Emilio M Sanfilippo
email: emiliosanfilippo@gmail.com

Farouk Belkadi

Alain Bernard

Ontology-based knowledge representation for additive manufacturing

Keywords: manufacturing, additive manufacturing, knowledge representation, reasoning, ontology, DOLCE manufacturing, additive manufacturing, knowledge representation, reasoning, ontology, DOLCE

The flourishing development of additive manufacturing (AM) technologies calls for robust IT methodologies and solutions to manage the plethora of data that is generated in the AM value chain. The purpose of this paper is to propose a principled knowledgebased model for AM in the form of a computational ontology. As corpus of formally represented knowledge, the ontology constitutes the backbone structure to organize AM data and automatically reason over experts' knowledge for data validation, ultimately supporting the development of algorithms and applications for decision making. By the end of the paper we show some modeling and reasoning examples based on the use of the proposed ontology in a prototype Web application.

Introduction

The development and industrial adoption of additive manufacturing (AM) technologies is bringing about a situation where the entire product development chain is digitalized, from the initial design of products to the simulation of processes for the optimization of fabrication, or the maintenance of products via online service systems [START_REF] Kim | Streamlining the additive manufacturing digital spectrum: A systems approach[END_REF][START_REF] Mies | Overview of additive manufacturing informatics:"a digital thread[END_REF]. Additionally, since AM machines are fully equipped with sensors, a plethora of data about AM processes is nowadays available to companies, which need methodologies and technologies to manage the data and capitalize the experience thereby stored [START_REF] Belkadi | Towards an unified additive manufacturing product-process model for digital chain management purpose[END_REF][START_REF] Thompson | Design for additive manufacturing: Trends, opportunities, considerations, and constraints[END_REF]. 1One of the challenges brought about by the organization of the AM value chain is the development of computer systems able to aid decision making but also to support data management in a homogeneous and consistent way [START_REF] Bonnard | A new digital chain for additive manufacturing processes[END_REF]. Computer Decision Aid systems play an important role for AM to assist experts in selecting fabrication parameters (among other relevant decisions), whose dependencies and inter-relations need to be carefully evaluated and analyzed to gain full control over fabrication [START_REF] Criales | Laser powder bed fusion of nickel alloy 625: experimental investigations of effects of process parameters on melt pool size and shape with spatter analysis[END_REF][START_REF] Gibson | Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing[END_REF]. Concerning data organization, different working groups rely on their own models to store and manage data [START_REF] Mies | Overview of additive manufacturing informatics:"a digital thread[END_REF]. A consequence is that when multiple communities need to share and possibly integrate data, there is no guarantee for the data to be manipulated in an interoperable manner [START_REF] Borgo | An ontological approach for reliable data integration in the industrial domain[END_REF][START_REF] Negri | Requirements and languages for the semantic representation of manufacturing systems[END_REF].

A way to both support decision making and tackle data management issues is by means of knowledge-based systems, where experts' knowledge is encoded in formal languages whose semantic is processable by computer systems. Ontologies [START_REF] Guarino | What is an ontology? In Handbook on ontologies[END_REF] have proven useful to both exchange data across applications and automatically reason over knowledge and data for knowledge discovery, constraints validation or decision making (see, e.g., [START_REF] El Kadiri | Ontologies in the context of product lifecycle management: state of the art literature review[END_REF][START_REF] Young | Manufacturing knowledge sharing in PLM: A progression towards the use of heavy weight ontologies[END_REF]).

The purpose of this research work is to propose an ontology for AM that is application and vendor independent to be reused across computer systems to support knowledge and data management in an interoperable manner. Also, since the model formally represents experts' knowledge in a computer amenable way, it can constitute the knowledge base for decision making algorithms to be exploited in AM applications.

The remaining of the paper is structured as follows. The next section introduces the context of our research and addresses the need for an ontology-based modeling approach. The state of art relevant to our work is presented in Section 3. The ontology is presented throughout Section 4, whereas Section 5 exemplifies the use of the ontology in a prototype Web application. Section 6 concludes the paper by addressing some challenges for future research work. Finally, the Appendix shows some queries and reasoning expressed over the knowledge base.

Research context

From both the academic literature [START_REF] Gu | Laser additive manufacturing of metallic components: materials, processes and mechanisms[END_REF][START_REF] Wang | Selection of additive manufacturing Rapid Prototyping[END_REF][START_REF] Zhang | An integrated decision-making model for multiattributes decision-making (madm) problems in additive manufacturing process planning[END_REF]] and real-world industrial practices, 2 it emerges that the data manipulated in AM tasks is highly heterogeneous in terms of both their originating sources and intended meaning. Figure 1 provides an overall view on the entities involved in the AM value chain. The idea is that an AM machine, which lies at the centre of the figure for its importance, requires and produces a plethora of data, which need to be accessed by different parties and computer applications. In order to make it possible, a knowledge base shared by all applications in the loop is necessary. Since data have to be managed in an integrated way, knowledge models able to deal with the representation of entities like processes, products, materials, and machines, including the relations among them, are needed [START_REF] Belkadi | Towards an unified additive manufacturing product-process model for digital chain management purpose[END_REF][START_REF] Liang | An ontology-oriented knowledge methodology for process planning in additive layer manufacturing[END_REF][START_REF] Witherell | Additive manufacturing: A transdisciplinary experience[END_REF]. These models have to be (i) general enough to encompass multiple entities and points of view, (ii) easily extendable according to application requirements, and (iii) vendors-independent to be easily reused and shared. Differently, companies manage data through proprietary legacy systems, which are developed to match very specific requirements [START_REF] Mies | Overview of additive manufacturing informatics:"a digital thread[END_REF]. It is not surprising that when multiple stakeholders need to interact by mutually exchanging data, the intended meaning of the data is often lost [START_REF] Negri | Requirements and languages for the semantic representation of manufacturing systems[END_REF][START_REF] Palmer | Interoperable manufacturing knowledge systems[END_REF]. Additionally, models for AM need to take into account the representation of (process, product, machine, etc.) parameters to explicitly analyze and specify their inter-relations and dependencies, as well as to aid experts during decision making procedures [START_REF] Belkadi | Towards an unified additive manufacturing product-process model for digital chain management purpose[END_REF][START_REF] Zhang | An integrated decision-making model for multiattributes decision-making (madm) problems in additive manufacturing process planning[END_REF].

