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KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT
KTH ROYAL INSTITUTE OF TECHNOLOGY
SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT
DEGREE PROJECT IN MECHANICAL ENGINEERING,
SECOND CYCLE, 30 CREDITS
STOCKHOLM, SWEDEN 2021
Real time monitoring of machining
process and data gathering for
digital twin optimization
AJITH RAJENDRAN
GAUTHAM ASOKAN
Real time monitoring of machining process and data gathering for Digital Twin Optimization
AJITH RAJENDRAN
GAUTHAM ASOKAN
KTH Supervisor: Tomas Osterlind
Nytt Supervisor: Bharat Sharma
KTH Examiner: Amir Rashid
© AJITH RAJENDRAN, GAUTHAM ASOKAN, 2021.
Department of Production Engineering and Management
School of Industrial Engineering and Management
KTH ROYAL INSTITUTE OF TECHNOLOGY
Stockholm, Sweden 2021
Acknowledgements
We would like to extend our gratitude to Sture, CEO Nytt AB for giving us this thesis opportunity.
The entire team at Nytt AB has been supportive and providing constructive feedback throughout
the course of this thesis, for which we would like to show our appreciation. A special mention to
our thesis supervisor from Nytt, Bharat Sharma for being really encouraging and supportive right
from the very beginning. He has provided us with a lot of information, feedback and had given
us the freedom to contact him when required even during the weekends and we are sure that the
process would have been a lot complicated without his time and guidance.
We owe a big part of our successful completion of the thesis to our KTH supervisor Tomas
Osterlind, Assistant Professor from the Department of Production Engineering at KTH Royal
Institute of Technology. He has been guiding us and giving vital information and feedback at
every instance required and has been a huge technical support given the expert he is in monitoring
machine tools using sensor technology. We are very thankful to him for having accepted our
request to be our supervisor and extending his help and guidance throughout the thesis period.
We would also like to express our gratitude to the PHD candidates Theodoros Laspas, Nikolas
Alexander Thiessen and Karoly Szipka for their help and technical support whenever we had
reached out to them.
A special thanks to our good friend, PHD candidate Jordi Altayo from the Department of EECS
at KTH for providing technical guidance and support during the sensor selection phase.
We also would like to thank CR Magnetics. Considering the fact that it is for educational purposes,
they had provided us the sensor for no cost, which we feel is a great gesture from them.
Finally, we would like to thank all the technical assistants, operators in the shop floor both at
KTH and Precima for aiding us during the experimentation.
Table of contents
ABSTRACT ........................................................................................................................................................ 6
1. Introduction ............................................................................................................................................... 7
1.1. Company Introduction ......................................................................................................................... 7
1.2. Background and Motivation ............................................................................................................... 8
1.3. Aim ........................................................................................................................................................... 8
1.4. Delimitations .......................................................................................................................................... 8
2. Theoretical Background ......................................................................................................................... 9
2.1. Monitoring systems and framework ............................................................................................ 9
2.2. Sensors .............................................................................................................................................. 11
2.2.1. Force sensors .......................................................................................................................... 13
2.2.2. Vibration Sensors ................................................................................................................... 14
2.2.3. Acoustic Emissions (AE) sensors ...................................................................................... 15
2.2.4. Current Sensors ...................................................................................................................... 16
2.3. Data Acquisition ............................................................................................................................ 17
2.4. Predictive maintenance ................................................................................................................ 19
3. Experimentation ..................................................................................................................................... 20
3.1. Overview of the setup .................................................................................................................... 20
3.2. Manual Lathe Turning .................................................................................................................. 21
3.3. Precima ............................................................................................................................................ 22
3.4. Hermle .............................................................................................................................................. 27
4. Discussion ................................................................................................................................................ 29
5. Conclusions ................................................................................................................................................. 30
6. Future Work ................................................................................................................................................. 31
6.1. Hardware Requirements .................................................................................................................... 31
6.2. Process optimization capabilities .................................................................................................... 31
Appendix .......................................................................................................................................................... 32
References ........................................................................................................................................................ 38
Table of Figures
Figure 1 Nytt AB Product Specifications (Source: Nytt-Tech.com) ............................................................... 8 Figure 2 Basic CNC Monitoring System Architecture ................................................................................... 10 Figure 3 Process Monitoring Architecture and Components (Source: (Cai et al., 2017)) ........................... 11 Figure 4 Components of REALISM monitoring system ............................................................................... 11 Figure 5 Placement of sensors in a machine tool for monitoring cutting tool condition (Bhuiyan &
