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Opportunities for Industry 4.0 to Support Remanufacturing

Authors:

Abstract

Remanufacturing is the process of bringing end-of-life products back to good-as-new. It plays a critical role in decoupling economic growth from growth in resource use, and in accelerating the circular economy. However, the uptake of remanufacturing activities faces obstacles. This paper reviews the challenges encountered by the remanufacturing sector and discusses how the Industry 4.0 revolution could help to effectively address these issues and unlock the potential of remanufacturing. Two case studies are included in this paper to exemplify how technology enablers from Industry 4.0 can increase efficiency, reliability, and digitization of the remanufacturing process.
applied
sciences
Article
Opportunities for Industry 4.0 to
Support Remanufacturing
Shanshan Yang, Aravind Raghavendra M. R. *, Jacek Kaminski and Helene Pepin
Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and
Research (A*STAR), 3 CleanTech Loop, #01/01, CleanTech Two, Singapore 637143, Singapore;
yangs@artc.a-star.edu.sg (S.Y.); kaminskijk@artc.a-star.edu.sg (J.K.); helene-pepin@artc.a-star.edu.sg (H.P.)
*Correspondence: aravindr@artc.a-star.edu.sg; Tel.: +(65)-6908-7911
Received: 28 June 2018; Accepted: 13 July 2018; Published: 19 July 2018
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Abstract:
Remanufacturing is the process of bringing end-of-life products back to good-as-new.
It plays a critical role in decoupling economic growth from growth in resource use, and in
accelerating the circular economy. However, the uptake of remanufacturing activities faces obstacles.
This paper reviews the challenges encountered by the remanufacturing sector and discusses how
the Industry 4.0 revolution could help to effectively address these issues and unlock the potential of
remanufacturing. Two case studies are included in this paper to exemplify how technology enablers
from Industry 4.0 can increase efficiency, reliability, and digitization of the remanufacturing process.
Keywords:
remanufacturing; sustainable manufacturing; Industry 4.0; smart factory; intelligent
machining
1. Introduction
The growth and stability of the traditional “take-make-consume-dispose” linear model has been
heavily dependent on the availability of resources. However, the linear model is now challenged by an
unprecedented rise in demand for the finite supply of resources, as it is expected that by 2030 there
will be three billion more middle-class consumers worldwide [
1
]. With this in mind, the “circular
economy” model is drawing global attention as an approach to decouple economic growth from
resource constraints. The underlying principle of the “circular economy” is to make products and
materials restorative and regenerative by design, and to maintain them at their highest utility and
value [2].
Circulation of the technical product life cycle is enabled through several end-of-life (EOL)
channels, including recycling, remanufacturing, reusing, refurbishing, etc. Among these EOL strategies,
remanufacturing shows noticeable advantages due to its effectiveness in preserving the added value
of products and assuring their quality. Remanufacturing is the process of bringing EOL products back
to good-as-new status through disassembly, cleaning, inspection/sorting, restoring, and reassembly.
It provides social, environmental, and economic benefits by offering to consumers end products with
assured quality at a competitive price, while protecting the intellectual property and brand image
of original equipment manufacturers (OEMs). It also creates and opens up new business and job
opportunities in the after-sales service market. Another main advantage of remanufacturing lies in
protection of the environment by reducing the usage of raw materials, carbon footprint, and number
of components being scrapped. However, the uptake of remanufacturing faces several obstacles,
which need to be resolved properly through collaboration among multiple players across the business,
government, investors, society, and research communities [
3
]. Fortunately, the advent of Industry
4.0 has provided immense opportunities for unlocking the potential for remanufacturing by reducing
the cost of transformation into a higher level of connectivity and efficiency.
Appl. Sci. 2018,8, 1177; doi:10.3390/app8071177 www.mdpi.com/journal/applsci
Appl. Sci. 2018,8, 1177 2 of 11
Industry 4.0, as the name suggests, refers to the fourth stage of industrialization, aiming for a high
level of automation in the manufacturing industry through the adoption of ubiquitous information
and communication technologies (ICTs). The boundaries between the virtual environment and real
world get increasingly blurred, which is called “cyber-physical production systems (CPPSs)”. In simple
terms, in CPPSs, electronic and mechanical components are linked through sensors in a network,
which provides a smart platform for data flow and data analytics. An early form of this technology is
the implementation of radio frequency identification (RFID) sensors, which have been in wide use
since the year 1999.
In this paper, the opportunities that Industry 4.0 bring to the remanufacturing industry are
discussed and presented from the perspectives of “Smart Life Cycle Data”, “Smart Factory”, and “Smart
Services”. Case studies are presented to exemplify the use of ICT to increase the efficiency and accuracy
of the remanufacturing process.