Figure 1: Broad view on the entities involved in the AM value chain

To deal with knowledge representation, data management, and computer aided decision making, we propose the development of computer applications based on knowledge engineering modeling techniques. In particular, the proposal explored in the paper is the development of a knowledge base as reference model to manage AM data and knowledge in a homogeneous and consistent way. Figure 2 exemplifies our approach. By looking at the figure, the knowledge base is the ground element to guarantee the seamless organization of data in a common repository, but also to aid decision making on, e.g., the selection of the parameters that are better suited to fabricate products.

As mentioned in the Introduction, the proposed knowledge base takes the form of a (populated) ontology, i.e., a vocabulary of terms which are formally characterized to be accessible by both computer systems and human agents [START_REF] Guarino | What is an ontology? In Handbook on ontologies[END_REF]. When multiple systems rely on the same ontology, the data they handle is semantically described in the same way. Also, since ontologies encode experts' knowledge in formal or even computational logics, they can be used to automatically reason over knowledge and data.

Ontologies are broadly used in the engineering domain, and international activities led by both academia and industry currently aim at creating libraries of reference ontologies for reuse. 3 However, because of the novelty of approaches dealing with the organization of the AM value chain, the literature on ontologies for AM is poor in comparison with domains like machining or assembly (see, e.g., [START_REF] Imran | The application of common logic based formal ontologies to assembly knowledge sharing[END_REF][START_REF] Negri | Requirements and languages for the semantic representation of manufacturing systems[END_REF][START_REF] Terkaj | Ontology-based modeling of production systems for design and performance evaluation[END_REF]). The next section provides a Figure 2: Knowledge-based approach review of the state of art relevant to our purposes; by identifying the limits of current works, we introduce the ontology in Section 4.

Ontologies for additive manufacturing

Researchers at the U.S. NIST have pioneered the use of ontologies for AM applications. In [START_REF] Roh | Ontology-based laser and thermal metamodels for metal-based additive manufacturing[END_REF][START_REF] Witherell | Toward metamodels for composable and reusable additive manufacturing process models[END_REF] the authors propose an ontology primarily targeted to the data management of powder bed fusion processes. The ontology builds on classes to represent materials, products' components, process parameters, and processes' parts. Unfortunately, the ontology blurs basic ontological modeling principles like the distinction between subsumption and parthood relations, or between contextual and non-contextual knowledge. For instance, the class for parameters subsumes lasers and sensors. It should be clear that the latter count as parameters only within specific application contexts; e.g., a laser is not a parameter for its manufacturer. Modeling pitfalls concerning the erroneous use of subsumption and parthood occur in [START_REF] Dinar | A design for additive manufacturing ontology[END_REF][START_REF] Liang | An ontology-oriented knowledge methodology for process planning in additive layer manufacturing[END_REF], too, preventing the reuse of the ontologies.

Eddy and colleagues [START_REF] Eddy | Knowledge management with an intelligent tool for additive manufacturing[END_REF] propose an ontology targeted to AM process planning. The ontology covers the ASTM classification of AM processes (cf. [START_REF] Gibson | Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing[END_REF]), as well as various classes for processes' participants. The ontology has been recently re-engineered [START_REF] Hagedorn | A knowledge-based method for innovative design for additive manufacturing supported by modular ontologies[END_REF] according to the upper-level ontology called Basic Formal Ontology (BFO) [START_REF] Arp | Building ontologies with basic formal ontology[END_REF] and BFO-compliant ontologies like the Relation Ontology4 and the Information Artifact Ontology (IAO). 5 It goes in the same direction the work presented in [START_REF] Ali | A product life cycle ontology for additive manufacturing[END_REF], where an ontology for AM based on the BFO, the IAO, and the Common Core Ontology (CCO) 6 , among others, is introduced. The comparison and possibly interoperability with the ontology in [START_REF] Hagedorn | A knowledge-based method for innovative design for additive manufacturing supported by modular ontologies[END_REF] is however left by the authors to future work.

