events without representing year spans in exact proportion. ...
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Only one third of studies on the Industry 4.0–sustainability link have been conducted in manufacturing, despite its centrality to “ensuring sustainable consumption and production patterns” (UN Sustainable Development Goal nr. 12). The European Ecodesign Directive singled out machine tools as key to the sustainability transition, not least due to their high energy usage and their increasingly becoming enmeshed in cyber-physical production systems. This paper aims to find out whether the digital transformation underway in machine tools is sustainable as well as to identify its central technological pathways. Externalities in machine tools are tracked over three decades (1990–2018) by means of a multi-method setting: (1) mapping the Technological Innovation System (TIS) of machine tools; (2) co-occurrence analysis of transnational patent families, in order to reduce geographical and market distortions (Questel’s FAMPAT); and (3) analysis of the incidence of digital and sustainable technologies in machine tools patent applications (WIPO PATENTSCOPE). A smart sustainability transition is currently not hampered by a lack of smart technologies but rather by the sluggish introduction of sustainable machine tools. Cyber-physical and robot machine tools have been found to be central pathways to a smart sustainability transition. Implications for harnessing externalities reach beyond the machine tools industry.
... First, an IIoT architecture in the RMFS is proposed. The IIoT structure integrates and creates a physical and cyber layer with an industrial cloud database and CPS for further massive data processing [83][84][85][86][87][88]. The CPS structure integrates into the physical layer and cyber layer. ...
... Sensors and cameras are applied for real-time monitoring, failure detection, and machine degradation for the suppliers. Thus, an automatic optical inspection based on computer vision and sensors for preventive and predictive maintenance in manufacturing systems are necessary for helping the engineer to determine the industrial environment and enhancing the operational efficiency and effectiveness in manufacturing systems [86,87,[103][104][105][106][107]. The energy efficiency can also be monitored through the centralized system to reduce the overall operating costs of the energy consumption. ...
In this paper, we intend to address the value creation of utilizing the Industrial Internet of Things (IIoT)-driven resource synchronization and sharing-based robotic mobile fulfillment system (RMFS) to enhance the overall operational effectiveness and efficiencies during information transfer and synchronization of resources. With the advent of IIoT, a graph theory-based heuristic under the multi-deep RMFS is used for computing the shortest path. A-star, Dijkstra, and genetic heuristic algorithms are applied for comparison. A simulation with a consideration of the different types of collisions is conducted for different algorithms. By providing a new three-tier IIoT architecture which includes the suppliers, RMFS, and the disposal center, a model is developed with different storage location assignment rules and strategies under the particular parties to minimize the operation costs. IIoT enables resource synchronization and information sharing, and the path will be generated under different order scenarios with different algorithms. The results show that different storage assignment rules and strategies may lead to 30% cost differences compared to the company’s current practice with random storage.
... Industry is quickly moving towards ever more precise and intelligent manufacturing [1]. Precise and intelligent machine tools play an important role in the success of smart factory concept in the aerospace industry [2]. ...
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This paper presents an experimental study conducted to assess the correlation between the intra-axis errors of prismatic axes for CNC machine tools. The validity and reliability of parametric models for the modeling of intra-axis errors (IAEs) of CNC machine tools in the context of indirect calibration are also assessed in this work. Three CNC machine tools with various controllers and guidance technologies were tested using two different measuring instruments. Two predictive models, namely Bézier and B-spline curves, are described and compared for the first time in this work. Both models are experimentally evaluated for accuracy and predictive efficiency using four evaluation criteria and new data sets from the three tested CNC machine tools. Results show a strong correlation between the positioning errors and the pitch and yaw errors for all the tested machines. The results also show that both proposed models are appropriate for the modeling of intra-axis errors, with the B-spline curves coming slightly on top in terms of performance. Moreover, with the same number of control points (n = 5), the two models provide residuals that are lower than the repeatability of the machine for most intra-axis errors tested. This experimental study thus confirms that a Bézier model of degree four and a B-spline model of degree two, both with five control points, are sufficient to represent the intra-axis errors for the tested CNC machine tools.
... The term Machine Tool 4.0 stands for a new technological evolution currently in progress, that defines a new generation of AI machine tools that are smarter, well connected, widely accessible, more adaptive and more autonomous [188,189]. In these systems, real-time manufacturing data from the physical devices are collected by diverse types of sensors and transferred into the cyber space by different networks, where is modeled by integrating the manufacturing data with an information model, a database and different intelligent algorithms. ...
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During machining processes, a large amount of heat is generated due to plastic deformation, in a very small area of the cutting tool. This high temperature strongly influences chip formation mechanisms, tool wear, tool life, and workpiece surface integrity and quality. In this sense, knowing the temperature at various points of tool, chip, and workpiece during machining processes is of utmost importance to effectively optimize cutting parameters, improve machinability and product quality, reduce machining costs, and increase tool life and productivity. This paper presents a review of the various methods for temperature measurement and prediction in machining processes, being the different methods discussed and evaluated regarding its merits and demerits. The most suitable method for a given application depends on several aspects, such as cost, size, shape, accuracy, response time, and temperature range. Lastly, some future perspectives for real-time cutting temperature monitoring in the scope of Industry 4.0 and 5.0 are outlined, as well as being presented a new field of tools capable of measuring and controlling cutting temperature, called smart cutting tools.