2. Literature Review
Remanufacturing activities span a number of industries. Remanufacturing has experienced a
higher chance for success in sectors in which:
Products are durable and usually contain high-value materials;
The technology cycle is stable and longer than the useful life cycle;
Restoration technologies are available;
Products have the potential to be leased or delivered as a service rather than as hardware.
The above-mentioned factors are also the reasons why more than 60% of remanufacturing
activities, in terms of production value, are concentrated in the aerospace, automotive, and heavy-duty
off-road vehicle sectors. Examples of products or components being remanufactured include aerospace
engines, alternators for automotive, gears for heavy-duty vehicles, etc. More recently, driven by
noticeable economic profits and ever more stringent environmental regulations, remanufacturing has
also been carried out by other industries, such as IT products, machinery, tires, and furniture.
Bottlenecks to remanufacturing vary by geographic location, industry sectors size of the individual
remanufacturer, and nature of business. Nevertheless, there are still some common challenges,
which are identified as follows:
Lack of standards and legislation
The lack of a commonly accepted definition and standards for remanufactured products in various
sectors has been identified as the most prevalent barrier to remanufacturing. This has resulted in
not only the loss of consumer trust in remanufactured products, but also restrictions on importing or
exporting remanufactured products in certain countries [4].
Lack of life cycle design awareness
Many barriers encountered during the remanufacturing process could be eliminated if proper
design features were included in the early stage of product design. For example, avoiding the use of
permanent joints reduces the complexity of disassembly processes, and standardizing part designs
simplifies the inspection and sorting process. Currently, most design for remanufacturing (DfRem)
tools or concepts remain within the realm of academic research. There is still a lack of an effective
DfRem tool commonly accepted and adopted by industry, which hinders the closing of the product
life cycle through remanufacturing [5].
Lack of sufficient market demand and core supply
“Cores” refer to the used products which are re-collected for remanufacturing. A lack of
understanding and negative perception of remanufacturing have limited market demand for
remanufactured products. When customers are not convinced of the quality of remanufactured
Appl. Sci. 2018,8, 1177 3 of 11
products, they expect remanufactured products to be sold at a lower price, which consequently reduces
the profit margin and discourages growth of the remanufacturing industry. Furthermore, due to
the current linear movement of product from the point of manufacture to customers and, ultimately,
to landfills, incineration, or material extraction, gaining access to EOL products and diverting qualified
ones to a remanufacturing facility also pose challenges to the growth of remanufacturing. There is a
need to change to circular or service-oriented models that can truly reverse the linear trend and assure
a supply of cores [4].
Skill/technology challenges and limited information sharing
Remanufacturing is a highly labor-intensive industry in comparison with traditional
manufacturing [
6
]. Many of the decisions made during the remanufacturing process are still dependent
on ad hoc engineering experience and thus require technically skilled engineers or technicians.
Meanwhile, there is still a need for the development of suitable nondestructive testing (NDT)
methods for repaired components or parts, which makes the qualification of remanufactured products
challenging. In addition to this, the lack of original design specifications and information on the
usage and repair history of the returned products further complicate the assessment of the viability of
core remanufacturing.
Even though progress towards the uptake of remanufacturing is still hampered by a few
challenges, Industry 4.0 reveals some powerful emerging trends that have the potential to address
these bottlenecks. While Industry 3.0 focused on the automation of a single machine and process,
Industry 4.0 pushes towards end-to-end digitalization of all physical assets and the entire supply
chain [
7
]. The rise of Industry 4.0 is leading to the drastic and rapid growth of data volume (Big Data),
driven by:
The availability of computing power and connectivity,
The advancement of analytic capability (e.g., artificial intelligence),
The introduction of new patterns of human and machine interaction (e.g., augmented reality
systems), and
The advent of technologies that ease the transformation of digital data into physical objects
(e.g., additive manufacturing and rapid prototyping).
When it comes to digitalization, Industry 4.0 possesses four main characteristics:
The vertical networking of small production systems, such as smart factories and smart products;
The horizontal integration of global value creation networks, such as new business and
cooperation models;
Through-engineering across the product design, product use, and EOL stages;
Exponential acceleration of technologies.
The fundamental idea of Industry 4.0 is to increase availability and integrated use of relevant
data by connecting all products, resources, and companies involved in the value chain and, ultimately,
to generate additional value from available data and to maximize customer benefit [
8
]. According to
a recent global industry survey [
7
], Industry 4.0 is no longer considered a “future trend” and for
many companies it is already the core of their strategy and research agenda. Around 40% of
companies surveyed reported that their vertical and horizontal value chains and through-engineering
model are already benefiting from an advanced level of digitization and integration; this number
is expected to increase to 75% within 5 years. Meanwhile, survey results suggested that global
industrial product companies would invest USD907 billion per year through to 2020 for digital
transformation. It is believed that the digitization of horizontal and vertical value chains, as well as the
product life cycle, will eventually revolutionize the product, manufacturing/remanufacturing industry,
and corresponding business models.
