Modelling Capabilities for Functional Configuration of Part Feeding Equipment

Abstract

This paper introduces a configuration framework for automatic configuration of production systems. The proposed framework consists of three key aspects; 1) functional configuration, 2) interface configuration and 3) behavioral configuration, that together offers the ability to automatically identify production resources, and aggregate them to form a production system. The main focus of this work is to model functional capabilities to facilitate automatic suggestion of part feeding resources, and exemplifies different approaches to model part feeding capabilities. (Full text can be accessed from here: http://urn.fi/URN:NBN:fi:tty-201709201940)

Full text

2351-9789 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the scientific committee of the 27th International Conference on Flexible Automation and Intelligent Manufacturing
doi: 10.1016/j.promfg.2017.07.357
Procedia Manufacturing 11 ( 2017 ) 2051 – 2060
Available online at www.sciencedirect.com
ScienceDirect
27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017,
27-30 June 2017, Modena, Italy
Modelling Capabilities for Functional Configuration of Part Feeding
Equipment
Michael Natapon Hanssona,*, Eeva Järvenpääb, Niko Siltalab, Ole Madsena
aDepartment of Mechanical and Manufacturing Engineering, Aalborg University, 9220 Aalborg, Denmark
bDepartment of Mechanical Engineering and Industrial Systems, Tampere University of Technology, 33720 Tampere, Finland
Abstract
This paper introduces a configuration framework for automatic configuration of production systems. The proposed framework
consists of three key aspects; 1) functional configuration, 2) interface configuration and 3) behavioral configuration, that together
offers the ability to automatically identify production resources, and aggregate them to form a production system. The main focus
of this work is to model functional capabilities to facilitate automatic suggestion of part feeding resources, and exemplifies
different approaches to model part feeding capabilities.
© 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 27th International Conference on Flexible Automation and
Intelligent Manufacturing.
Keywords: Production System Configuration; Capability Matchmaking; Part Feeding; Knowledge Modelling
1. Introduction
Due to decreasing product life cycles and increasing demand for product variants in low volumes, changeability
has become a vital requirement for current production systems [1]. Important aspects for developing changeable
production systems are that production resources can be rapidly (re-)configured and deployed to accommodate
* Corresponding author. Tel.: +45-21281986.
E-mail address: mhanss@m-tech.aau.dk
© 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the scientific committee of the 27th International Conference on Flexible Automation and
Intelligent Manufacturing