Choudhury, 2014) .............................................................................................................................................. 13 Figure 6 Construction of a piezoelectric sensor (Thomas Kleckers, n.d.).................................................... 14 Figure 7 IEPE accelerometer ........................................................................................................................... 15 Figure 8 (a) Design of an AE sensor (b) Example of an Industrial AE sensor ........................................... 16 Figure 9 Construction of Hall effect sensor ................................................................................................... 16 Figure 10 Split core Hall effect sensor by LEM ............................................................................................. 17 Figure 11 Resolution- 16-bit vs 3-bit ............................................................................................................... 18 Figure 12 DAQ Hardware with Various Input Modules ............................................................................... 19 Figure 13 Machine tool monitoring setup ....................................................................................................... 21 Figure 14 LEM APR B-10 and CR Magnetics CR4111s sensors(LEM, n.d.)(CR Magnetics, n.d.) ............ 21 Figure 15 Sensors clamped to spindle wire and the workpiece used ............................................................ 22 Figure 16 Data captured during the test on lathe ........................................................................................... 22 Figure 17 Blank and finished part .................................................................................................................... 23 Figure 18 Setup and CT sensor clamped to main and sub-spindle drive wires in the controller box. ....... 23 Figure 19 Visualization of sub-spindle current consumption data captured during a day .......................... 24 Figure 20 Visualization of the main spindle current consumption data captured during a day ................. 25 Figure 21 Combined current consumption from the main and the sub-spindle for a short period ........... 25 Figure 22 Graphical representation of current consumption during one cycle ........................................... 26 Figure 23 One cycle current consumption recorded at Precima along with tool identification and operation
times ................................................................................................................................................................... 30 Figure 24 Day 2 sub-spindle current consumption data at Precima ............................................................. 32 Figure 25 Day 3 sub-spindle current consumption data at Precima ............................................................. 33 Figure 26 Day 4 sub-spindle current consumption data at Precima ............................................................. 34 Figure 27 Day 5 sub-spindle current consumption data at Precima ............................................................. 35 Figure 28 Day 6 sub-spindle current consumption data at Precima ............................................................. 36 Figure 29 Day 7 sub-spindle current consumption data at Precima ............................................................. 37 Figure 30 MTconnect data collected from Precima ....................................................................................... 37
ABSTRACT
In the development stages of a Digital twin of production assets, especially machine tools, real
time process monitoring and data gathering proves to be vital. Having a monitoring system that
monitors and updates the operators or managers in real time, helps improve productivity in terms
of reducing downtime through predictive/preventive analytics and by incorporating in process
quality assessment capabilities. When it comes to Real time monitoring of machine tools and
processes, sensor technologies have proven to be the most effective and widely researched. Years
of research and development have paved the way for many smart sensor technologies that come
both inbuilt with the machine tools as well as external applications. However, these technologies
prove to be expensive and complicated to implement especially for Small and Medium
Enterprises. This thesis focuses on evaluating and testing a simple, cost-efficient monitoring
system using inexpensive sensor technologies that would help optimize an existing Digital twin
setup for machine tools for Small and Medium Enterprises. Experiments with a 5 axis CNC
machine tool using different tools and varying operating parameters, materials were performed,
and the relevant sensor data were collected, mapped, analysed for accuracy and benchmarking.
The thesis also evaluates the integration of this data with the information already collected from
other sources, improve existing data reliability, and provides guidelines on this could be
transformed usefully to create more value to SME’s.
Keywords: Digital Twin, Sensor Technology, Machine tool Monitoring, Small and Medium
Enterprises, Benchmarking, Data gathering, Data reliability.
1. Introduction
As an integral part in any Digitalizing of Manufacturing systems Real time monitoring and data
gathering of manufacturing processes not only enhance the performance and reduce downtimes,
but also help incorporate in process quality checks that help improve the overall product and
process quality. This Master thesis performed with Nytt AB focuses on developing one such
monitoring system using sensor technology to enhance an existing Digital twin setup for machine
tools and to improve and verify the reliability of the data gathered.
1.1. Company Introduction Nytt AB is a new start-up company founded in 2018. Nytt’s focus is to make the operators drive
the process of information flow and provide a solution that is simple and intuitive. Their solution
primarily consists of the smartphones, the opapp that is installed in the smartphones through
which the operators can communicate and drive the flow of information and the dashboard that
the managers can use to monitor the entire production. Nytt AB uses a simple and ingenious
solution of using a common day to day appliance like smartphones to monitor and gather vital
data about the status of the machine tool. The basic idea behind this solution is to improve
information flow across all levels of the company. With their solution targeted towards SMEs,
Nytt AB wants to capitalize on the experience of the operators. By using trained smartphone
cameras to monitor the stack lights (as shown in Figure 2- on the right), vital information about
the status of the machines is gathered, and live feedback and information are provided on an
intuitive and informative dashboard. These smartphones that are used to monitor the stack lights
have an ‘opapp’ installed in them, which basically shows all the information gathered by the
smartphone cameras. The opapp also provides the platform for the operators operating the
machine to view and add information in the form of fault reports, maintenance reports, operator
assignment and a lot more. The dashboard user gets access to information like machine status,
production status, operator information, OEE (Overall equipment effectiveness) analytics ,
feedback, and escalation along with some task and assignment features.
Nytt AB has been testing the solution in four CNC machines from a partnership with PRECIMA
PRODUKTION AB. There are also other partnership collaborations running with three potential
customers who have been studied in the development phase as the application is not completely
launched in the market.
Figure 1 Nytt AB Product Specifications (Source: Nytt-Tech.com)
1.2. Background and Motivation Currently the customers of Nytt AB are provided with only descriptive analytics or an insight into
the past and the effects of the decisions that were taken. The current level of digitalization of
Nytt’s solution is somewhere between the second and the third stage i.e, there is minimal
interaction between the physical and digital systems. To have a good and sustained market, Nytt
must focus on improving the interaction between the physical and digital versions and try to
implement preventive and predictive analytics. However, this requires large amounts of data,
benchmarking and additional sources of data capturing other than just the stack lights. This also
improves reliability and allows for more information and value to be provided to the customers.
