DRAFT
ONTOLOGY FOR ADDITIVE MANUFACTURING PROCESSES
Bryan Chu
Graduate Research Assistant
chub3@rpi.edu
Congrui Li
Graduate Research Assistant
lic10@rpi.edu
Marshall Xu
Associate Research Scientist
max7@rpi.edu
Johnson Samuel*
Assistant Professor
samuej2@rpi.edu
Rensselaer Polytechnic Institute, Department of Mechanical Aerospace and Nuclear Engineering,
110 8th Street, Troy, NY 12180, USA
* Corresponding Author
KEYWORDS
Additive Manufacturing, Ontology
ABSTRACT
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with basic understandings of the additive manufacturing
processes. However, although different types of additive
manufacturing techniques share common properties, they are
often treated as wholly unique. Thus, it is also difficult to use
data to develop additive manufacturing processes. For
example, it is difficult to infer what parameters to use when
inkjet printing a material even if there is data describing its
performance when used in a fused deposition modeling
application.
The creation of an additive manufacturing ontology will
help solve both the issue of finding useful data and using the
data to develop and improve additive manufacturing
processes. Creating a unified ontological framework, will
reduce the fragmentation caused by the multitude of
proprietary formats, making searching for useful data easier.
Leveraging the power of the semantic web will aid in
validating hypothesized relations, as well as potentially
revealing hidden ones. The main challenge will be convincing
those with data to provide it to populate the dataset.
1. INTRODUCTION
2. ONTOLOGY SUMMARY
The field of additive manufacturing is growing at an
incredible pace. New techniques emerge regularly. At the
same time, existing processes are refined and improved upon
to use a wider range of materials and satisfy more stringent
manufacturing requirements.
One issue that is slowing the progress of additive
manufacturing is the difficulty in finding data to aid in the use
of new equipment or materials or the design of new processes.
This often necessitates lengthy and expensive periods of trial
and error. Even when satisfactory operating parameters are
determined, they usually only work for one specific
combination of equipment and materials. Worse yet, they are
often kept proprietary to prevent competition, perpetuating the
difficult conditions. This stifles innovation in the additive
manufacturing space by artificially lengthening the
development stage.
Fortunately, not all data is kept proprietary. Many
laboratories perform the basic research necessary to come up
In general terms, an ontology is a formalized scheme that
describes the types of entities involved in a domain and the
relationships between those entities, in the most generic terms
possible. This can be further supplemented by rules that limit
or modify the relations between entities based on properties of
those entities. These entities and relations can be
conceptualized as maps, such as those in the appendices. Here,
entities are represented as nodes and the relations are
represented as arrows and relationships can be expressed as
<entity a> <relation> <entity b> in the direction of the arrow.
For example, Object [is] subClassOf Entity. All of these
relationships have an inverse relation defined the opposite
way, for example, Entity hasSubClass Object, which are
omitted to reduce clutter.
The conceptual map is realized by encoding the entities,
relations, and rules into the Resource Description Framework
(RDF) [1]. With the rules encoded, data scientists are able to
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populate a database with numeric data corresponding to
entities in the ontology. Subsequently, the database and rules
of the ontology can be used to confirm or identify various
relationships in the numerical data. Corey please add more
here
2.1 Materials Processing Ontology
[1] Lassila, O., & Swick, R. R. (1999). Resource description
framework (RDF) model and syntax specification.
http://www.w3.org/TR/1999/REC-rdf-syntax-19990222
[2] Dumontier, M., Baker, C. J., Baran, J., Callahan, A.,
Chepelev, L. L., Cruz-Toledo, J., ... & Hoehndorf, R. (2014).
The Semanticscience Integrated Ontology (SIO) for
biomedical research and knowledge discovery. J.
Biomedical Semantics, 5, 14.
For the additive manufacturing processes ontology, the
ontology is names the Materials Processing Ontology (MPO).
A conceptual map of the ontology is shown split into two parts
in Appendices 1 and 2. Appendix 1 shows the class hierarchy
of entities in the ontology and Appendix 2 shows the valid
relationships between entities. In Appendix 2, the entities with
bolded borders, step and process, are duplicated to remove
overlapping lines to improve clarity. In these maps, the
relationship arrows are drawn such that there is a path from
any node to the product, which mirrors the many inputs one
output nature of manufacturing processes.
Three super classes of entities are identified, viz.,
attribute, object, and process. Attributes are used to describe
the various numeric data corresponding to other entities, such
as material properties. They are also used to describe abstract
attributes, such as when a step is recurring, for use in
determining rules in the RDF.
Objects are the easiest to explain as they mostly represent
physical entities involved in the additive manufacturing
process. The exception is the model which is used to describe
the combination of CAD and slicer used in the manufacturing
processes. It is important to note here that the terms used in
the ontology are application-agnostic, hence, the use of terms
like actuator and energizer when specific applications use
specific technologies like stages or lasers.
The process entities are those used to describe the steps in
the additive manufacturing process such as indexing,
scanning, etc. These are used to establish the sequence of the
process. The complexity of defining the sequence of additive
manufacturing processes is shown in the right side of
Appendix 2. Numerous different relationships are needed to
account for situations such as subprocesses and the looping
nature of some additive manufacturing processes while still
defining a strict sequence for the process.
The left half of Appendix 2 defines the more usual
relationships between entities in additive manufacturing
processes. At the heart of all additive manufacturing processes
is the process of adding energy to materials to induce changes
that allow them to be manipulated. This is represented in the
ontology by the Energizer which addsEnergyTo a Material,
both of which isParticipantIn a Step or Process. This is
facilitated by other pieces of Equipment, typically Actuators.
Actuators move the Material and other pieces of Equipment
during certain Steps or Processes as defined by the Model.
3. EXAMPLE USE CASE
ACKNOWLEDGEMENTS
REFERENCES
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APPENDIX 1: MPO CLASS HEIRARCHY
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APPENDIX 2: MPO ENTITY RELATIONSHIPS
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APPENDIX 3: MPO USE CASE- FUSED DEPOSITION MODELING
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