The new cellular network standard 5G meets the networking requirements for many industrial use cases due to the advantages of low latency, high bandwidth, and high device density while providing a very good quality of service. These capabilities enable the wireless realization of digital twins (DT), a key element of cyber-physical production systems. A requirement of DT is the bidirectional exchange of information between the digital and the physical world. 5G is the only technology that enables wireless, highly scalable, and flexible realization of even safety- and latency-critical connections (e.g., between a machine tool and its motion control unit). In this paper, a 5G enabled DT of a machine tool for machine control and simulation is developed and implemented. A bidirectional control of the physical machine tool and the DT is realized by offloading the machine control unit to an edge server via 5G. The simulation instances are also offloaded to the edge server and obtain the required information from the control system. In addition, this paper outlines the components for implementing a 5G-enabled DT and aims to discuss the benefits, disadvantages, and potentials of the 5G-enabled DT.
Smart Manufacturing (SM) epitomises the idea of connected, technology and data-driven factories that can provide the high levels of innovativeness and responsiveness required in today’s fiercely competitive and unpredictable global market. With numerous SM-driven initiatives worldwide such as Industry 4.0 (I4.0), Made in China 2025 etc., SM developments have been fuelled by technological advances such as Cloud Computing, Big Data, Cyber-Physical Systems and the Internet of Things (IoT). This paper analyses the role of such technologies in SM and how they can be put into a coherent framework for the implementation of SM solutions, termed Link4Smart. This is illustrated with the Link4Smart factory concept, where horizontal integration (across the factory shop floor) and vertical integration (from machine to Engineering Resource Planning). With self-aware, self-predict, self-compare, self-configure, self-maintain and self-organized Link4Smart Machine Tools, smart manufacturing systems can change, update and/or adapt to daily production challenges.
The hotel industry is one of the fastest-growing industries in the country. Fewer Millennials are willing to work in the hotel industry, which primarily employs unskilled workers. The purpose of this study is to look at the factors that influence millennials' retention and perceptions of technology in the Malaysian hotel industry. This study is carried out using a quantitative method. The Google Forms questionnaire was created and distributed to Millennial employees in star rated hotels in Kuala Lumpur. Employee retention strategies such as personal emotion support, reward and recognition, work environment, and work characteristics, according to the findings of this study, can boost job satisfaction, which leads to retention. It is vital for businesses to be able to keep its people in order to stay in business. Despite the fact that this study intended to include all aspects of staying at a company for a long period, it fell short. According to the study, compensation, work schedule flexibility, and work-life balance are the most important factors for employees to stay with the company, while lower compensation and benefits, inequality and favouritism, a lack of importance for professional growth, and a lack of development opportunities are the most important factors for employees to leave. According to the research, the organisation should focus on the following retention measures to boost employee happiness and retention: flexible working hours, employee awards, and career development programmes.
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The agnostic robotic paradigm (ARP) represents a recent development as the use of robots becomes more common, and there is a need for agnostic robots to cope with rich artificial objects environments. All parties and stakeholders need to seize the imminent opportunity and act on ushering in the revolutionary changes of contemporary robotic and facility control solutions. The scalability and effectiveness of robotic enterprise solutions depend primarily on the availability of operational information, robotic solutions, and their information infrastructure. However, different functions and software of robotics and facilities are being launched in the market. Therefore, this paper investigates the implementation of the emerging ARP for the Industrial Internet of Things (IIoT) and resource synchronisation flexible robotic and facility control system to address this challenge. We propose an Artificial Intelligence (AI) edge intelligence and IIoT-based agnostic robotic architecture for resource synchronisation and sharing in manufacturing and robotic mobile fulfillment systems (RMFS). We adopted simultaneous localisation and mapping (SLAM) as one of the edge intelligence, provided the simulation results, and tested with multiple parameters under different conflicts. Our research suggests that purposely developing an ARP for flexible robotic and facility control system via IIoT assisted with AI-edge intelligence are a good solution for both operational and management level under a cloud platform.
The digital twin is now within reach while manufacturing and manufacturing process become increasingly digital and the Internet of Things (IoT) is becoming more and more dominant. Digital twins are intended to model complex structures and processes that communicate with their environments in various ways, for which it is challenging to predict effects over the entire lifecycle of the product. A digital twin is a virtual model that during its life cycle simulates a physical entity or operation, providing a near real-time connection between both the physical and virtual world. A digital twin allows the industry to detect physical issues sooner, predict outcomes more accurately, and build better products. The IoT drives digital twin as a trend in a wide range of industries, by offering them the potential to take the advantages, of mass customization along with mass personalization, maintaining, at the same time, mass production efficiency. Developing a digital twin needs different components, including sensors, communications networks, and a digital platform. This chapter aims to map major architectures and applications of digital twins for Industry 4.0, along the lines of manufacturing systems, manufacturing processes and robots, automation and virtual reality in manufacturing.
