HOMOGENEOUS ADMINISTRATION OF EXPERIMENTS IN MATERIAL
SCIENCE FOR CONFIGURATION, MONITORING AND ANALYSIS
Ferdinand Ferber1, Thorsten Pawlak2, Thorsten Hampel2, Franz-Barthold Gockel1, Rolf Mahnken1
1 Lehrstuhl für Technische Mechanik (LTM)
2 Informatik und Gesellschaft (HNI)
University of Paderborn, Warburger Straße 100, D-33098 Paderborn
[email protected]
ABSTRACT
In this paper, experimental techniques and studies of deformation and failure arising in thermo-mechanically loaded specimens
as a result of cyclic thermal shock will be used to show the integration of experiments into virtual knowledge spaces. It will be
shown, that the extension of automation to a complete integration of the workflow into systems for computer supported
cooperative work leads to several benefits in long term experiments. Main objectives are to achieve a high automation level
and to encourage teamwork.
Introduction
Material experiments at the LTM consist of techniques and studies of deformation and failure arising in thermo mechanically
loaded specimens as a result of cyclic thermal shocks. The deformation is measured by the 3D-digitalisation method. Surface
damage and crack initiation are localized by the eddy current method. Because of these problems it seems reasonable to
analyse damage in materials by means of state-of-the-art continuum mechanics and experimental methods [1-4].
As material test usually run lots of cycles a complete test run often lasts several days [5]. Most of the applied systems are
loosely coupled and need to be run manually. This creates a demand for a high level of automation, in fact, the user interaction
must be kept as simple as possible. Furthermore, the configuration, the monitoring and the analysis of a test run would be
operated best from only one system to reduce media-discontinuity. In the course of this paper, it will be shown how concepts
of virtual knowledge spaces [6] can lead to higher productivity for material testing. So far, the integration of different systems
took place (manually) by the operator, transferring data-files from one system to another, e.g. the results from the eddy current
measuring to the analysis system. In this context the goal is to achieve an integration of all systems into one place. Different
kinds of media should be accessible to many groups. This is where knowledge spaces suit best. In short, virtual knowledge
spaces offer object-oriented document management. Access can be archived web based or through rich client applications.
Mapping the units of a material test scenario to objects, whether they are force ramps, temperature curves or measurement
results, all could be managed in virtual knowledge spaces. This leads to a homogenous generation and administration of
knowledge, which, besides, is location-independent. Furthermore, by using the existing mechanisms of virtual knowledge
spaces for documentation and publishing the need of additional systems becomes redundant.
A virtual knowledge space [7-9] can contain arbitrary interaction objects (research materials, representations of experiments,
real machines etc.), which are editable synchronously or asynchronously. A room also disposes of editing and communication
functions, thus reducing media discontinuity between researchers. Rooms, once created, are managed in self administration.
Due to the concept of views, a virtual knowledge space can be explored in different ways: At times it is displayed as a simple
web page, at other times as an electronic whiteboard or even a file view in an explorer. This enables a combination of
innovative forms of media supported cooperation (e.g. cooperative, discourse oriented, knowledge structured) with a long
lasting administration of knowledge repositories (virtual library) and learning objects.
The Computer Supported Cooperative Work (CSCW) System opensTeam [10] offers a completely open source based
framework based on the concepts of virtual knowledge spaces. Its infrastructure connects existing university services and
creates a specifically tailored cooperation and even an e-learning platform. Based on existing standard technologies, a
multitude of interfaces and templates can be used and integrated into existing infrastructures, according to preferences and
available development capacities. Since spring of 2006 opensTeam is part of the Sage-Debian-Linux-Distribution.
This paper will outline the integration of machines into virtual knowledge spaces. By way of example, this is illustrated by the
open
sTeam
material test experiment. The main focus will be on the interface between the hardware and the CSCW System
using web services.
Related Work
Connecting experiments and CSCW Systems resembles the idea of virtual labs. There exist virtual labs like [12], but these
labs are mainly focused on the simulation of the respective experiments. Experiment costs shall be reduced by simulating the
behaviour of the material for educational purposes. But the idea to connect machines that run experiments to the system which
manages the workflow and documents of the experiments is new. This leads to a new way of working with experiments. The
high level of integration offers the advantages of virtual labs, with the additional benefit of actually executing the experiment in
real life and on one system.
Some Research shows that for now nearly no control system offers web services as a modern interface to the visualisation
system. A product description given on the website of the German company Beckhoff states that “Open standards such as
OPC, XML, web service or MODBUS/TCP are used for interfacing the software with visualisation systems or audio-visual
equipment, ...” [11]. But there is no detailed information about the specification.