Although the better quality of the ontologies in [START_REF] Ali | A product life cycle ontology for additive manufacturing[END_REF][START_REF] Eddy | Knowledge management with an intelligent tool for additive manufacturing[END_REF][START_REF] Hagedorn | A knowledge-based method for innovative design for additive manufacturing supported by modular ontologies[END_REF] in comparison with the ontologies previously discussed in this section (e.g., [START_REF] Ali | A product life cycle ontology for additive manufacturing[END_REF][START_REF] Eddy | Knowledge management with an intelligent tool for additive manufacturing[END_REF][START_REF] Hagedorn | A knowledge-based method for innovative design for additive manufacturing supported by modular ontologies[END_REF] properly distinguish between different relation types), we refrain from reusing them because of the ambiguous nature of some modeling elements imported from BFO-compliant ontologies. In particular, the class InformationContentEntity, which lays at the heart of the IAO, has instances things like design specifications or process plans, which are meant to be about some entities in reality [START_REF] Ceusters | Towards foundations for the information artifact ontology[END_REF]. This is not necessarily true in domains like product design or manufacturing; e.g., in a common planning scenario, at the time in which a process plan is created, there is not the corresponding process, which may never occur if the plan is never realized. 7Finally, Qi and colleagues [START_REF] Qi | A categorical framework for formalising knowledge in additive manufacturing[END_REF] present a preliminary study based on category theory to model the inter-relations between AM parameters, e.g., between layer thickness and particle size distribution. However, because of the adopted formalism, it remains unclear how the ontology can be exploited for application purposes, e.g., to manage or validate stakeholders' data against formally encoded knowledge.

Table 1 provides a summarizing comparison of the state of art in order to better frame the scope of our research. The columns Product, Process, Resource, and Parameters are used to assess whether the ontologies cover modeling elements about those entities. As it can be seen from the table, the ASTM classification of AM processes is covered only by some ontologies. Also, the representation of parameters greatly varies across the papers depending on application requirements. The Reused ontologies and Modeling language columns are self-explicatory; Semantic Web languages like the Web Ontology Language (OWL) [START_REF] Group | OWL 2 web ontology language document overview[END_REF] and SWRL [START_REF] Horrocks | Swrl: A semantic web rule language combining owl and ruleml[END_REF] are mostly used. 8The last row of the table introduces the work presented in the following sections. Similarly to (some of the) existing works, the ontology covers the representation of products, processes, and resources. Also, to support a wide modeling of parameters, we rely on the scientific literature on AM, the expertise of academics and industrial stakeholders, as well as AM machines, products, and software vendors' catalogues. From an ontology engineering perspective, on the one hand, differently from [START_REF] Eddy | Knowledge management with an intelligent tool for additive manufacturing[END_REF][START_REF] Liang | An ontology-oriented knowledge methodology for process planning in additive layer manufacturing[END_REF][START_REF] Qi | A categorical framework for formalising knowledge in additive manufacturing[END_REF][START_REF] Roh | Ontology-based laser and thermal metamodels for metal-based additive manufacturing[END_REF][START_REF] Witherell | Toward metamodels for composable and reusable additive manufacturing process models[END_REF], the ontology is founded on rigorous modeling principles like the ones discussed in [START_REF] Guarino | An overview of OntoClean[END_REF][START_REF] Guizzardi | Ontological foundations for structural conceptual models[END_REF] hand, the ontology trades-off generality with domain-specificity in such a way to cover a broad range of concepts to deal with the heterogeneity of AM knowledge and data, as well as to be easily extended to specific application scenarios. For this purpose, differently from [START_REF] Ali | A product life cycle ontology for additive manufacturing[END_REF][START_REF] Hagedorn | A knowledge-based method for innovative design for additive manufacturing supported by modular ontologies[END_REF], the ontology is based on the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) [START_REF] Borgo | Foundational choices in DOLCE[END_REF][START_REF] Masolo | Wonderweb deliverable D18[END_REF] as core methodology for knowledge organization, classification, and conceptual analysis. We rely on DOLCE, first, because it covers (high-level) modeling elements which are needed for knowledge representation in manufacturing, e.g., the distinction between objects, processes, and qualities (see Section 4), but also relations like parthood or participation that are necessary to model the structure of complex items like assemblies and the links between objects and processes, respectively. Second, because DOLCE has been already used for modeling engineering design and manufacturing knowledge (e.g., [START_REF] Borgo | Knowledge-based adaptive agents for manufacturing domains[END_REF][START_REF] Borgo | Foundations for a core ontology of manufacturing[END_REF][START_REF] Solano | Knowledge representation for product and processes development planning in collaborative environments[END_REF]). Third, because DOLCE is one of the upper-level ontologies guiding the development of a knowledge-based architecture for the Industrial Ontology Foundry (IOF) (cf. Section 2). Therefore, by relying on DOLCE, we support and facilitate the comparison and interoperability with IOF modeling efforts. Since DOLCE is formalized in first-order (modal) logic, we use it as reference framework, whereas the OWL ontology presented in Section 4 simplifies and adapts it to the expressivity of Semantic Web languages.

The proposed ontology for additive manufacturing

For limits of space the following sections introduce only some modeling elements of the ontology; the reader can refer to the ontology file for an insightful overview of the ontology's structure. 9 Section 4.1 presents the upper-level branch of the ontology, whereas the AM branch is introduced in Section 4.2.

The ontology is developed in OWL by means of Protégé10 . For the sake of readability, we use the syntax of Description Logics [START_REF] Baader | The description logic handbook: Theory, implementation and applications[END_REF] throughout the paper. The languages SWRL and SPARQL are used for expressing rules and queries, respectively. 11

The upper-level branch of the ontology

One of the core purposes of upper-level ontologies is to facilitate the grouping of classes sharing common high-level characteristics, e.g., entities that are primarily extended in space like machines, materials, or manufacturing tools, and entities that unfold through time such as manufacturing or maintenance processes. Therefore, by using upper-level classes and relations one avoids the duplication of axioms across a domain-specific ontology. Also, when multiple application systems are aligned to the same upper-level, they have a higher chance of interoperating for the fact of sharing the same core elements [START_REF] Keet | The use of foundational ontologies in ontology development: an empirical assessment[END_REF].