Having additional data gathering sources like independent sensors to monitor machine parameters
and auxiliary devices can provide real time monitoring of the process and help incorporate process
quality and preventive maintenance through predictive and prescriptive analytics. Hence, this
thesis was formulated to evaluate the impact of having additional data gathering sources like
sensors on the existing Digital twin and data gathering solution at hand.
1.3. Aim The initial aim of the thesis is to evaluate and test real-time monitoring and additional data
gathering of various machining processes to monitor the machine tool and thereby optimize its
Digital twin. This gives way to the first research question:
RQ1. What sensor technologies can be used for Real time machine tool monitoring and how can it be
used to enhance an existing Digital Twin setup?
To answer RQ1, a theoretical research methodology was adopted, and various sensor technologies
were studied and compared. Once the right sensor technologies were identified based on different
criteria, they were implemented in a machine tool to gather data and validate the results. Thus,
the second research question is as follows:
RQ2: Can the Hall -Effect True RMS AC Current Transducer be used in identifying and monitoring
different machining processes and in turn the machine tool itself?
1.4. Delimitations This master thesis focusses on providing a real time monitoring system for small and medium
enterprises that do not have access to expensive machine tools that come with inbuilt advanced
and precise monitoring technologies. Hence, the sensor technologies like the Hall Effect True
RMS AC current transducer and the other components used in this thesis are chosen with factors
like cost, simplicity, and ease of implementation in mind. Smart sensors and expensive machine
tools having inbuilt monitoring technologies might produce better and accurate results. The
measured data from the sensor system implemented might not be accurate, but they provide some
sort of benchmarking with which it is possible to monitor the Machine tools. Also, it is not the
purpose of this thesis to have a highly accurate data gathering system, but it is more focussed
towards helping the company Nytt AB enhance their digital twin setup and provide more value to
its customers. The experiments within this thesis were planned to include different processes,
materials, and parameters, however still a lot of future work and data gathering will have to be
done for optimized results.
2. Theoretical Background
In the past decade, Digital/Virtual Manufacturing and Production technologies have gained
massive interest amongst many companies and research companies. The benefits from
incorporating such technologies such as reduced errors, downtimes, machining times, improved
quality, safety in the working environment have been the major driving factors. When it comes to
development of Digital twins of machine tools and production assets, a large amount of research
work has been conducted in areas such as development of virtual machine tool systems, simulation
of machining capabilities, simulation of the complete machine tool model as well as simulation of
specific type of machining application (Cai et al., 2017). However, the intricacies involved in the
process and the machine tool itself, make it hard to exactly replicate the physical system and
interpret the reliability of the data and results of the simulations. A lot of research has also been
focussed on developing monitoring systems and technologies for machine tools that provide a
platform for improving data reliability for digital twin construction and optimization, preventive
and predictive maintenance, and machining error predictions. Sensor technologies have been the
most widely used and researched when it comes to monitoring machining processes and
parameters. Past research studies show that characteristics like Acoustic emissions, cutting forces,
torque, temperature, motor power and current, vibrations can be used as indicators to extract vital
information about the process (O’Sullivan, 2015). Technological advancements have paved the
way for machine tool manufacturers to provide inbuilt sensors that actively monitor the machine
health and the process parameters; however, they prove to be expensive. Hence, many researchers
have explored and tested different ways of acquiring and analysing the data that can be obtained
from machine tools. It is also therefore the aim of this thesis to provide a comparatively simple
and inexpensive machine tool monitoring and data acquisition solution using sensor technology.
The various sensor technologies and the frameworks that have been studied and tested in
monitoring of machine tools have been illustrated in detail in this chapter.
2.1. Monitoring systems and framework
Process monitoring systems, in general have been used for detecting tool wear and tool breakage
during machining. The effectiveness of such monitoring systems hugely depends on the analysing
and decision-making algorithms, complexities in operating, and the sensor choice [Integrated
Force sensor for drilling]. Also, the accuracy and reliability of the acquired data greatly depends
on the proximity of the sensors to the source which often adds to the complexity of the system.
Hence, developing a monitoring system for machine tools using sensor technology is often
complex and expensive. Huge amount of research has been conducted in developing process
monitoring systems however most of them are limited to monitoring the cutting process in. Sensor
driven monitoring systems developed for machining applications usually have one or more sensors
that a wider band of operating range and configurability to accommodate different tools, locations,
and operating conditions. These sensors are then coupled with Data Acquisition devices that
enable the user to interpret and obtain valuable data from the output of these sensors with the
help of a driver software. Figure 2 shows the basic framework/architecture for a monitoring system
at a top level.
Figure 2 Basic CNC Monitoring System Architecture
However, researchers have used and developed different systems based on the components
available to them and their requirement. Many of them have designed their own framework for
the data capturing and analysing phase, with neural networking and machining learning
approaches being the most common. (Cai et al., 2017) have even studied the possibility to
construct a digital twin by integrating additional sources of data like key machining parameters to
the sensory data. Figure 3 shows the system architecture used by them. By obtaining sensory data
from sensors and machine parameters from the gateway of the controller and using data fusion
techniques, the researchers were able to extract machining feature information and develop an
autonomous decision interface for monitoring the machine health.
Figure 3 Process Monitoring Architecture and Components (Source: (Cai et al., 2017))
(O’Sullivan, 2015), on the other hand, as shown in Figure 4 also developed a sensor fusion system
that enables multiple process parameter monitoring and provide feedback on the machine tool
health and performance. By integrating accelerometer, force and acoustic emission sensors on the
machine turret and vicinity, they were able to obtain vital information on process parameters and
develop a process monitoring learning loop through complex neural networking and Machine
learning approaches.