Appl. Sci. 2018,8, 1177 4 of 11
3. Smart Remanufacturing in the Digital Age
To pave the way towards a wider uptake of remanufacturing, the challenges discussed in
the previous section need to be addressed and properly resolved by OEM and independent
remanufacturers. Fortunately, the advent of Industry 4.0 has presented immense opportunities to
unlock the potential of remanufacturing. In this section, the opportunities that Industry 4.0 bring to
remanufacturing are discussed based on the three aspects previously mentioned, namely, Smart Life
Cycle Data for Design for Remanufacturing and EOL Management, Smart Factory for cost-effective
and green remanufacturing operations, and Smart Services for a successful remanufacturing business
model. Figure 1describes the three application areas and technical enablers from Industry 4.0.
The technology enablers are presented in the outer rim of the circle, and include smart sensors,
cloud computing, robotics, machine-to-machine communication (M2M), additive manufacturing,
and others.
Appl. Sci. 2018, 8, x FOR PEER REVIEW 4 of 10
3. Smart Remanufacturing in the Digital Age
To pave the way towards a wider uptake of remanufacturing, the challenges discussed in the
previous section need to be addressed and properly resolved by OEM and independent
remanufacturers. Fortunately, the advent of Industry 4.0 has presented immense opportunities to
unlock the potential of remanufacturing. In this section, the opportunities that Industry 4.0 bring to
remanufacturing are discussed based on the three aspects previously mentioned, namely, Smart Life
Cycle Data for Design for Remanufacturing and EOL Management, Smart Factory for cost-effective
and green remanufacturing operations, and Smart Services for a successful remanufacturing business
model. Figure 1 describes the three application areas and technical enablers from Industry 4.0. The
technology enablers are presented in the outer rim of the circle, and include smart sensors, cloud
computing, robotics, machine-to-machine communication (M2M), additive manufacturing, and others.
Figure 1. Opportunities from Industry 4.0 for remanufacturing, and its key enablers.
3.1. Smart Life Cycle Data
From the initial product design and development stage, and all the way until the EOL stage,
various product information is generated and captured. Ideally, this information should be shared
across the product life-cycle stakeholders to support product life-cycle management [5]. However,
the flow of product information remains essentially unestablished due to ineffective data extraction,
loss of information during product transfer between stakeholders, undeveloped platforms to support
information sharing, and other policy restrictions. Ineffectiveness of data circulation has reduced the
efficiency of product life-cycle management and the quality of service provided. For example,
incomplete information on returned cores remains a big challenge for the majority of remanufacturers
Figure 1. Opportunities from Industry 4.0 for remanufacturing, and its key enablers.
3.1. Smart Life Cycle Data
From the initial product design and development stage, and all the way until the EOL
stage, various product information is generated and captured. Ideally, this information should
be shared across the product life-cycle stakeholders to support product life-cycle management [
5
].
However, the flow of product information remains essentially unestablished due to ineffective data
extraction, loss of information during product transfer between stakeholders, undeveloped platforms
Appl. Sci. 2018,8, 1177 5 of 11
to support information sharing, and other policy restrictions. Ineffectiveness of data circulation
has reduced the efficiency of product life-cycle management and the quality of service provided.
For example, incomplete information on returned cores remains a big challenge for the majority of
remanufacturers [
5
]. In order to restore these products back to “good as new” quality, remanufacturers
have to recreate product knowledge which existed at the product design stage. In this regard, the digital
transformation of Industry 4.0 has shed some light on addressing this concern by improving data
transferability and building the knowledge/data-sharing platform. This could be enabled through
sensors, embedded systems, and connected devices (“Internet of Things”), as well as a comprehensive
data management platform. For example, when information regarding computer-aided design (CAD),
bill of materials, parts information, manufacturing and assembly instructions, data from product
use stage, and repair history information are stored in a central system and are easily accessible
by remanufacturers, the repair decisions during the remanufacturing process could be made easily
and operations could be carried out in a more efficient manner. Furthermore, when information,
such as product failure modes and rates, replacement frequency, cleaning efficiency, disassembly
challenges, and upgrading challenges, is extracted effectively from the remanufacturing stage and fed
back to product designers, many of the barriers occurring during the remanufacturing process could
be avoided in the next generation of products by incorporating proper design features [
9
12
]. This is
strategically important and a substantial cost-saving measure, as more than 70% of product costs are
determined at the product development stage [13].