2052 Michael Natapon Hansson et al. / Procedia Manufacturing 11 ( 2017 ) 2051 – 2060
changing requirements. This involves the abilities to rapidly identify feasible production system solutions, rapidly
add and/or remove production resources to/from the layout, and rapidly program or configure the control logics of
the resources and the production system as a whole [2]. While research within reconfigurable and evolvable
production systems has progressed the development of modular and intelligent resources that can be rapidly
reconfigured and deployed [3,4], designing such resources and configuring production system solutions still remains
a time-consuming activity [5]. Configuration of production systems involves solving certain design aspects such as
identifying relevant production resources that can be utilised, investigating whether the resources are compatible
with one another, layout of resources in combination, and verification and design optimisation of resources to ensure
production of a product [6,7]. To address the challenge of bringing changeability to production systems, there is a
need to develop methodologies, information models and tools for facilitating rapid configuration of production
systems in terms of automating the process of selecting production resources and aggregating systems out of them.
One type of production equipment that could greatly benefit from automatic configuration is part feeding
equipment. For assembly production, the term part feeding is used to describe equipment that handles two separate,
but closely connected process tasks, which consist of; part structuring and part presentation. Part structuring
concerns the process of singulating individual parts from a bundle of parts, and arranging individual parts from
having a random orientation towards a known orientation. Part presentation concerns the process of moving an
individual structured part to a predetermined location. There exist a vast range of resources that can be applied for
part feeding, but in general they can be divided into four different categories; mechanically based part feeders,
flexible part feeders, bin picking part feeders, and magasine part feeders [8].
The research for this paper focuses on the specific configuration aspect of how to enable automatic identification
of part feeding solutions (henceforth referred to as functional configuration). According to Lohse and Ratchev [9],
the development of a method for functional configuration needs to address two challenges. The first challenge is to
define knowledge models that describe the functionalities of a resource on a required level of detail so that it can be
used in the configuration process. The second challenge is to formalise methods and algorithms that can be used to
automate the configuration process in terms of matching production resources against product requirements.
In this paper, we will address the first challenge where the main focus is on the task of modelling functionalities
of part feeding resources to enable functional configuration. In Section 2, a framework for automatic configuration
of production systems is presented to describe the role and requirements for functional configuration. In Section 3,
we will review related work on how to conduct functional configuration. In Section 4, a capability-based approach
for functional configuration is elaborated, and in Section 5, we will address how to model functional capabilities of
part feeding resources to enable functional configuration using the capability-based approach.
2. Production System Configuration Framework
Before we address how functional configuration can be conducted, it is necessary to understand the context and
role this specific configuration aspect has in the process of configuring a production system. Fig. 1 illustrates the
proposed framework for production system configuration and consist of three configurational aspects: functional
configuration, interface configuration and behavioural configuration. The outset for automatic configuration of
production systems is that a set of candidate resources can be identified (functional configuration). Furthermore, in
order to validate if the candidates in combination are suitable for producing a specific product, the configuration
process needs to validate both if the resources are structurally compatible (interface configuration) and if they are
able to fulfill the desired behaviour for the specific production context (behavioural configuration). This of course
requires that product requirements and resources are described according to the information needed for each of the
configuration aspects.
In order to initiate the functional configuration process, the requirements for a solution need to be specified in
terms of required functionalities, and resources need to be described according to the functionalities that they
provide. For production equipment, functionalities are used to describe the capabilities of a resource that can be
utilised for a specific purpose. For example, part feeding equipment can be described as having the capabilities to
structure and present parts, which are capabilities that can be utilised to conduct the process of part feeding (as
described in Section 1). Functional configuration is initiated by a product requirement specifying a set of processes
that are needed to produce a product (e.g. part feeding, pick and place, milling and drilling). Identification of

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Michael Natapon Hansson et al. / Procedia Manufacturing 11 ( 2017 ) 2051 – 2060
resources is then conducted by searching for resources that offers the required functionalities to perform each of the
processes. As soon as a collection of resources has been identified that provides all of the required functionalities, it
is categorised as a candidate solution to produce the product.
After the functional configuration has found and grouped resources into candidate solutions, the interface
configuration can be initiated, where it has two purposes that have to be fulfilled. The first purpose of the interface
configuration is related to the layout and compatibility of a solution. For example, there should be a reasoning
mechanism to validate if a gripper can be structurally combined with a robot, and that the robot is not placed outside
of reach for placing a product part into a fixture. Another purpose of the interface configuration is to establish a
specification of the desired interaction between resources. For example, if a robot with a gripper has to pick a part
from a part feeder and place it in a fixture, the fixture itself will utilise a specific set of geometrical interfaces on the
part. The fixture will then dictate the available part interfaces that a gripper may utilise, which in turn dictate the
interfaces that should be accessible after the part has been fed from the part feeder. These chains of interaction
interfaces between resources and a part are necessary to establish because they further specify spatial requirements
that may affect the design of resources.
After the interface configuration has filtered out resources that cannot be structurally combined and has
established interaction interface specifications, the resources’ behaviour can be configured. A resource’s behaviour
can be viewed as a transformation system, where a resource will change the state of a part after interaction. The task
for the behavioural configuration is to; 1) verify that the state of a part is transformed according to requirements, and
2) optimise the design of a resource until a/the desired state is achieved and the operational requirements are met
(e.g. cycle-time and throughput). This of course requires that numerous design scenarios can be tested, which
requires that simulation- and optimisation methods are utilised to guide the behavioural configuration process. An
example of how behavioural configuration can be conducted for a vibratory bowl feeder is described by Hansson et
al. [10]. For processes that change a part’s geometrical structure (e.g. milling, drilling and welding), a specification
of input state and desired output state needs to be defined as part of the product requirements in order to conduct
behavioural configuration of resources that perform such processes. For logistical processes (e.g. part feeding and
transportation), the changes of state are primarily related to spatial changes. As mentioned previously, these are
specified according to the interaction interface specification, since such state description needs to be reasoned
between multiple resources in relation to a part.
Functional Configuration
Interface Configuration
Behavioural Configuration
Candidate
Resources
Compatible
Resources
Feasible
Resources
Establishment of
Solution Space
Refinement of
Solution Space
Verification and
Optimisation of
Solution Space
Configuration Procedure
Functional
Description
Resource Description
Interface
Description
Behavioural
Description
Process
Specification
Product Requirement
Process
Interaction
Interfaces
Business and
Operational
Specification
Product
Model
Product
Interface
Description
Property/
Feature
Description
Figure 1 - Framework for configuration of production system