Figure 4 Components of REALISM monitoring system
2.2. Sensors
Sensors can be defined in numerous ways but generally they can be stated as a device that receives a stimulus and responds with an electrical signal. The stimulus is the quantity, property, or condition that is received and converted into electrical signal. The Sensors can be classified in both simple and complex ways. Firstly, they can be classified as Active and Passive. An active sensor requires an excitation signal i.e., an external power source for their operation whereas a passive sensor does not need any external power source. Secondly, based on the reference they
can be classified as absolute and relative. Absolute sensors detect a stimulus by referring to an absolute physical scale that does not depend on the measurement conditions, while a relative sensor generates a signal that relates to a specific case. Finally, they can be classified based on their characteristics and properties (Fraden, 2016) Sensors are widely used in machine tools for different applications, namely for monitoring, controlling, optimization, and the autonomous stage. In monitoring stage, the data from sensors are collected and being displayed to monitor the machine tool’s condition. The feedback provided is in turn used for controlling the machine. Most of the machine tools have both monitoring and controlling functions being used in practice. The optimization application is currently being studied where optimum parameters can be determined based on values measured by the sensor to achieve higher cutting efficiencies. Furthermore, machine tools performing operations with optimum cutting conditions autonomously by using AI technologies are also being explored. (Fujishima et al., 2016) Various types of sensor technologies are employed in machine tools for the above-mentioned applications. Error! Reference source not found. shows different sensing techniques used to monitor the tool condition along with the comparison of different parameters like type of measurement, cost, and reliability. Moreover, multiple sensors could be combined and be used in fusion to investigate the operation or process happening in the machine. This combined output from different sources would result in lesser uncertainty. Additionally, if one of the sensors fail or loses sensitivity other sensor(s) could offset this and improve the reliability of the tool condition monitoring technique. However, selection of sensors and number of sensors needed would be difficult to determine and depend on factors like cost efficiency, intended purpose of usage, and practicality of the sensors. Besides the sensors chosen should be protected from the machining environment and positioned in a way that they do not interfere with the machining processes. Careful consideration must be given to placement of sensors for better concentration and higher reproducibility of the signals. Figure 5 shows appropriate location for different sensors in a machine tool to measure different phenomena. (Bhuiyan & Choudhury, 2014) Table 1 Comparison of different tool wear sensing techniques (Bhuiyan & Choudhury, 2014)
Figure 5 Placement of sensors in a machine tool for monitoring cutting tool condition (Bhuiyan & Choudhury, 2014)
As previously mentioned, numerous sensors are being used to monitor the machine tool.
However, the sensors that were investigated for this thesis are the most commonly used and
relevant to the intended purpose. The following chapters will review four sensors namely, force
sensors, vibration sensors, acoustic emission, and current sensors.
2.2.1. Force sensors
Forces are amongst the most important signals observed in a machining process. Having the
knowledge of forces measured during a machining process would provide meaningful information
for the purpose of monitoring and controlling the process. Force sensors can be helpful in
evaluating cutting condition, tool condition and quality of machined surface. The most popular
sensing techniques for force measurements are strain gauges and piezoelectric sensors. (Zhou &
Nikumb, 1995)
In this chapter only piezoelectric sensor is reviewed as they are more widely used in the industries.
Piezoelectric crystal is the key component in this sensor. The working principle is, if a mechanical
stress is applied to a piezoelectric crystal, it produces a proportional electric charge. This charge
can be converted into voltage measure by using a charge amplifier. Figure 6 shows the construction
of such a sensor. It contains two piezoelectric crystals and in between them there is an electrode
present to pick up the charge that is produced by the applied stress. The whole setup is enclosed
in a metal housing.(Thomas Kleckers, n.d.)
Figure 6 Construction of a piezoelectric sensor (Thomas Kleckers, n.d.)
2.2.2. Vibration Sensors
Vibration occurs in a machining system due to the oscillations from cutting forces. It could also
be defined as the back-and-forth movement of a component. Vibrations are usually part of the
machine, but it could sometimes indicate that there is some trouble. The common types of
vibration sensors are accelerometers, strain gauges and eddy-current sensor. (Byrne et al., 1995)
This chapter looks at the most widely used vibration sensor, the piezoelectric accelerometers.
These sensors similar to a piezoelectric force sensor, contains a piezoelectric element. The
principle states that the applied force on the piezoelectric element due to vibration or change in
motion produces an electric charge. However, these sensors require an amplifier to amplify the
generated charge. The sensors that require an external amplifier are called as charge mode
accelerometers and sensors that come with an in-built charge sensitive amplifier is called as
Integrated Electronic Piezoelectric (IEPE) accelerometers. An IEPE requires a constant current
source for its operation which could be provided by the measurement hardware. Figure 7 depicts
an IEPE sensor.
Figure 7 IEPE accelerometer
2.2.3. Acoustic Emissions (AE) sensors
Acoustic emissions are elastic stress waves that are transmitted via the machine tool structure.