3.2. Smart Factory
Due to the uncertainty involved in the number and quality of cores returned, remanufacturing
operations need to incorporate a high degree of flexibility to react quickly and appropriately to
various product reconditioning requirements. Smart factories, which enable high flexibility and small
batch size production, seamlessly address this complexity issue associated with remanufacturing
operations. “Smart factories” are essentially at the core of Industry 4.0. “Smartness” is achieved
using electronic hardware/software, as well as networking of production resources. Compared
with traditional manufacturing, more ancillary hardware and software, like RFID tags, barcodes,
laser markers, sensors, as well as communication infrastructure, will be embedded into the factory to
enable machines to collaborate with each other using intelligent analytics. In the future, in a smart
remanufacturing environment, machines could obtain incoming core information through scanning
a barcode attached to the core, adapt the remanufacturing operations through self-optimization and
smart fixturing capabilities, update the process-related information to a database via wireless transfer,
and store remanufacturing knowledge gained from experience. This could enable a substantial
reduction of the labor force and lessen the dependency on high-skilled operators. In the meanwhile,
with various types of sensors embedded into equipment and cells, data from the manufacturing
process could be retrieved and sent for real-time analysis. This would support the early detection of
machines or cell failures and allow preventative strategies to be implemented to avoid unplanned
maintenance and catastrophic failures. Information associated with the product manufacturing
data could also been recorded and stored as part of the product life-cycle data. In addition,
energy-efficient remanufacturing processes could also be achieved through collecting real-time energy
consumption data and implementing an energy management system accordingly. Further innovative
technologies, such as additive manufacturing, 3D scanning, automated guided vehicles, inspection
drones, hybrid manufacturing/process [
14
,
15
], and augmented reality tools, will continuously drive
down the cost of remanufacturing operations while also delivering substantial improvements in the
quality of the repaired product. Taking aerospace remanufacturing as an illustration, worn airfoils
can sometimes be repaired using the laser metal deposition technique, an additive process where
metal powder is melted by a computer numerical control (CNC) laser on a robot arm to form a
direct fusion-bonded deposit on the blades. This technology offers the precision and low heat input
necessary to successfully achieve such restoration, compared to more conventional fusion welding
Appl. Sci. 2018,8, 1177 6 of 11
processes. In addition, sustainability needs to be an important factor to be considered while adopting
these advanced technologies. The life-cycle assessment method could be conducted to gauge the
environmental impact of these new technologies to make sure the advancement is not only technically
achieved but is also environmentally friendly [16].
3.3. Smart Services
One of the challenges that remanufacturers face, as explained earlier, is the control of the
timing, quality, and quantity of cores returned. In this regard, the product service system has
provided opportunities to cope with the complexity of core return for remanufacturing [
17
]. In this
emerging and disruptive business model, ownership of the product is usually retained by the OEMs
or retailers and only the service or usage is offered to customers (e.g., selling “flying hours of the
engine” instead of selling “engines”). Hence, it creates a mandate for manufacturers or retailers to
monitor their product’s performance during its runtime and to forecast remanufacturing operations
on the cores returned based on the predicted remaining life of the product. On the other hand,
from the consumers’ perspective, as they pay for the service rather than the ownership of the
product, market acceptance for remanufactured goods will likely be increased, leading to a successful
remanufacturing model. Real-time monitoring of product in-use and data analysis via embedded
sensor networks and cloud-based computation could enable predictive maintenance by the early
detection of problems. Increased connectivity among products, customers, and manufacturers,
promoted by Industry 4.0, presents immense opportunities for boosting the product service model.
Take, for example, machines that are leased to the power plant sector. Power plants rely heavily on
the continuous availability of their machinery. To increase the reliability of machines, smart sensors
are embedded into the machines to monitor critical factors, such as temperature, pressure, switches,
energy consumption, and vibration in real time [
18
]. Sensor data are collected and logged into a central
server through a network for prediction of potential wear and estimation of components’ useful life,
and for scheduling maintenance or remanufacturing of components in a timely manner.
4. Case Illustrations
As mentioned previously, traditional remanufacturing is hindered by low process efficiency
and data silos among various product life-cycle stages. In this section, we present two
case studies to demonstrate how Industry 4.0 helps traditional remanufacturing achieve better
connectivity between machines through seamless data flow and real-time data capturing for condition
monitoring, respectively.
4.1. Smart Repair Cell
Currently, most remanufacturing tasks are highly labor intensive, as returned cores vary greatly
in terms of their form and condition. Even though human intelligence is capable of adapting to such
variations and performing adaptive manual repair tasks, dealing with variability in the part profile
and form accuracy of each core still remains challenging for the remanufacturing process. This is
particularly a concern for aerospace part repair, as the sector has stringent requirements for compliance
to the original part specifications.
To achieve consistency and high flexibility in remanufacturing operations, a smart
remanufacturing cell, with special focus on repair operations, has been simulated at the Advanced
Remanufacturing and Technology Centre (ARTC). The smart cell, as graphically described in Figure 2,
comprises the following layers:
Shared database—in this layer, all virtual data pertaining to every individual repaired component
are stored and organized based on the design and existing repair information. By organizing
the repair data, backtracking the core’s repair process history and failure information is a
well-established process.