The most general classes of our ontology, which are all disjoint from each other, are shown in Figure 3. Following DOLCE, objects12 are entities that can change through time while retaining their identities, e.g., a manufacturing tool which remains the same entity (indeed, it keeps the same product ID) although the changes it undergoes due to wear. Examples of objects are AM machines and tools, but also entire factories, simple and assembled products, persons, design specifications, and manufacturing plans.

In order to organize the spectrum of entities classified by Object, this class is specialized in PhysicalObject and Description (see Figure 3). The first class models objects having a spatial location, e.g., persons, tools, or products. Physical objects are particularly relevant for manufacturing modeling; hence, PhysicalObject is further specialized in various classes (cf. Section 4.2). The second class groups entities like plans and design specifications from the perspective of their 'content'. For example, when one creates a CAD model saved in a STL file and duplicates the file, the result is two copies of exactly the same model. It is this latter entity that we call description (cf. [START_REF] Mizoguchi | Part 3: Advanced course of ontological engineering[END_REF] for a similar approach). 13Process is the most general class for entities that unfold through time. 14 Following DOLCE, objects can take part in processes in various ways; participation is the most general link holding between objects and processes. We will see in the next section how both Process and participation are extended.

icalObject covers the class DescriptionSupport. According to the ontology engineering literature (e.g., [START_REF] Wang | Towards an ontology of software: A requirements engineering perspective[END_REF]) digital files are physical objects since they are stored in the physical memory cells of hardwares, hence they have spatial locations. The ontology we present can be consistently extended to model the distinction between digital and non-digital physical objects, if needed by application requirements.

14 DOLCE uses the term 'perdurant' whereas we use 'process' because this is a more common term in manufacturing [START_REF] Solano | Knowledge representation for product and processes development planning in collaborative environments[END_REF].

The class Material 15 is the most general classifier for amounts of matter, e.g., the materials used to fabricate products by AM processes. With this class one can easily model the distinction between material and immaterial objects. Example of the latter are hole or pocket features [START_REF] Sanfilippo | What are features? an ontology-based review of the literature[END_REF].

The use of the class NonQuantitativeValueSpace has to be contextualized within a global approach to model what DOLCE calls qualities, i.e., the characteristics of objects, processes, or materials, e.g., weight, height, diameter, speed, material state, etc. In ontology engineering, various approaches have been proposed for representing qualities, e.g., [START_REF] Guizzardi | Ontological foundations for structural conceptual models[END_REF][START_REF] Masolo | Qualities in formal ontology[END_REF][START_REF] Rijgersberg | Ontology of units of measure and related concepts[END_REF]. For our purposes, we trade-off conceptual expressivity with application concerns; hence, in order to distinguish between qualities and their values, to make sense that qualities' values can be provided according to (different) measurement units, but also to facilitate the creation of data models, we model qualities via relationships rather than classes. In particular, by taking benefit of the OWL distinction between data properties and object properties, we distinguish between quantitative and non-quantitative quality-relations, namely, (i) relations linking instances of Object, Process, Material, or Type (see below) to numerical values (e.g., being 30kg heavy or being 3mm height), and (ii) relations linking instances of these classes to non-numerical values (e.g., being light, being flake-shaped, being small, etc.). The class NonQuantitativeValueSpace is used to collect the latter values. E.g., the data property hasThicknessInMillimetre can be used to model the numerical value of a product's layer thickness, whereas the object property hasShapeValue can be used to model, e.g., the flake shape of metal powders. 16 Finally, the class Type is used when quantification over properties is needed within the expressivity of languages based on first-order logic like OWL [START_REF] Borgo | Foundational choices in DOLCE[END_REF][START_REF] Sanfilippo | Design knowldege representation: An ontological perspective[END_REF]. 17 For manufacturing knowledge representation, this approach is common, e.g., for process planning, when one needs to model the type of machine that is required to be used without however specifying the individual machine that will be possibly used [START_REF] Sanfilippo | Modeling manufacturing resources: An ontological approach[END_REF]. 18 Similarly, in the context of design, reference to types is necessary to talk about the product types under design, whose physical realizations do not necessary exist at the designing time [START_REF] Sanfilippo | Design knowldege representation: An ontological perspective[END_REF]. In the ontology, the relation isOftype binds an instance of Material, Object, or Process to a type; e.g., one can say that the material constituting a certain product is of type NickelAlloy718 (cf. next section).

Table 2 reports the relations used in the upper-level branch of the ontology. 15 In DOLCE this class is called 'amount of matter'. 16 Quantitative and non-quantitative values can be easily related. E.g., one may formally establish that layers qualify as small when their thickness is less than 0.2mm. 17 There exists various theories of types in ontology engineering (e.g., [START_REF] Herre | General formal ontology (GFO): A foundational ontology for conceptual modelling[END_REF]). Types with a cognitive dimension can be represented in DOLCE (Core) through the Concept class [START_REF] Borgo | Foundational choices in DOLCE[END_REF]. 18 In the context of process modeling, the reader can refer to the distinction between Activity (type) and ActivityOccurrence in the Process Specification Language (ISO 18629) [START_REF] Grüninger | Using the PSL ontology[END_REF].

Relation

The additive manufacturing branch of the ontology

This section shows the extension of the upper-level branch to additive manufacturing. The ontology includes broad classes and relations, which can be easily specialized to meet specific modeling scenarios and requirements.