These waves are generated from different sources like the cutting tool, workpiece, and machine
body. Some of the significant sources of stress waves include deformation of machine structures,
fracture and chip deformation, friction that occurs between workpiece, chip and tool, and material
reacting to thermal energy. AE is a useful sensor for detecting tool breakage since a high amplitude
signal occurs when there is tool breakage. Types of sensor used to measure acoustic emissions are
piezoelectric, capacitive transducers and laser interferometers. (Gautschi & Gautschi, 2002)
The piezoelectric AE sensors made from PZT(lead zirconate titanate) as detection element is the
most common type used in the industries. It works in the same way as the other piezoelectric
sensors, when the AE waves generated from a source reach the PZT element inside the AE sensor
deforms and converts it into an electrical signal. The construction of an AE sensor as shown in
Figure 8(a) similar to an accelerometer but instead of a seismic mass, a damping material is attached
to the transduction element. Figure 8(b) shows AE sensor used in the industries.
(a) (b)
Figure 8 (a) Design of an AE sensor (b) Example of an Industrial AE sensor
2.2.4. Current Sensors
Measuring spindle motor current can be useful in detecting in tool breakage, tool wear, and
collision. Current sensors are mostly based on hall effect. A voltage is generated perpendicular to
current and the magnetic field, when a current carrying conductor is placed in a magnetic field.
This principle is called hall effect. Furthermore, power can be computed by measuring current
and the line voltage. (Zhou & Nikumb, 1995)
One of the simplest current sensors is analog output linear hall effect sensor. It consists of ferrite
or a silicon steel as the core and a hall effect IC. These components are placed inside a plastic
housing. The current flowing through the conductor which is being measured by the sensor
generates a magnetic field. This is being captured by the core and the flux is concentrated on the
hall effect IC. The output from the hall effect IC is a voltage signal which is proportional to the
current in the conductor. The measurement setup is easy to retrofit, and it does not disturb the
actual cutting process. Figure 9 shows the construction of the hall effect sensor and Figure 10
shows an actual sensor with a split core design.
Figure 9 Construction of Hall effect sensor
Figure 10 Split core Hall effect sensor by LEM
2.3. Data Acquisition
Sensors being transducers that translate physical measurable phenomenon into a form of electric signal, usually voltage or current that is proportional to the measured phenomenon. The signals from the sensors also vary in terms of the type of signals, namely analog and digital. This process of measuring an electrical or physical phenomenon is called Data acquisition (DAQ) and such a DAQ system consists of sensors, DAQ measurement Hardware and a computer with driver software. Choosing the right type of DAQ hardware plays a critical role in the process of data acquisitor. Typica DAQ hardware devices measure/generate either +/-5V or +/-10V. Also, sensors in general need signal conditioning processes like amplification or filtering prior to measurement. The below Table 2 summarizes the common signal conditioning requirements for different sensors and measurement types studied in this thesis.
Table 2 Signal conditioning for different sensors
Amplification
Attenuation
Isolation Filtering Excitation Linearization
CJC Bridge Complet
ion
Accelerometer
X X X X
Microphone X X X X
Strain gauge X X X X
In the process of selecting the right DAQ hardware, sampling rate or the spend at which the signal
data are captured plays also an important role. Sampling rates are usually specified through
hardware or software timers and are usually around 2MS/s. However, choosing the sampling rate
heavily depends on the maximum frequency of the signal to be measured and it is usually
considered to be at least 10 times higher than the latter. One other major component of Data
Acquisition is the resolution of the data, which translates to the smallest detectable change in the
signal. The following figure shows the difference between a 16-Bit Resolution vs 3-Bit Resolution
Chart of a sin wave.
Figure 11 Resolution- 16-bit vs 3-bit
Although accuracy during measurement is not directly related to the resolution, it cannot be better
than the resolution of the measurement device. Accuracy specifications depends greatly on the
type of measurement hardware. Ideally, the measurement device should measure the absolute
value with 100 percent accuracy, but in reality, there always exists some uncertainty or error. This
uncertainty/error may occur because of many factors like system noise, gain error, offset error
and non-linearity. Absolute accuracy of a DAQ device maybe calculated with the formula specified
below
𝐴𝐵𝑆𝑂𝐿𝑈𝑇𝐸 𝐴𝐶𝐶𝑈𝑅𝐴𝐶𝑌 = ([𝑅𝐸𝐴𝐷𝐼𝑁𝐺 ∗ 𝐺𝐴𝐼𝑁 𝐸𝑅𝑅𝑂𝑅] + [𝑉𝑂𝐿𝑇𝐴𝐺𝐸 𝑅𝐴𝑁𝐺𝐸 ∗ 𝑂𝐹𝐹𝑆𝐸𝑇 𝐸𝑅𝑅𝑂𝑅] + 𝑁𝑂𝐼𝑆𝐸 𝑈𝑁𝐶𝐸𝑅𝑇𝐴𝐼𝑁𝐼𝑇𝑌)
The accuracy of the measurement hardware also depends on the type of signal that needs to be
evaluated. Noisy signals for example might adversely affect the accuracy. Moreover, DAQ
hardware must be selected considering the availability of different input modules according to the
requirement. Most DAQ’s have modules for Digital, Temperature Input, universal Analog Input,
Voltage Input, Strain and Bridge Input, Sound and Vibration Input. The Below figure shows a
DAQ chassis with different Input modules that can be used. Most DAQ’s also provide the users
with flexibility in terms of integrating with the computer system and the most common ways are
either through Ethernet or USB connections.
Figure 12 DAQ Hardware with Various Input Modules
One major consideration when it comes to setting up a DAQ system is the Driver software. The
Driver software provides the base for interaction between the DAQ Hardware, and the user
interface also known as the application software. The library within the driver software manages
this interaction between the hardware and the application software. These libraries come with the
required documentation and the utilities for different programming languages that provides the
users with flexibility to choose from the different programming languages for integration with the
application software.