Appl. Sci. 2018,8, 1177 7 of 11
Software platform—this layer defines all the software tasks related to the data flow and
machine-to-machine communication channels. With a common software platform, high-speed
computations are carried out both in real time and offline, helping seamless data transfer between
machines without any loss in data quality.
Physical process—this layer of the smart cell defines the actual repair tasks to be carried out on
the core. Since the process requires physical transfer of the component and its storage container
across the actual repair processes, it relies heavily on factory automation to conform to Industry
4.0 standards.
In standard manufacturing protocols, design of the component using computer-aided design
(CAD) precedes part manufacturing. Yet, in the remanufacturing process, the type and level of damage
to the core will affect repair decisions. Some of the damage modes include, but are not limited to:
cracking, wear, physical distortion, high-temperature damage, surface corrosion, etc. Upon entering
the cell, cores will be registered in the shared database using identification markers during a process
termed “Part Registration”. This process provides the system with core information, such as original
part number, serial number, raw material identification, bill of materials (if it is an assembly), OEM CAD
design information, and repair history.
Once cores are registered, a single/series of repair processes will be planned for each core in
a sequence suitable for its identification and condition, based on established standard OEM repair
protocols or other approved methods. The repair sequence will involve the utilization of several
machines and manufacturing processes, such as brazing, thermal/cold spray, and laser cladding to
regenerate the damaged area of the core. Process-related information will be updated in the database
via wireless transfer and will constitute the digital “life-story” of the parts. This registration process is
crucial in product life-cycle management (PLM) to improve the product design in the future and to
ensure an appropriate decision-making process at the next remanufacturing cycle.
Appl. Sci. 2018, 8, x FOR PEER REVIEW 7 of 10
In standard manufacturing protocols, design of the component using computer-aided design
(CAD) precedes part manufacturing. Yet, in the remanufacturing process, the type and level of
damage to the core will affect repair decisions. Some of the damage modes include, but are not limited
to: cracking, wear, physical distortion, high-temperature damage, surface corrosion, etc. Upon
entering the cell, cores will be registered in the shared database using identification markers during
a process termed Part Registration”. This process provides the system with core information, such as
original part number, serial number, raw material identification, bill of materials (if it is an assembly),
OEM CAD design information, and repair history.
Once cores are registered, a single/series of repair processes will be planned for each core in a
sequence suitable for its identification and condition, based on established standard OEM repair
protocols or other approved methods. The repair sequence will involve the utilization of several
machines and manufacturing processes, such as brazing, thermal/cold spray, and laser cladding to
regenerate the damaged area of the core. Process-related information will be updated in the database
via wireless transfer and will constitute the digital “life-story” of the parts. This registration process
is crucial in product life-cycle management (PLM) to improve the product design in the future and
to ensure an appropriate decision-making process at the next remanufacturing cycle.
Figure 2. Smart remanufacturing cell-process flow.
As most of the repair processes result in addition of material to the parts, further machining
processes need to be carried out to bring the repaired component back to its original dimensions. This
is a critical stage of the repair process, as it will determine the accuracy and tolerances of the final
repaired part. To generate the optimum tool path, automated adaptive machining is adopted. Firstly,
the as-received physical dimensions of the core need to be defined in 3D digital format. This could be
realized using various three-dimensional scanning methods, such as laser triangulation, structured
light, probing methods, etc., to create the digital twin of the core. The scanned output is stored in the
shared database, usually in point-cloud form. Secondly, the scanned data is compared with the original
digital design of the part (computer-aided modelCAD), also called golden sample/nominal data, to
obtain the geometry difference, using reverse engineering software. Thirdly, on-machine probing or a
scanning method is carried out to measure the information on the machine coordinate system (MCS)
and to align the repaired part with respect to the machine datum. Lastly, via the developed machine-
to-machine communication software platform, geometry differences and the MCS are fed into
computer-aided manufacturing (CAM) software for the adaptive tool-path generation. The tool path
generated based on this aligned datum is stored in the shared database layer as a numerical control
(NC) file. Following the additive repair processes, when a part is ready for final machining, the NC file
is retrieved from the database to be used for machining. Various machining processes, such as 5-axis
milling, turning, grinding, abrasive polishing, etc., are carried out to achieve the final dimensions on
the repaired component. Machining-related information is also stored in the database as part of the
product’s digital “life-story”. The real-time monitoring of the machining data plays a key role in
Figure 2. Smart remanufacturing cell-process flow.
As most of the repair processes result in addition of material to the parts, further machining
processes need to be carried out to bring the repaired component back to its original dimensions.