Machines, products, and features. The class PhysicalObject is extended in two main classes, namely, Person and PhysicalArtefact. The first one is useful to model human agents, e.g., taking part in manufacturing processes, whereas the second one is the most general classifier for physical objects that are created to satisfy market requirements.

The two classes are therefore disjoint, since they cannot have common instances.

Person is specialized to cover roles that human agents may play in engineering contexts. Examples are MachineOperator and MfgEngineer, 19 among others.

PhysicalArtefact covers various classes, among which MfgDevice, Product, and Feature are particularly interesting from a manufacturing modeling stance. MfgDevice is a general classifier to better organize subclasses of PhysicalArtefact, e.g., to explicitly state disjointness axioms with other classes. Its instances are made of material and may be adopted in manufacturing processes; examples are machines and their components (e.g., sensors, tools, engines, energy sources, etc.). Product -as the label suggests -models physical goods that are fabricated to bring economical value to manufacturing companies. Feature refers to things like holes, steps, bumps or protrusions, among others [START_REF] Sanfilippo | What are features? an ontology-based review of the literature[END_REF]. This class is disjoint from both MfgDevice and Product.

MfgDevice is specialized in various classes, the most important one being MfgMachine for manufacturing machines. AmMachine specializes the latter class for the explicit modeling of additive manufacturing machines, see (Ax1) and (Ax2). The relationship mechanismOf used in (Ax1)-(Ax2) is taken from previous work based on both DOLCE and IDEF [START_REF] Sanfilippo | Modeling manufacturing resources: An ontological approach[END_REF]. It specializes the relationship of participation (cf. Table 2) and grasps the link between manufacturing processes and their 'active' participants. 20 By axioms (Ax1)-(Ax2), machines do not necessarily participate in manufacturing processes. The reason is that an individual machine may be never employed in any manufacturing process.

Ax1 MfgMachine MfgDevice ∀mechanismOf.MfgProcess Ax2 AmMachine MfgMachine ∀mechanismOf.AmProcess Classes like EnergySource (e.g., lasers), BuildPlatform or BuildChamber are included in the taxonomy of PhysicalArtefact and are useful to model (some of) the components making up whole AM machines. 21In order to express the link between an assembled physical artefact and its components, the hasComponent relation is introduced. It specializes hasPart and holds only between physical artefacts. The class MfgMachineComponent is introduced in (Def1).

Def 1 MfgMachineComponent ≡ MfgDevice ∃componentOf.MfgMachine Classes like BuildChamber or BuildPlatform are not subclasses of MfgMachine-Component, since it is not necessarily the case that their instances are components of machines. Consider, e.g., an instance of BuildPlatform, call it bp, which at a time t is not related to any machine and cannot be therefore classified as machine component. However, when bp relates at time t different from t to an individual machine via hasComponent, it is automatically classified as machine component; cf. the (simplified) example in formulas (f1)-(f4), where the latter formula is the result of the reasoning.

f 1 BuildPlatform(bp) f 2 MfgMachine(mch) f 3 hasComponent(mch, bp) f 4 MfgMachineComponent(bp)
Product is extended in AmProduct to explicitly cover products resulting from AM processes (Ax3). In axiom (Ax3), the relationship outputOf, based on DOLCE and IDEF [START_REF] Sanfilippo | Modeling manufacturing resources: An ontological approach[END_REF], specializes participation to grasp the outcomes of manufacturing processes.

Ax3 AmProduct Product ∃outputOf.AmProcess Following AM experts' knowledge [START_REF] Gibson | Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing[END_REF], it is relevant to characterize the distinction between products and AM products, since only the latter are fabricated by the addition of multiple layers of material. An AM product is therefore understood as the (ordered) sum of layers fabricated during an AM process. In the ontology this is (partially) grasped by (Ax4), where hasLayer captures the link between AM products and layers.

Ax4 AmProduct ∃hasLayer.AmLayer Note that, first, layers are physical artefacts resulting from AM processes; they are part of AM products but are not products on their own. Hence, AMProduct and AMLayer are disjoint classes. Second, layers are not components of AM products, hence the relation hasComponent cannot be used to link a layer to a product.

As mentioned at the beginning of the section, the representation of physical artefacts includes a taxonomy of feature classes, which are of key relevance for knowledge representation and data management in AM [START_REF] Shi | Manufacturability analysis for additive manufacturing using a novel feature recognition technique[END_REF][START_REF] Zhang | Am feature and knowledge based process planning for additive manufacturing in multiple parts production context[END_REF]. Recall that there is no exhaustive list of features [START_REF] Sanfilippo | What are features? an ontology-based review of the literature[END_REF], hence the ontology covers only some common feature classes, which can be extended to meet specific application requirements.

The relationship featureOf is used to relate instances of Feature to instances of either Product or AMLayer. Also, (R1) establishes that when a feature is made of material and is related via featureOf to a product, then it is (proper) part of the product. For example, a rib is part of the product to which it is related. Clearly, this does not imply that features are products' components, which is formally excluded in the ontology.