2.4. Predictive maintenance
Predictive analytics uses advanced analytic techniques that make use of historical data to display
real-time insights and help predict future events. Modern machine tools to operate efficiently uses
subsystems such as sensors, IT tools and actuators that monitors the machine condition and the
machining process. In doing so it collects valuable data that could not only be used for process
optimization but also prevent catastrophic tool failure by determining the optimal time to repair
or replace the part. Predictive maintenance is an extension of condition monitoring. It employs
predictive models to detect abnormalities and classify defects. The data collected from the sensors
and other systems could be fed into predictive models to improve product quality and enhance
productivity by integrating it with the process realization systems. Predictive models could also be
used as adaptive control for machining processes and eliminating error approaches. (Kozłowski
et al., 2020)
3. Experimentation
After initial evaluation and research on various sensors and monitoring methods, it was concluded
that developing a cost-efficient and easy setup solution for machine tool monitoring can be best
achieved by using non-intrusive means. Hence, the single element AC current transducer was
chosen. The CT sensor can be clamped to any live wire and capture the current drawn which is
an unadulterated signal. However, clamping the sensor to the main power or any other auxiliary
devices would have made it difficult to carry out the research. So, a decision was made to clamp
it either on a spindle power supply/axis drive of machine tool as it would provide more valuable
information, and a setup was developed based on this.
3.1. Overview of the setup
Once it was decided that a current transducer was being adopted, the next step was the selection
of CT sensor that was going to be used for the experiments. Some of the considerations for
selection of sensors were:
• Amp range the sensor is going to measure.
• Accuracy of the measurement.
• Ease of installation.
After an initial study, the operating range of a CNC machine tool was found out to be 0-100A
and since the end data was going to be used for monitoring and measuring a standard accuracy of
1% was deemed to be enough. Also, as the installation required to be as easy as possible without
disconnecting wires, a split-core design was to be chosen. So, matching these criteria the following
two CT sensors, LEM APR-b10 and CR magnetics CR4111s as shown in the Figure 14 below were
to be installed on the machine tool.
The final setup included both the sensors being clamped to the target current carrying wire/vector
drive. The output of this sensor is then connected to a NIcDAQ 9178 USB/Ethernet chassis with
a 10V voltage input module as shown in the figure below. The output is then displayed and
recorded and stored locally on a computer that is connected to the DAQ with the help of NI
LABVIEW NXG software. An additional power source of 24V is connected to the sensor to
power it. The installations were made to three different machines for monitoring different
machine tool operations and conditions. The setup is shown in the Figure 13.
Figure 13 Machine tool monitoring setup
Figure 14 LEM APR B-10 and CR Magnetics CR4111s sensors(LEM, n.d.)(CR Magnetics, n.d.)
3.2. Manual Lathe Turning
This section covers the testing that was conducted on a manual lathe turning machine at KTH
lab. These tests were conducted to check the functioning of the CT sensors and analyze the data
captured from them. Both the sensors were clamped to the spindle live wire of the lathe and a
cylindrical workpiece was used to perform a face turning operation as seen in Figure 15. After the
data was captured, it was analyzed on MATLAB. A graph as shown in Figure 16 was plotted with
amp on the y-axis against time on the x-axis. The turning operation was performed at 4 different
speeds and with each increase in speed, the current consumed increased as expected. The peaks
indicate the start of the spindle which also increases for the same reason. However, there is a
noticeable variation in measurements between the two sensors this due to the difference in
measuring ranges. The LEM sensor is capped at 50 amps while the CR magnetics sensor is capped
at 100 amps.
Figure 15 Sensors clamped to spindle wire and the workpiece used
Figure 16 Data captured during the test on lathe
3.3. Precima
The work presented here was carried out along with Precima in a regular production machine so
as to understand the information that could be collected during series production. Precima is a
contract manufacturer and the sister company of Nytt AB which specializes in advanced precision
machining. (Precima, n.d.)
The setup was clamped to a Doosan PUMA 2000 series turning machine which produces a gear
and spline part. The blank and the finished part are shown in Error! Reference source not
found.. For the experiment, the two CT sensors were clamped to the main and sub-spindle drive
wires of the machine as shown in Figure 18. The data was captured for 7 days using the same setup
in the previous experiment. However, the files being created by LabVIEW NXG contained data
worth at least a few hours per day, therefore they were created in TDMS format. NI’s TDMS
format is used for storing large data sets and this format is optimized to save the measurements.
(National Instruments Corp., 2015)
Figure 17 Blank and finished part
Figure 18 Setup and CT sensor clamped to main and sub-spindle drive wires in the controller box.
The data was being managed remotely using Teamviewer to start and stop the recording. Once
the measurement was complete, the collected data had to be processed for inspection and offline
analysis. There were different options available to do this, for example NI’s DIAdem, MATLAB,
and python. Since, DIAdem was generally slow in reading and processing data, therefore
MATLAB was adopted again to do the task. Thereafter, a script was written to visualize the data
collected and it included a TDMS reader function to read the file format.
