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POSSIBILITIES TO DEVELOP
FOREST MACHINE SIMULATOR BASED EDUCATION
WITH TECHNOLOGY
Pekka Ranta
TUT/DMI/Hypermedia Laboratory
[email protected]
1. The description of ProForSim –project
On the Development Project in Forest Machine Simulator-Based Training
(ProForSim) simulator-based education has been developed and the tacit knowledge
of experienced harvester drivers is made explicit at the North Karelia College of
Further Education in Valtimo, Finland. Project is financed by European Social Fund.
Other partners are Komatsu Forest Oy Ab, Ponsse Inc., Timberjack Inc., University of
Joensuu, Faculty of Forestry; Finnish Forest Research Institute, Tampere University
of Technology/ DMI/Hypermedia Laboratory and North Karelia Polytechnic.
2. Analysing the competence of a harvester operator
Assessment of the tacit knowledge of harvester operators represents a relatively new
area of research. Tacit knowledge refers to the know-how which is acquired through
personal experience and which can be difficult to communicate to others or to
simplify through modelling or other such means (Nonaka 1995). It is interesting to
note that quite little is known about the competence of a machine operator. Data on
forest work and its quality collected by researchers and the rich amount of process
data recorded by harvesters and forest machine simulators can be used to describe and
analyse the work process. The cooperative research bodies included in this project are
TUT/DMI/Hypermedia Laboratory, the University of Joensuu and the Finnish Forest
Research Institute/ Research Centre in Joensuu. Forest machine manufacturers have
made available process data acquisition systems and provided valuable research
support. Thirty four professional operators from Eastern Finland participated in
different phases of the study.
The research material has been analysed using mathematical statistical methods e.g.
self-organizing maps and data mining methods. The research data is collected from
the simulator and from the genuine harvester. The detailed research results are
published in Ranta, Laamanen, Pohjolainen & Väätäinen 2004. More detailed
research results about tacit knowledge of a harvester operator are published in
ProForSim –project also in Väätäinen et. al 2004 and Ovaskainen et al. 2004.
Simulator research in January to December 2003, Self-Organizing Maps (SOM) and
Principal Component Analysis (PCA) were used to profile harvester drivers.
Correlations between productivity and the different movements of the boom were
analysed. Also the productivity of different harvesting techniques were compared
with each other using the t-test of independent means. Statistically significant positive
correlations were found between productivity and the use of extension of the boom
and four simultaneously used boom joints and negative correlations were found
between productivity and vertical boom movements. Seven different driver clusters
were discovered from data. It was typical for the drivers in the most productive profile
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to use boom effectively and smoothly and they were also skillful to handle stems.
They worked with average size working points. The comparison of the profiles was
hindered by lack of process criteria that describe comprehensive quality of operator´s
performance and timber harvesting. (Laamanen 2004; Ranta, Laamanen, Pohjolainen
& Väätäinen 2004.)
Harvester process data mining (PlusCAN data collector) and work technique analysis
from January to October 2004 in demanding conditions in thinning. Data mining
methods are very useful, when we want to find regular operating models from very
large amount of data. GUHA (General Unary Hypotheses Automation) is a method of
automatic generation of hypotheses based on empirical data. It generates
systematically hypotheses supported by the data. The interesting research questions
are: 1. Do the operators have some regular working techniques in demanding working
tasks and situations? 2. Are there any common heuristic operating models (e.g. length
of the boom, felling directions, size of working point)? We found that there are
several regularities in the use of the boom length 3-6 m on front sector and 6-9 m on
side sectors. Operators move the trunk to processing area to opposite side of the strip
road, when the boom length is 3-6 m and a felling happened in front sectors.
Operators avoid the movement to over strip road, but sometimes it is more appropriate
to make e.g. bigger piles or it is appropriate to cover strip road with twigs. The
operator working points were quite small. Some operators had regularity on this. The
most efficient operator did not use much time with the little trunks, when he made
decision on quality of a trunk. He had a very clear schema about which tree was worth
processing.
3. Research results
On the basis of the research results it has been possible to analyse operator
competence and produce profiles for the different operator groups. An operator plans
the work in strategic, tactical and operational level (Ranta 2003). Kariniemi (2003)
and Gellerstedt (2002) have also stressed the meaning of operators planning and
foreseeing in a logging process. This aspect emphasises prepared, controlled, planned
and reasoned action which includes theory and practice. These procedures are
performed in situ at an operative level, the study of which provides the basis for
know-how evaluation. The assessment of the various required planning and operation
tasks is performed by the operator on a case-by-case basis. (Ranta et al. 2004.) An
effective driver can operate efficiently in all phases of the work cycle. Additionally he
minimizes the share of non-productive work phases by concentrating on productive
work. (Väätäinen, Ovaskainen, Asikainen & Sikanen 2003.) The operation itself is a
justified compromise between several work-influencing factors. An experienced
operator can build in advance a broad image of the various work phases, the factors
affecting the work process, the quality of the harvesting process and its limitations. A
skilful operator is able to always think 4-5 stems ahead during felling work and
analyse the factors which affect the work in each treatment area. This ensures that
felling operations proceed smoothly, professionally and in a manner which preserves
the felling machine and takes into consideration both the environment and the
processes further down the production chain. Automated, work-oriented smooth
control of harvesters and especially loaders is an essential factor which is achieved
through the controlled execution of decisions. An experienced operator can utilise the
kinetic energy created during felling and processing. Furthermore, the operator
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controls the felling device of the loader by means of synchronized movements,
accelerating, swinging and braking, whereupon the loader movements become almost
“at one” with the operator’s own hand movements. The operator’s tacit knowledge is
brought especially to the fore in demanding felling conditions.
