BAMM Aspect Meta Model*

Andreas Textor1, Steffen Stadtmüller1, Birgit Boss1 and Johannes Kris-
                                tan2
                       1
                           Robert Bosch GmbH, 70469 Stuttgart, Germany
                            2
                              Bosch.IO GmbH, 12109 Berlin, Germany

        Keywords: Digital Twin, Aspect Model, RDF, Industry 4.0, meta model,
        semantics.


Digital Twins – digital representations of physical as well as abstract
assets, e.g., a drilling machine or a production process – are the founda-
tion of digitalization efforts in production and logistics as they aim to
achieve consistent data homogeneity and interoperability. We conceive
Digital Twins as a collection of Aspects, where the Digital Twin estab-
lishes identity by representing a specified asset and the Aspects provide
a domain-specific view on this asset. Technically an Aspect is a soft-
ware service that offers functionality and data related to the represented
asset. Each Aspect references a concrete Aspect Model, which formally
describes the data that is provided by the Aspect. An Aspect Model
contains both information about the runtime data structure (e.g., that
there is a property in the data called "temperature", and that it has a nu-
meric value) and information that is not part of the runtime data (e.g.,
the physical unit and the value range). It does not, however, contain ac-
tual runtime data (e.g., a numeric value representing the current temper-
ature), as this will be delivered by an Aspect conforming to this Aspect
Model. The combination of raw runtime data and its corresponding As-
pect Model yields information.
The Open Manufacturing Platform specifies the BAMM Aspect Meta
Model (BAMM, [1]) and develops accompanying tooling to create,
manage, and leverage such Aspect Models. This helps to support
manufacturing organizations in setting up an environment to make
data-driven decisions that can help manage risk, optimize production,
and adopt and explore new revenue streams or business models.


*
    Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons Li-
    cense Attribution 4.0 International (CC BY 4.0).
Known standards such as IEC 61360 and international data dictionaries
like ECLASS [2] and IEC CDD [3] do not solve the problem to deter-
mine information from data in a specific context. A meta model is re-
quired allowing to express both schema information to perform data
validation and domain semantics to make implicit information explicit.
BAMM addresses both: Aspect Models express a schema with a de-
fined RDF [4] vocabulary and are validated by a comprehensive set of
rules in the Shapes Constraint Language (SHACL, [5]). Domain se-
mantics are captured by a combination of structural elements, relations,
namespaces and reified named concepts. RDF is ideally suited to ex-
press a graph-shaped model that is organized in multiple namespaces.
In comparison to OWL [6], BAMM favors aggregation over inheritance
in modeling, thus enabling domain experts without background in on-
tology engineering to create and maintain Aspect Models.
With the discussed challenges touching domains ranging from automo-
tive [7] to environmental policy making [8] BAMM has a broad field to
unfold its potential. We demonstrated this potential in internal applica-
tions, where over 15M product instances spanning over 0.5M product
types are represented with Digital Twins, that each have about 5 to 10
Aspects. Large scale product lifecycle management solutions exploit
the corresponding Aspect Models in processing and display of the data
pertaining to these products.
The talk will discuss BAMM as a case study for applying semantic
technology in the manufacturing domain and will focus on rationales
for its design and draws lines to adjacent Semantic Technologies. The
presentation will conclude with examples from and a discussion of the
anticipated impact on manufacturing industry.

References
1. Open Manufacturing Platform, BAMM Aspect Meta Model (2021).
   https://openmanufacturingplatform.github.io/, last accessed 2021-07-28
2. Eclass. https://www.eclass.eu/, last accessed 2021-07-28
3. IEC 61360-4, Common Data Dictionary (CDD - V2.0014.0017).
4. Cyganiak, R., Wood, D., Lanthaler, M., RDF 1.1 Concepts and Abstract Syntax.
5. Knublauch, H., Kontokostas, D., Shapes Constraint Language (SHACL).
6. Bock, C et al.: OWL 2 Web Ontology Language Structural Specification
7. Catena-X. https://catena-x.net/de, last accessed 2021/07/28
8. BMWi. Nachhaltige Produktion: Mit Industrie 4.0 die Ökologische Transformation aktiv
   gestalten. https://www.plattform-i40.de/, last accessed 2021/07/28