R1 featureOf(x, y) ∧ madeOf(x, z) → madeOf(y, z) ∧ partOf(x, y)

Figure 4 shows a partial view on the taxonomy of PhysicalArtefact. The reader can refer to the OWL file for a detailed view on the ontology. As previously said, modeling types in manufacturing ontologies is useful, especially in design or planning contexts, when experts need to refer to certain entities without reference to their physical counterparts, e.g., because the latter do not exist at the designing (or planning) time. E.g., machine types are the sorts of entities described in machines vendors' catalogues. Indeed, when one reads that a machine is compatible with certain materials, it is clear that reference is not done to specific physical machines (or materials) but to non-physical entities that are only possibly realized in physical machines. In the proposed ontology this non-physical entity corresponds to a machine type defined by multiple properties such as building volume, building capabilities, etc. (cf. [START_REF] Borgo | Ontological analysis and engineering standards: An initial study of IFC[END_REF] for a similar approach). For our purposes, types are particularly useful to model properties related to the compliance of machine types with material types, a restriction that affects the participation of both (physical) machines and materials in individual processes. Rule (R2) guarantees, indeed, that when a material is the input of a process, which uses a certain machine as mechanism, the machine type exemplified by the machine has to be compatible with the material type exemplified by the material (inputOf is a further extension of participation based on DOLCE and IDEF [START_REF] Sanfilippo | Modeling manufacturing resources: An ontological approach[END_REF]).22

R2 Machine(x) ∧ isOftype(x, y) ∧ Process(z) ∧ Material(v) ∧ isOfType(v, w) ∧ mechanismOf(x, z) ∧ inputOf(v, z) → compatibleWithMaterialType(y, w)
For example, consider the AM machine type called FormUpType, which -according to its vendor's catalogue -is compatible only with the metal types specified by (f5).

f 5 FormUpType MachineType ∀compatibleWithMaterialType.(AluminiumAlloyAlSi10Mg TitaniumAlloyTi6A4V MaragingSteel18Ni300Type NickelAlloy625Type NickelAlloy718Type StainlessSteel316LType)
Assume that an individual physical machine of type FormUp is used as mechanism in a process having input a material of polymer type. Stating this fact in the ontology raises an inconsistency because of (R2) and (f5). The next section shows how these sorts of constraints can be useful to validate additive manufacturing data against experts' knowledge.

AM processes. The general class for the representation of processes (Process) is extended in MfgProcess and AmProcess to cover manufacturing and AM processes, respectively; a similar distinction is done at the type level. Coherently with the overall approach to represent types, the class AmProcessType is specialized according to the ASTM classification (see [START_REF] Gibson | Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing[END_REF]) into seven main subclasses, namely, BinderJettingPro-cessType, DirectedEnergyDepositionProcessType, MaterialExtrusionProcess-Type, MaterialJettingProcessType, SheetLaminationProcessType, VATPho-topolymerisationProcessType, and PowderBedFusionProcessType. For the sake of the example, the latter class is specialized into DirectMetalLaserSinteringType, ElectronBeamMeltingType, SelectiveHeatSinteringType, SelectiveLaserMelt-ingType, and SelectiveLaserSinteringType. 23Finally, the ontology covers various parameters, mostly modeled as OWL data properties, to represent, e.g., the maximum power value of lasers, the velocity of scanning processes, hatch distances, the building volume capabilities of machines, and so on. The reader can refer to the OWL file for an insightful overview of these relations.

Figure 5 shows some of the classes introduced across Section 4.2.

Figure 5: The AM ontology branch (partial view)

A prototypical ontology-based Web application for additive manufacturing

This section shows the use of the ontology in a Web application, which is developed in tandem with industrial partners involved in a research project as prototype module within a larger information system. The application is currently used in experimental settings to populate the ontology and guarantee that it matches with both experts' knowledge and stakeholders' requirements.

The main purposes of the application are (i) to support the specification of what the domain experts involved in the project call templates; (ii) to (re-)use the templates for the definition of cases, (iii) to validate both templates and cases, and (iv) to facilitate their reuse in new AM projects. Figure 6 provides a schematic view of the application, which is formed by a user interface mainly used to take users' inputs, and a server-side module that, among other things, allows to manipulate data inputs, validate them against the formal constraints of the knowledge base, store and retrieve them for reuse.

A template is a collection of AM data; for instance, a product template is used to specify the nominal characteristics of a desired product like its dimensions, material type or features; or, a machine template is used to introduce in the knowledge base a machine type along with its characterizing properties, e.g., building volume capabilities, compatibility with material types, energy source's power, the capability to produce features of certain dimensions, etc. Along with product and machine templates, the application covers process, material, and feature templates. A case is a collection of templates which are needed for the additive manufacturing of a product. It should be clear that the core motivation to distinguish templates from cases is that the former can be reused and customized for multiple cases.

From the perspective of the ontology, product, process, feature templates, and cases are modeled through two main classes, Template and Case, respectively, both of which specialize the more general Description class (see Figure 3). The relation hasTemplate specializes hasPart and links cases to templates. Material and machine templates are modeled through the classes MaterialType and MachineType, the reason being that these templates are used to insert new types in the ontology, e.g., a new machine type and the material types that it is able to process. As we will see in the example below, the relationships specifiesMaterialType and specifiesMachineType can be used to relate a template (or case) to material and machine types, respectively. 24The content of each template in the Web application is structured according to manufacturing knowledge provided by the academic literature, by machines and materials vendors' catalogs, and by the domain experts involved in the project. Since the ontology formally specifies the overall body of knowledge, all data field entries in the application are modeled through the elements and constraints of the ontology. For instance, a process template allows to define machine and fabrication parameters such as build orientation, layer thickness, hatch distance, scanning speed, laser mode, laser power, laser beam spot diameter, and laser pulse duration, among others, where each parameter is mapped to an element of the ontology. E.g., assuming that scanning speed is measured in millimetres per second (mm/s), the ontology covers the data property relations specifiesMinScanSpeedInMillimetres-PerSecond and specifiesMaxScanSpeedInMillimetresPerSecond for the minima and maxima scanning speed values, respectively. 25For the sake of the example, assume we need to specify a case for manufacturing solid coupons made of nickel alloy 625 with dimensions of 16x16x15 mm by using a machine of type FormUp. 26 According to our application pipeline, we first define the required templates, which are then reused in a single case. Figure 7 shows the machine and product templates; as it can be seen from the figure, users can specify various data fields, but they also have the possibility of customizing templates by adding new fields. Templates may be fully or only partially filled; e.g., by looking at Figure 7, the fields about features are not filled, because they are not necessary for the scenario at hand. Once a template is saved, the data is pushed into the knowledge base to populate the ontology; Figure 8 shows the product template inserted into the knowledge base. As said, each data field in the Web application corresponds to a modeling element of the ontology; e.g., from Figure 8 it can be seen the use of the relations specifiesMinHeightInMillimetre and specifiesMaxHeightInMillimetre to instantiate the product's height values provided through the data fields Product length (in mm) in the application.