4. Modelling a harvester operator´s performance on simulators for educational
purposes
The effectiveness of the training is significantly influenced by, in addition to the
simulator itself, the learning methods, learning materials, and guidance resources
employed. The quality of simulator-based study can also be enhanced through teacher
training. The key point of view is to understand the competence needs of a harvester
operator’s work as whole. The ProForSim -project has developed a curriculum,
training exercises, study methods, an Internet-based learning environment and both
digital and printed teaching materials for controlled timber harvesting study purposes.
One solution to develop the quality of learning is to illustrate the real work processes
(videos) and concepts of a managed logging processes. In this way trainees can
construct mental models, which demonstrate transfer of learning and added value at
working in a real environment. The operating models (DVD-videos of ProForSim)
illustrating the implementation of a managed logging process present the
fundamentals and main concepts of mechanised logging. The video clips are
developed for studying the planning involved in mechanised logging, its management,
quality, execution and work safety assisted by simulator sessions and practical
training in the forest operating a real-life harvester.
How can we support learning with simulator in this very complex domain? The main
question is how we are able to support perception detection, information processing,
planning and decision making. When we discuss the educational simulators, very
often the main question is, how authentic this simulator is? This is important, but it
conceals important points of view. A newcomer needs very low level of difficulty or
augmented information rich information than in genuine environment. It also conceals
very easily learning of planning and decision making. There are some possibilities
e.g. to use augmented reality methods, which means that a trainee has a chance to
illustrate the cut-to-length evaluation, felling directions, working sector, and selection
order of the felled trees. The key point is that a trainee should make his thoughts,
plans and arguments visible for others (cf. Ranta 2003; Norman 1983). In this way an
expert or a teacher can analyses the planning and operation. They can support
decision making of a student. This gives also possibility to create easier learning
environments (context) for the newcomers. This way their cognitive pressure is not so
demanding especially in the beginning of the harvester education.
Self-organising maps and data-mining methods offer interesting possibilities when we
want to evaluate operators’ performance with process data analyzing method. If we
can model operators´ performance, it gives the possibility to collect and analyses data
and give appropriate reports to students how they should change their competence
profile. If we can model operators´ logging process to conceptual model, we have a
possibility to construct an ontology of this. The term ontology means a specification
of a conceptualization. Gruber (1993) defines an ontology as a description (like a
formal specification of a program) of the concepts and relationships that can exist for
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an agent or a community of agents. If we have an ontology of operators´ profiles
(operating model), authentic use cases, a planned and a focused simulator stand and a
plenty of a metadata rich learning material like video clips, we can support a trainees
reflection on action/debriefing with analyses. We can use also artificial intelligence,
data mining tools and semantic web methods for the selection of an adaptive learning
material based on student profile (Nykänen 2002). Research has been done on these
questions at the TUT/DMI/Hypermedia Laboratory, and a master thesis will be
published in the near future in which these issues are looked into (Huhtamäki 2004).
Harvester manufactures has done high quality development work with simulator
based training. But we have still a lot of joint, multicultural, and multiprofessional
development work in the future if we want to construct a solution which will take into
account the varied needs in this very complex and comprehensive domain.
About the speaker: Mr. Pekka Ranta is working as a senior researcher at the
Hypermedia Laboratory of Tampere University of Technology. He is a project
manager in the Development Project in Simulator-Based Forest machine training
(simumedia.pkky.fi).
Bibliography:
Gellerstedt, S. 2002. Operation of the single-grip harvester: motor-sensory and gognitive work. Int. J.
For. Eng. 13(2):35-47
Gruber, T. 1993. What is an ontology? http://www-ksl.stanford.edu/kst/what-is-an-ontology.html
Huhtamäki. J. 2004. Managed Publishing Process for Structured Information. Tampere University of
Technology/Hypermedia Laboratory. Will be published in the near future. . In Finnish.
Kariniemi, A. 2003. Metsäkonetyön kuva – Ajattelun ja suunnittelun merkitys. In Kehittyvä puuhuolto
2003 – seminaarijulkaisu. Käpylä Print Oy, Helsinki. s. 13-22. In Finnish.
Laamanen, V. 2004. Making a Harvester Operator´s Tacit Knowledge Explicit Using a simulator and
Mathematical Methods. Master of science thesis. Tampere University of Technology. In Finnish.
Nykänen, O. 2002. Developing XML Authoring processes. Technology review. Tampere University
of Technology/Hypermedia Laboratory, Department of Mathematics.
Ovaskainen, H., Uusitalo, J. & Väätäinen, K. 2004. Characteristics and Significance of Harvester
Operators' Working Technique in Thinnings. Journal of Forest Engineering 15(2): 67 -77.
http://www.lib.unb.ca/Texts/JFE/
Ranta, P. (2003) Possibilities to develop forestmachine simulator based studying. Proceedings of PEG
2003. Powerful ICT Tools for Teaching and Learning [http://PEG2003.org]. St.Petersburg, Russia.
University of Exeter, England.
Ranta, P., Laamanen, V, Pohjolainen S. & Väätäinen K. (2004). Making a Harvester Operator´s
Tacit knowledge Explicit. Tampere University of Technology, Digital Media Institute. Hypermedia
Laboratory. Report 2004:1. In Finnish.
Väätäinen, K, Ovaskainen, H. Ranta, P. & Ala-Fossi, A. 2004. Hakkuukonekuljettajan hiljaisen
tiedon merkitys hakkuutulokseen ja korjuuprosessin laatuun työpistetasolla. Will be published in the
Metsäntutkimuslaitoksen tiedonantoja -series in December 2004. In Finnish.
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