This is a critical stage of the repair process, as it will determine the accuracy and tolerances of
the final repaired part. To generate the optimum tool path, automated adaptive machining is
adopted. Firstly, the as-received physical dimensions of the core need to be defined in 3D digital
format. This could be realized using various three-dimensional scanning methods, such as laser
triangulation, structured light, probing methods, etc., to create the digital twin of the core. The scanned
Appl. Sci. 2018,8, 1177 8 of 11
output is stored in the shared database, usually in point-cloud form. Secondly, the scanned data is
compared with the original digital design of the part (computer-aided model—CAD), also called
golden sample/nominal data, to obtain the geometry difference, using reverse engineering software.
Thirdly, on-machine probing or a scanning method is carried out to measure the information on the
machine coordinate system (MCS) and to align the repaired part with respect to the machine datum.
Lastly, via the developed machine-to-machine communication software platform, geometry differences
and the MCS are fed into computer-aided manufacturing (CAM) software for the adaptive tool-path
generation. The tool path generated based on this aligned datum is stored in the shared database layer
as a numerical control (NC) file. Following the additive repair processes, when a part is ready for final
machining, the NC file is retrieved from the database to be used for machining. Various machining
processes, such as 5-axis milling, turning, grinding, abrasive polishing, etc., are carried out to achieve
the final dimensions on the repaired component. Machining-related information is also stored in the
database as part of the product’s digital “life-story”. The real-time monitoring of the machining data
plays a key role in defining the surface integrity of the machined parts. In the subsequent case study,
the effect of sensor integration on machine tools and its impact on Industry 4.0 is discussed in detail.
In addition, thanks to the advancement of machine tool technology, both additive and subtractive
processes can be performed with one single machine. Hybrid machines—combining both laser metal
deposition blown powder and 5-axis milling in one machine—are now commercially available for use
in production. This provides the benefit of single-machine and single-work setup, hence improving
the volumetric accuracy of the final repaired component. In addition, the cost of multiple machines,
corresponding part transfer automation, and the space requirement are reduced substantially.
Smart repair cells are designed to be an effective way of repairing each component adaptively
with utmost accuracy and tolerance due to seamless information flow across the processes. Currently,
a smart repair cell concept is simulated at ARTC using various scenarios such as number of machine
tools, robotic automation vs manual part transfer, and volume of cores. Based on the simulated results,
the best operational efficiency is calculated to select the right hardware combinations. Know-how in
reverse engineering, adaptive toolpath development, and individual repair processes have already been
embedded into the smart repair cell. Ongoing work at ARTC is focused on building up the database
based on number of cores, real-time machine tool monitoring, and the final remanufactured part
conformance data, which will eventually help machine learning for automatic repair decision making.
It should be noted that the above-mentioned remanufacturing cell focuses mainly on the adaptive
repair process. Disassembly, cleaning, re-assembly, and, most critically, the NDT process for analyzing
issues related to adhesion of the repaired material, change of material structure, porosity, inclusions, etc.
are not addressed in this paper.
4.2. Sensorized Machines for Intelligent Machining
Machine tools could become increasingly smart by perceiving their own states and the state
of the surrounding environment, which is considered as “intelligent machining”. Key enablers for
this capability include smart sensors embedded into machine systems, a data acquisition system
which can transmit the processing data quickly without data loss, and also the intelligent central
system which receives and analyzes the data. Such an intelligent machining system is of great
importance for evaluating the system’s health and to enable prediction of a possible breakdown or
malfunction before it happens. Currently, advanced condition-based maintenance is triggered when
an asset reaches a predefined unacceptable level detected by the sensoring system. These thresholds
usually originate from experts’ experiences (machine operators) or manufacturers’ recommendations.
However, when multiple complicating factors coexist, false alarms might be triggered. To resolve such
complications, sensors and advanced computer systems will be used in tandem during intelligent
machining to monitor the system health. Both historical and real-time sensor data will be used in the
evaluation of the machine condition via artificial intelligence techniques to substantially increase the
Appl. Sci. 2018,8, 1177 9 of 11
prediction accuracy and the quality of the produced part. This work is now being carried out by a
research group in ARTC.
A large number of sensing devices, including vibration sensors, current sensors, temperature
sensors, and acoustic emission sensors, have been installed into various critical subsystems of a
computer numerical control (CNC) machine, such as spindles, linear guides, ball screws, cutting tools,
etc. All the sensors are linked to three data acquisition (DAQ) systems. Each DAQ system performs
data acquisition and storage. The data are used for analyzing the correlation between machine health
and the quality of the product selected to be machined. Sensor data are captured and recorded in the
database during the process of machining the product, which is, in our case, a carburized steel shaft.