2054 Michael Natapon Hansson et al. / Procedia Manufacturing 11 ( 2017 ) 2051 – 2060
3. Related Work
As mentioned in Section 1, the focus of this paper is on how to model capabilities that can be utilised to conduct
functional configuration of part feeding systems. In the following, we will briefly go through different
methodologies for modelling functional capabilities.
Kitamura et al. [11, 12] present an ontological model to describe device functions on the basis of a Function-
Behaviour-Structure (FBS) framework. The structural layer is used to describe the decomposition of
devices/resources, which are mapped to the behaviours that each device offers. Functions are described as a specific
utlisation of behaviours, to which the same behaviours can be described as offering different functionalities,
depending on the purpose they are used for. Since their primary focus for the framework has been on how to
conduct functional modelling of devices and how such modelling could be used as a communication tool between
engineers, the discussion of how their model could be integrated into a configuration process is limited.
Lohse [13] describes an ontological framework that proscribes how to model the domains of products, processes
and production equipment, and their interrelations. The modelling of production equipment has also been according
to a FBS approach. The framework’s initial purpose was to facilitate an easier process for system integrators to
select and configure production equipment. Although not focusing on the implementation of methods for automatic
configuration of production systems, Lohse argue that the models are developed to be incorporated in such process.
Järvenpää et al. [14] have developed an ontology-based Capability Model, which is used to describe the
capabilities of production resources. The representation includes the capability name and the parameters related to
the specific capability. The model builds links between simple and combined capabilities, and therefore allows the
automatic identification of potential resource combinations for certain product requirement represented on a
combined capability level.
Pfrommer et al. [15] describe a skill-based framework that models the relation between products, processes and
resources. Skills are used to describe functionalities of resources that can be used to conduct production processes.
Furthermore, skills can be mapped to control logic, e.g. PLC- and robot programs, that specifies how a skill is
achieved for a specific resource. Skills can be used to formulate tasks, which describes how a product should be
produced. However, the discussion on how resources are identified is limited, since the focus primarily is on rapid
deployment of resources.
Antzoulatos et al. [16] describe a framework for plug-and-produce production, where automatic identification of
required resources can be derived from product specifications. Resources are described according to functional
capabilities, and uses specific rules to match a product specification against capabilities. Each capability can be
translated to coherent control logic, so that rapid deployment of the resource can be facilitated.
Most of the available approaches presented above are developed for specific domains with limited focus on how
the functional capabilities of part feeding resources can be modelled. Furthermore, since the primary focus has been
on establishing the methodologies, limited attention has been on describing different modelling approaches and how
they influence the automatic identification of resources.
4. Functional Configuration using a Capability-based Approach
In this paper, the outset for conducting functional configuration is by using a capability-based modelling and
matchmaking approach [14]. As illustrated in Fig. 2, the basis for the capability-based approach is a capability
model that describes how capabilities are combined and how they can be utilised to perform certain production
processes. Resources are described according to the capabilities they provide, and product requirements specifies the
required processes needed to produce a product. Thereby, a relation between resources and product requirements
can be established by utilising the capability model as a matchmaking protocol and a search tree for identifying
candidate resource combination.
In the following, the basic concept behind the capability model will be described, along with how it can be used
as a basis for conducting a matchmaking procedure. How the matchmaking process is specifically conducted is out
of the scope of this paper, but is explained with more details in Järvenpää et al. [17].