Each day’s data was segmented into four sections and at first signals from the sub-spindle and the
main spindle were visualized in different graphs as shown in Figure 19 and Figure 20 . Subsequently,
both the signals were combined, and a smaller section of the data(around 40 minutes) showed a
clearer image about the process as displayed in Figure 21 and it can also be noted how each cycle
starts with the main spindle being active and then switches to the sub spindle. Figure 22 reveals
how the data looks for a single cycle of operation. The main spindle was active for around 5.5
minutes and the sub-spindle was active for around 3.7 minutes. The total operation time was 8
minutes and 42 seconds. Afterwards, the MT connect data collected from the machine was used
for correlation and verification of the data and it was matching exactly as expected.
Figure 19 Visualization of sub-spindle current consumption data captured during a day
Figure 20 Visualization of the main spindle current consumption data captured during a day
Figure 21 Combined current consumption from the main and the sub-spindle for a short period
Figure 22 Graphical representation of current consumption during one cycle
Furthermore, the Current consumed individually by the main and sub-spindle for one cycle was
calculated by taking a mean of each instance and its equivalent value. They were 2.24 Ah and 1.87
Ah respectively and then the total current consumed for one cycle was computed by adding both
values. These values were recorded for the first day of monitoring the process. Unfortunately, the
sensor collecting data from the main spindle stopped recording since the connection was
interrupted and could not be fixed. So, for the rest of the days' data was collected only from the
sub-spindle sensor.
Table 3 shows the information gathered from analysing the recorded data. It could be noted that
vital information such as the total run time, number of parts produced and current consumption
by the sub spindle was interpreted from the data. The parts produced and the run time exactly
matched with the Opapp and MTconnect data. It can also be observed from the graph the current
consumed by sub spindle was largely consistent with not much variation.
Table 3 Sub-spindle data
Day Total run time
(mins)
Parts produced
Sub Spindle
Cycle time(mins)
Total current consumed for the day(Ah)
Mean total current
consumed (Ah)
Current consumed per
cycle(Ah)
Day 1 510 59 3.7 110.6224335 30.4047 1.8749565
Day 2 135 16 3.7 30.055544 30.4617 1.8784715
Day 3 175 14 3.7 26.24533333 30.4 1.874666667
Day 4 804 90 3.7 167.015595 30.0929 1.855728833
Day 5 495 50 3.7 94.26150833 30.5713 1.885230167
Day 6 828 86 3.7 158.853395 29.9535 1.8471325
Day 7 747 69 3.7 131.0671905 30.8031 1.8995245
Figure 21 Average current consumed/cycle over different days
3.4. Hermle
Figure 22 Setup and CT sensor clamped to main spindle wire in the controller box.
1.82
1.83
1.84
1.85
1.86
1.87
1.88
1.89
1.9
1.91
1 2 3 4 5 6 7
Ah
Day
Current consumed per cycle
Current consumed/cycle Mean
In the next phase of the experimentation, the CT sensors were attached to the spindle motor drive
output of a 5 Axis vertical milling Hermle C270U machine in order to measure the current
consumed by the spindle during the different operations. For this purpose, an experiment was
designed with 2 different operations milling and drilling with different combinations of the feed
rate and speed(rpm) parameters. The workpiece used for the experimentation was an Aluminium
block of 500*400*160 dimensions, clamped on to the worktable of the CNC as shown in the
figure below.
A process plan that included a no-cut run above the workpiece, rough milling of top surface in
two passes, followed by fine milling with two passes, and drilling of 6 holes with different
parameters was designed with the following program.
It was observed that during milling, the peak current consumption was achieved during spindle
start and stop. During the no cut run the current consumed by the spindle was almost constant
throughout the different feed and speed rates corresponding to the rough and fine milling proess.-
However, during the actual milling on the workpiece which included a rough milling of 3mm and
then a fine milling of 2mm with 2 passes for each, it was observed that there was a gradual decrease
in the current consumption, as the feed , speed, volume of material removed(during the passes)
and depth of cut reduced as shown in figure 23.
Figure 23. Current consumed during face milling, no cut(picture on the left), rough milling 3mm and fine milling 2mm(picture on the right)
The drilling experimentation included drilling 6 holes first with a constant feed and varying speed
and then 6 holes with constant speed but varying feed. The current consumption pattern during
the first stage of the experiment was rather consistent throughout the different speed, with a spike
in current consumption at the instant when the speed is changed followed by a sharp drop to a
constant current consumption during the drilling. It was also observed that there was a sharp rise
and drop in the current consumption when the speed of the spindle was decreased from 4550 rpm
to 3900rpm which accounts for the current consumed during gear change in the spindle gearbox.
The current consumption trend during drilling with different feed rates and constant speed can
be seen in fig 24. Observations show there was a small increase in current consumed by the spindle
everytime the feed rate was increased.
Figure 24. Current consumed during drilling, constant feed rate(picture on the left), constant speed(picture on the right)
4. Discussion
This section discusses the findings from the experiments conducted. The tests demonstrate the
capability to monitor the machine tool. The experiments conducted in Precima provided the most
useful results as the data was gathered during regular production of parts. When it comes to the
productivity calculation, it could be done by fixing a threshold to denote the start and end of the
cycle. In case of Doosan PUMA 2000 series machine used in Precima, during the cycle start
minimum current consumed was around 1A and when the cycle was not running it dropped to
near zero. So, these values could set as thresholds to know when the cycle starts and stops and
can also be used to compute productivity.