Assuming that we have already inserted a process template in the knowledge base (see Figure 9), a case can be specified to organize together the templates, refine them -when needed -and validate their dependencies. As an example, consider a modeling situation in which the user, when specifying the case, changes the material type previously selected in the product template from nickel alloy 625 to stainless steel CX. Once the user tries to submit the case to the knowledge base, the application triggers an error message, since FormUp machines cannot be used with this material type (recall formula (f5)); therefore the user has to revise the inserted data in order to proceed. Similar constraints are implemented, e.g., to guarantee that the range of selected parameters such as laser power or scanning speed are configurable on machines of the selected type, or that cases and templates have to consistently model the inter-relations between machine and material types. For instance, (R3) establishes that a machine type cannot be configured with a laser power greater than a predefined maximum value. Should the rule be triggered, the knowledge base becomes inconsistent and the user needs to revise the specified data. 27In this way we guarantee that the data specified through the Web application is consistent with the knowledge formalized in the ontology (see the Appendix section for examples of reasonings for constraints checks). Also, the formal constraints in the ontology can be used in tandem with scenario-based rules to support experts' decisions making. For the sake of Figure 7: Machine and product templates example, by rule (R4) a reasoner is able to infer the laser power parameter to be configured on the machine of the preselected FormUp type. It should be clear that, on the one hand, these sorts of rules for decision making can be useful to (semi-) automatise decision making processes especially when multiple dependencies between data have to be checked. On the other hand, these rules are context-dependent and efforts are required to formalize experts' knowledge in computational terms. Once the knowledge base is populated with users inputs, queries can be formulated to retrieve data for reuse. In the prototype of the application, SPARQL [START_REF] Prudhommeaux | Sparql query language for RDF[END_REF] queries can be expressed by filling out forms similar to the ones showed in Figure 7. For instance, let us assume that the user wishes to retrieve data concerning the compatibility between machine and material types. By using the Web application, the user selects the desired values; the selection is converted in a SPARQL query, like the one showed in (Q1), and data -if found -is showed (see Table 3). The reader can refer to the Appendix for further query examples.

Q1 For each machine type in the KB, retrieve the compatible material types.

SELECT

Conclusion

The paper presents a computational ontology to represent and reason over AM knowledge and data. The development of the ontology has been motivated with respect to the need for a reference knowledge model to harmonize data management, support data sharing, and validate data against experts' knowledge. Also, the ontology constitutes the semantic backbone of the information system architecture developed within a research project. As part of this architecture, we presented a prototype Web application which is used to create, validate, and reuse AM data and knowledge. By relying on the ontology, the data managed through the application is indeed semantically characterized to facilitate their sharing across multiple stakeholders. Additionally, by reasoning over the ontology, the data can be checked against possible pitfall.

With respect to the state of art about ontologies for AM, our approach relies on both experts' knowledge and well-known modeling principles. The purpose is, first, to rely on rigorous modeling criteria; second, to facilitate the reuse of the ontology and its extension for specific application requirements. As discussed throughout Section 3, existing AM ontologies are not directly reused mainly because the modeling patterns upon which they rely are either mistaken or not well suited for manufacturing knowledge representation. Our research work is therefore a contribution towards a principled knowledge base for AM.

Since the additive manufacturing domain is constantly evolving under the pressure of both academia and industry, it is not surprising that further work on our proposal is required. In the next future we expect to use the ontology to develop algorithms and softwares to support data sharing and data tracing along the entire AM value chain, as well as to aid experts' decision making. Concerning this latter application scenario, at the current state of the Web application, data are either manually inserted or reused by retrieving them from existing templates or cases. In the future version of the application, templates and cases will be (semi-) automatically filled by algorithmically matching users' requirements and Key Performance Indicators (KPI) to both experts' knowledge and the past experience stored in the knowledge base. As shown in Section 5, formal rules can be already used to support decision making processes but in order to foster the use of the application in real-world settings, the presented approach needs to be complemented with techniques such as multi-attributes case-based reasoning or concepts similarity measures, among others. To achieve these results, the ontology will be continuously maintained by populating it with research and stakeholders data but also by updating its structure whenever this will be needed. Finally, in order to share our research results with the ontology engineering community active in the manufacturing domain, the ontology will be presented to the Industrial Foundry Ontology (IOF) initiative in such a way to facilitate its reuse and adoption.