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The capability model is a knowledge model for describing the functionalities that resources provides in terms of
capabilities (such as moving and grasping). Furthermore, to create a mapping between resources and product
requirements, processes are defined according to the required capabilities needed to realize the process. As
illustrated in Fig. 3, capabilities can be divided into two types; simple capabilities and combined capabilities. Simple
capabilities are lower level capabilities that can be assigned to resources. Combined capabilities are upper level
capabilities that are derived by combining multiple capabilities. In order to specify how combined capabilities are
established, both simple and combined capabilities can be defined to provide capability associations, and combined
capabilities can be defined according to required capability associations. Combined capabilities are primary used to
manage capabilities that derives from co-operating resources, but can also be used to decompose capabilities into a
hierarchical structure.
Functional Configuration
feeding
passiveReorientation
orientationRejectionAssociation objectCirculationAssociation
passiveOrientationRejectionactiveOrientationRejection
reorientationAssociation
physicalOrientationAdjustment
orientationRecognitionAssociation
objectDischargeAssociation
physicalSingulation
objectSingulationAssociation
objectSingulation
objectCirculation
physicalObjectPresentation
objectDischarging
orientationRecognition
escapementAssociationescapementDeleveryAssociation
passiveEscapementDelevery activeEscapementDelevery singleObjectEscapement multibleObjectEscapement
physicalOrientationSorting
informationalSingulation
objectRecognitionAssociation positionRecognitionAssociation
objectRecognition positionRecognition
informationalObjectPresentation
informationalSingulationAssociation
physicalSingulationAssociation
presentationAssociation
physicalObjectStructuringAssociation
MagasinStructuring
physicalBulkStructuring
physicalOrientingAssociation
informationalObjectStructuringAssociation
informationalBinStructuring informational Structuring
physicalSeperationAssociation
bulkSeparation
Capability Model
Product Requirement
Capability Matching
Resource Description
Capability x
- Parameter 1
- Parameter 2
Capability y
- Parameter 1
Candidate Resources
Part x
State 1
Part Feeding
Pick and
Place
C
C
...
Part x
State 1
Part x
State 1
Part Feeding
Pick and
Place
...
Part x
State 1
Figure 2 - The principle for functional configuration using a capability-based approach
Process
Capability
Simple
Capability
Capability
Association
Combined
Capability
enablesProcess
(eP)
relation isA
requiresCapabilityAssociation
(rCA)
providesCapabilityAssociation
(pCA)
providesCapabilityAssociation
(pCA)
2..*1..*
0..*
1..*
Resource
hasCapability
(hC)
1..*
0..*
0..*
1..*
Figure 3 - The primary concepts and relations of the capability model

2056 Michael Natapon Hansson et al. / Procedia Manufacturing 11 ( 2017 ) 2051 – 2060
Fig. 4 shows an example of a capability model that can be used to identify resources that can conduct a Pick and
Place process. Here, Pick and Place is specified as requiring the capabilities of Picking, Placing and Transporting,
which are combined capabilities that each requires specific capability associations. For instance, in order to pick a
part, the system needs to be able to move to a specific location and grasp a part. This is captured by specifying that a
Moving Association and a Grasping Association are required. There may be multiple capabilities that offers such
capability association, for instance, both the Vacuum Grasping and Finger Grasping capabilities offers a Grasping
Associations, to which the capability model can be used to capture different resources that can be used to pick an
item. Through this specific example of the capability model, two candidate solutions for a pick and place process
can be identified; a robot arm with a vacuum gripper and a robot arm with a finger gripper.
5. Capability Modelling of Part Feeding Equipment
In this section, we will investigate how a model can be established to enable functional configuration of part
feeding resources. As an example, Fig. 5 depicts the main identified capabilities of two different types of part
feeding resources; a vibratory bowl feeder (mechanically based part feeder) and a flexible feeder. As mentioned in
Section 1, part feeding processes can generally be described to handle two tasks; part structuring and part
presentation. In the following, only capabilities related to part structuring processes have been modeled for the part
feeding resources. Capabilities should of course be defined on the granularity level on which the configuration and
Picking
Pick and
Place Placing
Transporting
Finger
Grasping
Releasing
Moving
Releasing
Association
Grasping
Association
Moving
Association
Finger
Gripper
Robot Arm
Vacuum
Gripper
Vacuum
Grasping
Process Combined Capability Capability Association Simple Capability Resource
rCA
rCA
rCA
rCA
rCA
rCA
rCA
pCA
pCA
pCA
pCA
hC
hC
hC
hC
hC
relation
Transporting
Association
Placing
Association
Picking
Association
Capability Association
Pick and
Place
Combined Capability
pCA
pCA
pCA
rCA
rCA
rCA
eP
Figure 4 - Example of capabilities and capability associations for defining a pick and place process, and the linking to relevant resources
Camera
Base UnitBase Unit
Bowl
Part
Dispenser
Orientation
Recognition
Part
Recognition
Storaging
Postion
Recognition
Vibratory Bowl Feeder Flexible FeederResources Simple Capabilities
Vibrating
Vibration
Transferring
Storaging
Vibration
Transferring
Physical
Singulation
Vibrating
Vibration
Transferring
Physical
Orientation
Resources Simple Capabilities
hC
hC
hC
hC
relation
hC
hC
hC
hC
hC
hC
hC
hC
Figure 5 - Capabilities of part feeding resources