As mentioned in section 3.3, the MT connect data from the machine at Precima was used for
verification and correlation of the data gathered from CT sensors. It can be concluded that both
data matched exactly as expected. The figure below shows the correlated data for a single cycle
and each section was assigned a colour code that was in turn connected to different tools being
used in the operation along with time taken by each tool. This pattern was consistent with entire
data that was recorded. Besides, the highs and lows in the graph of each cycle could not be
interpreted as it required to understand the process in depth which wasn’t possible. There were
also deviations in some of the cycles which couldn’t be discerned from the MT connect data.
The sensors were reliable for the short time(around a week) during which they were in operation,
however the wiring and setup must be checked and ensured since, one of the sensors stopped
working due to improper connection. Moreover, tests should be conducted in the future to check
the reliability of the sensors in the long run.
This thesis only covered the data that could be gathered from spindle drive of a mach ine tool.
The CT sensor could also be clamped on to axis drives, and other auxiliary components which
could reveal more information about the machine tool.
5. Conclusions The main objective of a machine tool monitoring system is to let the user make more informed
decision thereby increasing productivity. To achieve this, Nytt uses a simple and easy to use
solution. However, tackling more complex problems requires additional sources of data capturing
and yet keeping the setup simple.
The focus of this thesis was on examining the additional data gathering sources like sensors and
the usefulness of the information provided by it. With a in depth background study and research
on different sensor, their working, machine tool parameters and optimum locations for measuring
them, it was found out that a number of different sensor technologies like temperature, vibration,
acoustic emission, current transducer have been proved useful and could be utilized for machine
tool monitoring purposes. Theoretical studies were conducted for selection of the sensors and
with an aim to keep the setup simple a current transducer (CT) sensor was chosen as it provides
a non-intrusive way of monitoring.
Two hall effect split core single element AC current transducers, from two different manufacturers
with a range of 0-10VDC were chosen and tested in this thesis. A very high sampling rate of
24Khz was used for the experiments, which resulted in a huge amount of data points. However,
a sampling rate of 1Khz would be sufficient for just monitoring purposes as in Nytt AB’s case.
Experiments proved that the Hall effect sensors could themselves be used as standalone sensors
for monitoring the machine tool and calculating productivity, however they have some limitations
like differentiating between waiting/setup times and stop times, where other sources like MT
connect or Nytt AB’s monitoring system could provide support.
Figure 23 One cycle current consumption recorded at Precima along with tool identification and operation times
The data collected from the sensor were consistent and could be verified and correlated with other
sources of data. The reliability of the sensors proved to be consistent and good. However, issues
with wiring and setup were noticed during experimentation especially as one of the sensors had a
small input module for the wires.
6. Future Work The results of this thesis work provide a strong foundation for future work in monitoring, data
gathering, and optimization of the machine tool and the machining process. Another area the
proposed solution has to be studied is implementing it in a real-world scenario. This section
discusses the future research in the following areas.
6.1. Hardware Requirements The devices like NI DAQ and power source provided by KTH were used in conducting the
experiments and to get results. However, to develop this as a working solution, fewer and cheaper
components would be required to make it cost efficient and easy to install.
One of the main challenges is replacing the NI DAQ with a hardware which is much smaller and
cheaper. On conducting some research, the multifunction DAQ produced by LabJack could be a
potential device used in the solution. The LabJack T7-Pro with WiFi connectivity could be paired
with the CT sensor to transmit the data collected to the cloud. The LabJack U3 could be another
viable option as it is much cheaper, but it needs an additional edge device to transmit the data.
These devices also have an added advantage as the support resources are provided for device
configuration and data collection (LabJack, n.d.). An alternative to LabJack devices could be a
purpose-built Arduino to monitor the current/energy. The guide on how to build an arduino and
interface it with a current transducer is described by Learn.(OpenEnergyMonitor, n.d.)
Moreover, cheaper alternatives for the CT sensors exist in the market and they can be tested out.
However, they might be less accurate in measurement and also might not be reliable in the longer
run.
There are many different possibilities when it comes to developing an effective solution. The
alternatives proposed must be carefully assessed in terms of cost efficiency, simplicity, and ease
of installation.
6.2. Process optimization capabilities There are different possibilities to use the information/data gathered from the sensors to not just
monitor the machine tool but can extended to applications optimizing the process, detecting tool
wear and more.
The experiment conducted by (Chen et al., 2015) describes how the instruction data such G-codes
could be combined with data from the CT sensor to reduce the spindle current consumption as
well as the machining time. It shows the capabilities of carefully analyzing the process and
changing the feed rate to effectively optimize the process.
The article by (Zhang, 2018) explains about optimization of the tool life by correlating with the
amperage data. The hypothesis was that the tools draw more power as it gets more worn.
Therefore, to compensate the dullness of the tool the machine has to draw more power to produce
parts with precision. This knowledge could prevent a catastrophic tool failure and plan the lifespan
of a tool efficiently.
These experiments have been left for the future due to limited resources and lack of time.
Regardless, analyzing the amperage data of machine tool provides important insights, and
opportunities to diagnose problems.
Appendix
Figure 24 Day 2 sub-spindle current consumption data at Precima
Figure 25 Day 3 sub-spindle current consumption data at Precima
Figure 26 Day 4 sub-spindle current consumption data at Precima
Figure 27 Day 5 sub-spindle current consumption data at Precima
Figure 28 Day 6 sub-spindle current consumption data at Precima
Figure 29 Day 7 sub-spindle current consumption data at Precima
Figure 30 MTconnect data collected from Precima
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