As modern machines are quite robust, it may take months or years before detectable changes in
machine operation and performance can be observed. Sensors can register small changes indicating
the beginning of degradation in machine components or subsystems. However, it is not guaranteed
that such changes will affect the quality of machined products. Therefore, it is difficult to determine
the remaining useful life of a whole machine system or subsystem based on one-time measurement
without access to historical data. In order to obtain the critical and more relevant machine data,
different levels of failure modes, such as tool wear, tailstock misalignment, and spindle unbalance,
were simulated. At the same time, changes in sensor data collected during experiments to simulate
different levels of machine failure modes, as well as the measurement of part quality, including surface
roughness, waviness, dimensions, and out-of-roundness, have been captured and documented in order
to search for correlations between the sensor signals, deterioration of machine subsystems, and quality
of machined products, as shown in Figure 3. To date, a total of 190 GB of data have been collected
during the machining of 45 shafts under the different simulated failure modes. Work is ongoing to
analyze these data and to generate a model for machine failure prediction.
Appl. Sci. 2018, 8, x FOR PEER REVIEW 9 of 10
were simulated. At the same time, changes in sensor data collected during experiments to simulate
different levels of machine failure modes, as well as the measurement of part quality, including
surface roughness, waviness, dimensions, and out-of-roundness, have been captured and
documented in order to search for correlations between the sensor signals, deterioration of machine
subsystems, and quality of machined products, as shown in Figure 3. To date, a total of 190 GB of
data have been collected during the machining of 45 shafts under the different simulated failure
modes. Work is ongoing to analyze these data and to generate a model for machine failure prediction.
Figure 3. Correlation between machine and process condition, sensor data, and quality of machined
products.
The purpose of developing intelligent machining capability is to be able to monitor machine
condition and part quality, to predict the need for maintenance, and to avoid costly failure and the
need for part rework. In the meanwhile, data captured will also form part of a product’s “digital life-
story”, which will be of great use for supporting product end-of-life decision making and analyzing
the cause of product failure. Additionally, the ability to retrofit older machines with smart sensors
and the data acquisition system to achieve intelligent machining is of paramount importance, as not
all companies can afford to purchase new modern machinery to enjoy the benefits of the sensor
system. The capability of retrofitting an existing machine with an advanced sensor system has been
developed and demonstrated through this current research work.
5. Conclusions
In this paper, some of the opportunities that Industry 4.0 bring to the remanufacturing industry
are discussed and presented from the perspectives of “Smart Life Cycle Data”, “Smart Factory”, and
“Smart Service”. It is observed that increased digitalization across the supply chain and enhanced cyber-
physical intelligence within the factory have effectively addressed several major concerns that
remanufacturers encounter. This will potentially reduce the cost of transformation in remanufacturing.
A smart remanufacturing cell, which provides tailor-made repair operations based on incoming
core conditions, is described in the case study section to showcase technology enablers from Industry
4.0 supporting the smart remanufacturing process. Smart sensors which are embedded into the CNC
machine enable the real-time monitoring of machine health and call for maintenance or component
remanufacturing in a preventative manner. Future work could also look at the economic analysis to justify
the viability and profitability of utilizing innovative technologies for a smart remanufacturing system.
Author Contributions: Conceptualization, S.Y., A.R.M.R. and J.K.; Methodology, S.Y.; Supervision, H.P.;
Writing-original draft, S.Y., A.R.M.R. and J.K.; Writing-review & editing, S.Y., A.R.M.R., J.K. and H.P.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
Figure 3.
Correlation between machine and process condition, sensor data, and quality of
machined products.
The purpose of developing intelligent machining capability is to be able to monitor machine
condition and part quality, to predict the need for maintenance, and to avoid costly failure and the
need for part rework. In the meanwhile, data captured will also form part of a product’s “digital
life-story”, which will be of great use for supporting product end-of-life decision making and analyzing
the cause of product failure. Additionally, the ability to retrofit older machines with smart sensors and
the data acquisition system to achieve intelligent machining is of paramount importance, as not all
companies can afford to purchase new modern machinery to enjoy the benefits of the sensor system.
The capability of retrofitting an existing machine with an advanced sensor system has been developed
and demonstrated through this current research work.
Appl. Sci. 2018,8, 1177 10 of 11
5. Conclusions
In this paper, some of the opportunities that Industry 4.0 bring to the remanufacturing industry
are discussed and presented from the perspectives of “Smart Life Cycle Data”, “Smart Factory”,
and “Smart Service”. It is observed that increased digitalization across the supply chain and enhanced
cyber-physical intelligence within the factory have effectively addressed several major concerns that
remanufacturers encounter. This will potentially reduce the cost of transformation in remanufacturing.
A smart remanufacturing cell, which provides tailor-made repair operations based on incoming
core conditions, is described in the case study section to showcase technology enablers from Industry
4.0 supporting the smart remanufacturing process. Smart sensors which are embedded into the
CNC machine enable the real-time monitoring of machine health and call for maintenance or
component remanufacturing in a preventative manner. Future work could also look at the economic
analysis to justify the viability and profitability of utilizing innovative technologies for a smart
remanufacturing system.