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Michael Natapon Hansson et al. / Procedia Manufacturing 11 ( 2017 ) 2051 – 2060
reconfiguration should take place. In Fig. 5, the vibratory bowl feeder is assumed to consist and be configured from
a bowl and base unit, and therefore the capability model needs to have relevant capabilities for these resource
entities. If the base unit of the vibratory bowl feeder was chosen to be described as consisting of resources such as
spring packs and driving coils, the capabilities would also be required to be described at a finer granularity level in
order to enable functional configuration.
Fig. 5 illustrates the capabilities from the resources individual perspective, but in order to facilitate functional
configuration, it is necessary to define a capability model that captures that both a vibratory bowl feeder and a
flexible feeder are candidate resources to perform part feeding. There are different approaches to defining a
capability model, each having specific advantages and disadvantages. In the following, three different approaches
will be exemplified: an accumulating approach, a back-tracking approach, and a technological approach.
Fig. 6 depicts a capability model using the accumulative approach. Here the focus is to model capabilities from a
bottom-up perspective by focusing on each individual capability and how capabilities are accumulated to form other
generic capabilities. The advantage of using this approach is that capabilities offering similar functionalities can
easily be defined as alternative implementations. The disadvantage is that capability constraints are not captured by
the capability model and needs to be incorporated as additional constraint rules in the matchmaking procedure. For
instance, in order to physically orient a part, it requires that a constraint rule is defined to capture that the capability
Physical Orientation only can be selected in combination with Physical Singulation.
Fig. 7 shows a capability model using the back-tracking approach. Here the focus is to model capabilities from a
top-down perspective, where capabilities are structured according to the reverse sequence of a process. The
advantage of using this approach is that the approach seeks to differentiate different technological implementations
(vibratory bowl feeding vs. flexible feeding) at an early stage, and thereby removes the need for establishing
constraint rules to capture dependencies between different capabilities. The disadvantage is that adding new
technological implementations of part feeding resources may need substantial changes of the model. For example,
adding bin picking capabilities requires that the capability Informational Structuring is reformulated to capture that
a Conveying Association only is required for flexible part feeders.
Fig. 8 illustrates how a capability model would be formed using the technological approach. Here the focus is to
model capabilities according to the different technological implementations. The outset for this approach is that each
Physical OrientationOrientation Recognition
Part RecognitionPostion Recognition
Vibrating
Association
Vibration
Transferring
Physical Singulation Informational
Singulation Vibrational Conveying
Orienting
Association
Vibration
Transferring Association
Singulating
Association
Storaging
Structuring
Postion Recognition
Association
Part Recognition
Association
Vibrating
Storaging
Association
Conveying
Association
Requires (Constraint Rule)
rCA
rCA rCA
rCA
rCA rCA rCA rCA
pCA pCA pCA pCA pCA pCA
pCApCApCApCA
Requires (Constraint Rule)
Part Feeding
relation
Structuring
Association
Presentation
Association
pCA
Part Feeding
rCA rCA
eP
Figure 6 - Capability modelling of part feeding resources according to an accumulative approach