Thermal shock experiment
The LTM of the University of Paderborn has built a thermal shock laboratory where metal samples can be treated, 3D
digitalized and surfaces scanned by the eddy current method. The machines can all work together in experiments but can be
reconfigured if needed. The setup shown in Figure 1, 2 consists of a powerful induction heating from HUETTINGER Electronic
[15], a water sprinkler for cooling, an industrial robot from ABB [13], a stereo camera system from GOM [14] and an eddy
current measuring unit. The robot is intended to hold the camera system and the eddy current sensor when measuring. The
setup is controlled by a control server, which is assisted by a programmable logic controller (LPC) for switching several motors
and coolant pumps. The control server realizes the main part of the automation of the machines. All machines are
preconfigured through the server and act as modules to the outside interface. Through this interface the CSCW system has
the ability to control the machines. The control sever additionally implements a web based user interface for stand alone use.
For further information on this issue see the respective paper from the University of Paderborn presented at this conference
[17].
Figure 1. Overview of the thermal shock setup
The tested material is a heat resistant stainless steel X15CrNiSi20-12, which is used in many high temperature applications. A
cylindrical geometry with D=60mm and L=60mm was chosen, which, due to its relatively large thickness, initiates thermalmechanical interactions. The specimen is heated up to 900°C by an induction heating (see Figure 4a) and shocked down by a
water sprinkler to near-room temperature. The complete setup with robot, sample holder, sprinkler and heater can be seen in
Figure 3.
Figure 2. a) Induction heating a cylindrical sample; b) robot moving the stereo camera; c) robot moving the eddy current
sensor over a stressed sample
After the application of 500 thermal shock cycles the cylindrical specimen was measured by a 3D-digitalisation (Figure 4b).
This measurement technique enables a full surface analysis, as shown in Figure 5a), of the plastic deformation, which can be
used for later model validations. In addition, a damage analysis of the specimen’s surface needs be done applying the eddy
current method (Figure 4c), which makes use of impedance changes in materials affected by cracks and structure changes.
(Figure 5b) shows an example of a typical crack signal, detected by the eddy current sensor. The high peek of the
demonstrated absolute value signal corresponds to the crack shown in the detail photo of the material surface. For the
illustration of the location and degree of damage the signals of the damage analysis are projected to the digital specimen
surface as presented in Figure5c).
Figure 3. a) Deformation after 500 thermal shocks, b) eddy current signal with crack peek, c) eddy current signal projected
onto the surface of the digitalised sample
The detected deformations are reproducible in quality and quantity and are dependant on the number of thermal shock cycles.
Cyclic applications of thermal shocks cause plastic deformations with magnitudes of several 1/10mm. For the damage analysis
via the eddy current method, different signal levels have been defined and accumulated in accordance with the number of
cycles. Signals of low level start at 50 cycles, while the higher levels start at 200 to 300 cycles. The damage groves, reaching
from micro cracks to macro cracks, and the accumulated signals can be interpreted as an accumulation of crack length. These
damage analysis data can be used for the validation of later life time investigations.
Experiments as part of collaborative systems
A standard scenario in experimental science could be as follows: A sample has to be exposed to several thermal shocks with
temperature ranges of 500°C or more to get information about the material under heavy load. Now, the sample will be
documented, measuring its height, length and weight. This is often done by using simple text editors. After that, the document
must be placed on some kind of persistent storage for later user. Let’s assume this is done on local hard disk. After
preparation work is done the sample is taken to a stand alone machine, which will conduct the shocks. The scientist places the
sample into the machine and programs the temperature curves and number of cycles. After starting the process he has to wait
until the cycles are finished, which might last a long time. Afterwards, the sample needs to be scanned for material damage.
This is done by another stand alone system. It has to be programmed separately and one has to wait until results are
available. Depending on how the sample is to be treated now, several steps could follow, in which somebody has to program a
machine, place the sample and wait for the results. If all results are available they have to be collected and copied back to
another computer to conduct further analysis. By doing this and writing the results of the experiment down, the scientist
produces more documents which are only placed on local hard disk. Finally, the results have to be published, which needs to
be done by a web server. In this context, the results need to be worked off and transformed in html in order to place them on
the server.
The disadvantage of the entire process is that the user has to switch between several programs for documenting and
publishing, as well as the machines to program the experiment. The workflow is not integrated, nor is the document
management. If someone needs to conduct further research he just has the public access on the web server at his disposal.
The source documents are out of reach because they are stored on a client machine. As this is, of course, a worst case
scenario it will now be presented how integrating the whole workflow into a collaborative system may form a next generation of
collaborative knowledge generation.
Figure 4. Loosely coupled systems
The same experiment is assumed. A sample has to be treated with several thermal shocks. But in contrast to the earlier
experiment, the scientist now uses a collaborative system for his preparation work, the experiment itself, the analysis and the
publication. He creates new documents for the experiment and attaches the information about the sample. Next, he generates
a new experiment object in order to program the machine. The particular machines are encapsulated in function blocks, which
can be configured to the experiment needs. Then, a sequence is programmed to reflect the temperature cycles and the later
measuring of the process. Now, the sample needs to be placed at one place and then, the experiment can be executed.