Author Contributions:
Conceptualization, S.Y., A.R.M.R. and J.K.; Methodology, S.Y.; Supervision, H.P.;
Writing-original draft, S.Y., A.R.M.R. and J.K.; Writing-review & editing, S.Y., A.R.M.R., J.K. and H.P.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
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©
2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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A growing concern about the environment, and especially about waste and landfill, has spurred research into the design of more environmentally benign products. A dramatic reduction in environmental impact can be made by product remanufacturing in which, in contrast to material recycling, the geometrical form of the product is retained and its associated economical and environmental value preserved. Our long term goal is to postulate and validate design metrics which effectively and efficiently measure the remanufacturability of given designs. The principal goal in this paper is to identify design characteristics which facilitate remanufacturing. This is accomplished by addressing the principal driving factors for remanufacturing, as well as identifying existing remanufacturing guidelines, philosophies, and practices. This information is leveraged and evaluated by investigating the remanufacturability of a product which is not currently remanufactured — an automobile door. From this, some illustrative design changes which increase the remanufacturability of the automobile door are proposed.
Book
Design for Manufacturability: How to Use Concurrent Engineering to Rapidly Develop Low-Cost, High-Quality Products for Lean Production shows how to use concurrent engineering teams to design products for all aspects of manufacturing with the lowest cost, the highest quality, and the quickest time to stable production. Extending the concepts of design for manufacturability to an advanced product development model, the book explains how to simultaneously make major improvements in all these product development goals, while enabling effective implementation of Lean Production and quality programs. Illustrating how to make the most of lessons learned from previous projects, the book proposes numerous improvements to current product development practices, education, and management. It outlines effective procedures to standardize parts and materials, save time and money with off-the-shelf parts, and implement a standardization program. It also spells out how to work with the purchasing department early on to select parts and materials that maximize quality and availability while minimizing part lead-times and ensuring desired functionality. Describes how to design families of products for Lean Production, build-to-order, and mass customization Emphasizes the importance of quantifying all product and overhead costs and then provides easy ways to quantify total cost Details dozens of design guidelines for product design, including assembly, fastening, test, repair, and maintenance Presents numerous design guidelines for designing parts for manufacturability Shows how to design in quality and reliability with many quality guidelines and sections on mistake-proofing (poka-yoke) Describing how to design parts for optimal manufacturability and compatibility with factory processes, the book provides a big picture perspective that emphasizes designing for the lowest total cost and time to stable production. After reading this book you will understand how to reduce total costs, ramp up quickly to volume production without delays or extra cost, and be able to scale up production rapidly so as not to limit growth.
Article
Nowadays, environmentally friendly cutting fluids should be applied in manufacturing processes. Concretely, in many machining processes the use of minimum quantity of lubrication (MQL) is established as common routine. In this paper, the use of natural biodegradable oils as an alternative to traditional canola oils used in MQL is analyzed, comparing five different alternatives (i.e.: sunflower oil, high oleic sunflower oil, castor oil and ECO-350 recycled oil). Firstly, the aim is to evaluate the feasibility of the proposed oils, just analyzing their tribological and rheological characteristics. Secondly, a full battery of experimental tests was performed in order to validate their behavior during cutting Inconel 718, a difficult-to-cut material commonly used in aeronautical components. Finally, life cycle assessment was carried out with the aim of analyzing their environmental feasibility. Results show that the combination of low viscosity with high friction coefficient implies an increase in tool life. In this line, ECO-350 recycled oil presents a feasible behavior and improves the tool life up to 30% in comparison with commercial canola oil. However, its recycling process has to be improved from an environmental point of view. Taking both issues, high oleic sunflower oil presents a balance between them. On one hand, it improves tool life (15%) and on the other hand, similar environmental impact is obtained compared with canola oil. This supposes an advance, being more efficiently, not only environmentally speaking but also technically.
Chapter
The aspect of monitoring key performance and maintenance data in technological products becomes increasingly relevant as the necessary sensor and IT technology develops rapidly. Nevertheless, there are conditions under which the integration of a sensor system and the interpretation of the data can be counterproductive and in an extreme case lead to a dilution of the relationship between a supplier and its customer. Within the paper the consequences of offering a Condition Monitoring System in the case of Industrial Product Service Systems are analysed, resulting from an extensive literature review and first in-depth interviews with managers from the manufacturing sector.
Article
Remanufacturing is a concept of strategic importance that enables a significant part of the value added to a product during its initial production to be retained. It is no longer acceptable to dispose continually of strategically important materials in landfill sites or waste the energy associated with their processing. Remanufacturing is the process by which used products and assemblies are returned to their new state with minimum waste and expenditure on materials and energy. Parts that do not wear out are reused in a rebuilt product that incorporates the technological advances deemed necessary to ensure that repairs can be carried out in a timely manner and the item returned to functionality in an efficient manner. This review provides an overview of how remanufacturing reduces industrial costs. Its strategic importance is stressed and examples are given to illustrate how the concept is applied in a wide variety of industries.