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In recent years, cyber-physical systems (CPS) have emerged as a promising direction to enrich the interactions between physical and virtual worlds. In this article, we first present the correlations among machine-to-machine (M2M), wireless sensor networks (WSNs), CPS and internet of things (IoT), and introduce some research activities in M2M, including M2M architectures and typical applications. Then, we review two CPS platforms and systems that have been proposed recently, including a novel prototype platform for multiple unmanned vehicles with WSNs navigation and cyber-transportation systems. Through these reviews, we propose CPS is an evolution of M2M by the introduction of more intelligent and interactive operations, under the architecture of IoT. Also, we especially hope to demonstrate how M2M systems with the capabilities of decision-making and autonomous control can be upgraded to CPS and identify the important research challenges related to CPS designs.
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One of the most significant directions in the development of computer science and information and communication technologies is represented by Cyber-Physical Systems (CPSs) which are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the internet. Cyber-Physical Production Systems (CPPSs), relying on the newest and foreseeable further developments of computer science, information and communication technologies on the one hand, and of manufacturing science and technology, on the other, may lead to the 4th Industrial Revolution, frequently noted as Industry 4.0. The key-note will underline that there are significant roots generally – and particularly in the CIRP community – which point towards CPPSs. Expectations and the related new R&D challenges will be outlined.
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This paper reviews the research and development of augmented reality (AR) applications in design and manufacturing. It consists of seven main sections. The first section introduces the background of manufacturing simulation applications and the initial AR developments. The second section describes the current hardware and software tools associated with AR. The third section reports on the various studies of design and manufacturing activities, such as AR collaborative design, robot path planning, plant layout, maintenance, CNC simulation, and assembly using AR tools and techniques. The fourth section outlines the technology challenges in AR. Section 5 looks at some of the industrial applications. Section 6 addresses the human factors and interactions in AR systems. Section 7 looks into some future trends and developments, followed by conclusion in the last section.
Computer numerical control (CNC) simulation systems based on 3D graphics have been well researched and developed for NC tool path verification and optimization. Although widely used in the manufacturing industries, these CNC simulation systems are usually software-centric rather than machine tool-centric. The user has to adjust himself from the 3D graphic environment to the real machining environment. Augmented reality (AR) is a technology that supplements a real world with virtual information, where virtual information is augmented on to real objects. This paper builds on previous works of integrating the AR technology with a CNC machining environment using tracking and registration methodologies, with an emphasis on in situ simulation. Specifically configured for a 3-axis CNC machine, a multi-regional computation scheme is proposed to render a cutting simulation between a real cutter and a virtual workpiece, which can be conducted in situ to provide the machinist with a familiar and comprehensive environment. A hybrid tracking method and an NC code-adaptive cutter registration method are proposed and validated with experimental results. The experiments conducted show that this in situ simulation system can enhance the operator’s understanding and inspection of the machining process as the simulations are performed on real machines. The potential application of the proposed system is in training and machining simulation before performing actual machining operations.
Energy consumption reduction is critical in various industrial environments. Machine tool manufacturers could contribute to this matter by developing advanced functions for machines. Power consumption of machining center was measured in various conditions. The conclusion was that modifying cutting conditions reduces energy consumption. This applies for either regular drilling, face/end milling or deep hole machining. Also, a new acceleration control method is developed to reduce energy consumption by synchronizing spindle acceleration with feed system. Experiments were performed to verify these methods and promising results were achieved.
The main purpose of any machining simulation system is to reveal or mimic the real machining process as accurately as possible. Current simulation systems often use G-code or CL data as input that has inherent drawbacks such as vendor-specific nature, incomplete data, irreversible data conversions and lack of accuracy. These limitations hinder the development of a truthful simulation system. Hence, there is a need for higher-level input data that can assist with accurate simulation for machining processes. In addition, there is also a need to take into account of true behaviour and real-time data of a machine tool. The paper presents a High-Fidelity Machining Simulation solution for more accurate results. STEP-NC is used as the input data as it provides a more complete data model for machining simulations. The status-quo of the machine tool is captured by means of sensors to provide true data values for machining simulation purposes. The outcome of the research provides a smart and better informed simulation environment. The paper reviewed some of the current simulation approaches, highlighted the current simulation problems, discussed input data sources for smart machining simulation and introduced the high-fidelity simulation system architecture.
This paper presents the state-of-the-art in machine tool main spindle units with focus on motorized spindle units for high speed and high performance cutting. Detailed information is given about the main components of spindle units regarding historical development, recent challenges and future trends. An overview of recent research projects in spindle development is given. Advanced methods of modeling the thermal and dynamical behavior of spindle units are shown in overview with specific results. Furthermore concepts for sensor and actuator integration are presented which all focus on increasing productivity and reliability.