Underlying automation procedures secure the correct order of execution. While the machines are connected to the
collaborative system a real time monitoring is possible during the process. If the experiment is over, all measuring results are
automatically written back to the collaboration system where they can be used for analysis. Furthermore, the system realizes
the publication of selected data.
Figure 5. Integration
The main advantage of this approach is that not only the documents are available through a collaborative system but also the
experiment itself. Easier configuration, monitoring, analysis and publication are done with one integrated system with flexible
right management for each step of the process.
Machines as part of CSCW Systems (virtual knowledge spaces)
By making machines available in virtual knowledge spaces, a vast diversity of scenarios is imaginable. The configuration of a
machine which reflects its behaviour is also a kind of knowledge which persistently needs to be stored. Having the possibility
to arrange machine objects with other objects, such as documents, provides a flexible way of managing this knowledge.
In order to make machine-objects available in virtual knowledge spaces, software is needed to provide an interface between
the machines and the CSCW System. One part of the software is an extension of the CSCW System. The other part is the
open
sTeam has the ability to be extended by steam services [18]. The
corresponding part of the interface on the machine side.
service has to implement the server side behavior of the machine object, which then shows up in the graphical user interface
of the CSCW System.
It is substantially important that the interface uses a standardised protocol. Web services are not yet widely spread, but they
offer a good concept for machine-to-machine communication. It is possible that machines which already have connectivity to
local area networks may implement web services in the future. This would lead to a very easy integration into other systems.
For now, there are nearly no systems in the scope of machine control which offer web service interfaces. This is why the
machines in the setup of this experiment have to be encapsulated into a piece of software which translates the machine
language into a web service compliant protocol.
Figure 6. sTeam room
In order to represent the machines’ entities in virtual knowledge spaces, they have to be modelled as object, as virtual
knowledge spaces consist of objects. As objects representing documents only have a small set of actions (move, delete,
arrange, etc.), the main challenge for machine objects is to model the actions to control the machines. The modelling has to be
kept very flexible in order to extend the actions if new machines are added or the functionality of the existing ones are
enlarged. This is why generic and self explanatory interfaces should be used. Web services do best comply with this
requirement, as they offer a standardised xml communication, as well as a self explanatory interface definition language. This
results in better cooperation between participating scientists and easier administration of the whole system. Due to this, web
services are the best solution for the described problem. They are defined as follows by the W3C Web Service Architecture
Working Group:
Figure 7. XML representation
„A Web service is a software system designed to support interoperable machine-to-machine interaction over a
network. It has an interface described in a machine-processable format (specifically WSDL). Other systems interact
with the Web service in a manner prescribed by its description using SOAP messages, typically conveyed using
HTTP with an XML serialization in conjunction with other Web-related standards.“
Figure 8. Data flow
The standard web interface of the existing control server has to be extended as a web service. Hence, a XML description of its
capabilities is written and the COAL protocol must be implemented to communicate with the sTeam server. In sTeam, the
machines are represented by a cylinder object, which is a synonym for the sample. Interacting with the sample sends the
commands to the control machine using the web service. Let’s assume the user wants to digitalise a sample with the stereo
camera system. By opening the cylinder object as shown in Figure 6, a list of available actions becomes visible. The actions
themselves consist of objects and are represented, for example, by scripts. By selecting the action ‘capture_sample.abl’ the
process is initiated as shown in Figure 8. The script is encapsulated in XML (Figure 7) and then transferred to the control
server over the SOAP protocol. On the control server the script is inflated and executed step by step. Each command has to
be sent with a proprietary protocol over Ethernet using a proprietary protocol to the corresponding machine. Each machine
processes the commands and returns results, if available. The results are collected at the control server and then again
encapsulated in XML and sent back to the sTeam server over the SOAP protocol. The sTeam server generates new objects
from the data taken and makes them available to the user. Now, it is possible to post process the data by the sTeam server,
as, for example, mathematical calculations assisted by the MuPAD-Computing server or visualising the data using web pages
or the rich client whiteboard Medi@rena [16]. Of course, the data is also accessible as normal files by ftp or webdav.
Publication is made easy, using the capabilities of the sTeam integrated wikis and blogs. The ability of using access rights on
every object gives a fine grained control over the entire process.
Figure 9. Self explanatory machines
Conclusions
Making machine objects available in virtual knowledge spaces, and hence, having the ability to make experiments with the
assistants of collaborative systems, offers a high integration of the entire workflow of material science. The advantage of using
web services is that a machine itself holds the command set and is therefore self explanatory. The next step would be to
uncouple each machine from the control server. This would lead to a web service interface for each machine. In future,
machines will come on the market which will have the capabilities to communicate via web services (Figure 9). This will permit
to automatically detect and integrate them into a collaboration server to easily extend virtual knowledge spaces.
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