2058 Michael Natapon Hansson et al. / Procedia Manufacturing 11 ( 2017 ) 2051 – 2060
technological implementation is defined as a specialised capability that enables part feeding. Furthermore, each
feeding capability is defined as conducting the task of part structuring and part presentation, and thereby only
capabilities that specifically handles these functionalites are required to be associated with the part feeding
capabilities. The advantage of using this approach is that the incorporation of new part feeding technologies can be
added without having to rationalise how it differs from previously modeled technologies. The disadvantage is that
the specialized capabilities acts more as a classification, rather than generic capabilities that can be used for
describing the capabilities of other resources.
As can be seen from the presented examples for modelling part feeding capabilities, the approaches differ in
terms of maintainability and requirements for the matchmaking procedure. The accumulative approach is useful for
modelling capabilities that have similar purposes, but requires that additional constraint rules are specified between
capabilities in order to filter out resources that cannot be functionally combined. The back-tracking approach is able
to capture these constraints directly in the capability model, but is not suited for continues addition of new
Physical OrientationOrientation Recognition
Part RecognitionPostion Recognition
Vibrating
Association
Vibration
Transferring
Physical Singulation
Informational
Singulation Vibrational Conveying
Vibration
Transferring Association
Storaging
Postion Recognition
Association
Part Recognition
Association
Vibrating
Storaging
Association
Conveying
Association
rCA rCA rCA rCA
pCA pCA
pCApCApCApCA
Informational
Singulation Association
Orientation Recognition
Association
Informational Structuring
pCApCA
Physical Structuring
Physical Orientation
Association
Physical Singulation
Association
pCA pCA
Informational
Structuring Association
Informational
Presentation
Part Feeding
eP
rCA rCA rCA rCA rCA rCA rCA rCA
Physical Structuring
Association
Information Transferring
Association
Escapment
Association
rCA
rCA
Physical Presentation
rCA rCA
eP
pCA pCA
relation
Figure 7 - Capability modelling of part feeding according to a back-tracking approach
Orientation Recognition
Part RecognitionPostion Recognition
Informational
Singulation
Postion Recognition
Association
Part Recognition
Association
rCA rCA
pCApCA
Part Feeding
Flexible Feeding
Vibratory Bowl Feeding
Physical Orientation Physical Singulation
Vibrating
Association
Vibration
Transferring
Vibrational Conveying
Vibration
Transferring Association
Storaging
Vibrating
rCA rCA
pCApCA
Storaging
Association
Conveying
Association
Informational
Singulation Association
Orientation Recognition
Association
Physical Orientation
Association
Physical Singulation
Association
eP eP
pCA pCA pCA pCA pCA pCA
rCA rCA rCA rCA
rCA rCA rCA
rCA
relation
Figure 8 - Capability model of part feeding resources according to a technological approach

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Michael Natapon Hansson et al. / Procedia Manufacturing 11 ( 2017 ) 2051 – 2060
technological implementations of resources. The technological approach is able to both capture constraints between
capabilities and enables continues incorporation of new technological implementations of resources, since the
approach seeks to model such technologies independently from each other.
6. Conclusion
In this paper, a framework for automatic configuration of production systems has been presented. The framework
introduces three different configuration aspects that cover how production resources can be identified, and how the
configuration process can be conducted to aggregate them into production systems. The presented framework is
expected to increase the responsiveness of production companies, in terms of reducing the development time of
production systems.
The primary focus of the paper is to describe how automatic identification of part feeding resources can be
conducted, for which a capability-based matchmaking procedure is used to conduct the identification of candidate
resources. The basis for the procedure is the definition of a capability model, to which various approaches to model
part feeding capabilities have been presented. Through the examples provided in this paper, the technological
approach is argued to be a suited approach for modeling part feeding capabilities, since it enables the modelling of
constraints between capabilities and offers continuous incorporation of different part feeding resources. Future work
will be focused on expanding the capability model for part feeding, together with implementation of the
matchmaking procedure. Furthermore, since the modelling approaches only been used to model part feeding
resources, further research has to be conducted to verify the approaches against other types of resources.
Acknowledgements
This work was supported by The Danish Innovation Foundation through the strategic platform MADE-Platform
for Future Production. Furthermore, it has received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement no 680759 (project ReCaM).
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