A1. Examples of queries

The queries are expressed in SPARQL [START_REF] Prudhommeaux | Sparql query language for RDF[END_REF]. For the sake of the examples, PREFIX statements to specify the namespaces of the ontology and other relevant documents, e.g., the RDF standard specification, are omitted.

A2. Examples of reasoning for constraints checks

Assume that formula (f18) substitutes (f9), so that the product template ptl 1 specifies now information about a product made of material type stainless steel CX.

f 18 specifiesMaterialType(ptl 1, stainlessSteelCXT ype)

By reasoning over the knowledge base, (f18) generates an inconsistency in tandem with (f6)-(f8) and (f10), because they violate (f5).

By formula (f19), the process template pstl 1 sets the maximum laser power value to 600 W. By (f10), pstl 1 specifies the usage of a machine of type FormUp, which by (f20) has maximum laser power of 500 W (recall (Q4)). The formulas trigger therefore rule (R3) and the user needs to revise the inserted data.

Figure 3 :

 3 Figure 3: Upper-level taxonomy

Figure 4 :

 4 Figure 4: Taxonomy of PhysicalArtefact (partial view)

Figure 6 :

 6 Figure 6: Schema of the Web application

Figure 8 :Figure 9 :

 89 Figure 8: Example of product template added to the knowledge base

f 6

 6 Case(cs 1) f 7 hasProductTemplate(cs 1, ptl 1) f 8 hasProcessTemplate(cs 1, pstl 1) f 9 specifiesMaterialType(ptl 1, nickelAlloy625T ype) f 10 specifiesMachineType(pstl 1, f ormU pT ype) f 11 specifiesMaxScanSpeedInMillimetrePerSecond(pstl 1, 750) f 12 Case(cs 2) f 13 hasProductTemplate(cs 2, ptl 2) f 14 hasProcessTemplate(cs 2, pstl 2) f 15 specifiesMaterialType(ptl 2, titaniumAlloyT i6A4V T ype) f 16 specifiesMachineType(pstl 2, eosintm290T ype) f 17 specifiesMaxScanSpeedInMillimetrePerSecond(pstl 2, 875)

f

  [START_REF] Group | OWL 2 web ontology language document overview[END_REF] specifiesMaxLaserPowerInWatt(pstl 1, 600) f 20 hasMaxLaserPowerInWatt(f ormU pT ype, 500)

Table 1 :

 1 . On the other Comparison of ontologies for additive manufacturing

	Paper	Product Process Resource Parameters Reused	Modeling
						ontologies	language
	[1]	Yes	Yes	Yes	Yes	BFO, IAO,	OWL
			(ASTM)			CCO
	[13]	Yes	Yes (no	Yes	Yes	None	OWL,
			ASTM)				SWRL
	[14]	Yes	Yes	Yes	Yes	MSDL [43]	OWL,
			(ASTM)				SWRL
	[25]	Yes	Yes (no	Yes	Yes	BFO,	OWL,
			ASTM)			RO, IAO,	SWRL
						FBO [17],
						[14]
	[32]	Yes	Yes (no	Yes	Yes	None	IDEF0,
			ASTM)				UML
	[40]	No	No	Yes	Yes	None	Category
							theory
	[42, 55]	Yes	Yes (no	Yes	Yes	None	OWL
			ASTM)
	Our work Yes	Yes	Yes	Yes	DOLCE	OWL,
			(ASTM)				SWRL

Table 2 :

 2 Upper-level relations

			Domain		Range		Example
	after	(inverse,	Process		Process		Process p 1 occurs
	before)						after process p 2
	encodes		PhysicalObject	Description		Description sup-
	(encodedIn)					port d encodes
							CAD model c
	hasNon Quanti-	Object,	Mate-	NonQuantitative-	Material m has
	tativeValue	rial, Process	ValueSpace		non-quantitative
							value solid
	madeOf		PhysicalObject	Material		Physical object
							pob is made of
							material m
	participatesIn	Object,	Mate-	Process		Object o partic-
	(hasParticipant)	rial, Type				ipates in process
							p 1
	partOf		Object,	Ma-	Object,	Ma-	Object o 1 has
	(hasPart)		terial, Process,	terial, Process,	parts objects o 2
			Type,Qualitative-	Type, NonQuanti-	and o 2
			ValueSpace		tativeValueSpace
	isOftype		Object,	Mate-	Type		Material m is of
			rial, Process			type NickelAl-
							loy718

Table 3 :

 3 Result of (Q1)

	?machineType ?materialType WHERE
	{?machineType a MachineType;
	compatibleWithMaterialType ?materialType.}
	machineType	materialType
	proXDMP200Type	stainlessSteel174PHType
		aluminiumAlloyAlSi12Type
	eosIntM290Type	nickelAlloy718Type
		nickelAlloy625Type
		aluminiumAlloyAlSi10MgType
	formUpType	aluminiumAlloyAlSi10MgType
		nickelAlloy718Type
		nickelAlloy625Type
		titaniumAlloyTi6A4VType
		stainlessSteel316LType
		maragingSteel18Ni300Type

Table 4 :

 4 Q2 Retrieve process templates that specify a maximum speed value comprised within 800 and 900 mm/s. Result: only pstl 2.SELECT ?processTemplate ?speedValue WHERE {?processTemplate a ProcessTemplate; specifiesMaxScanSpeedInMillimetrePerSecond ?speedValue. FILTER (?speedValue >= 800 && ?speedValue <= 900) } Q3 Retrieve cases which specify information about products that are made of either nickel alloy type 625 or titanium alloy type Ti6AV and that have been produced by machines of type FormUp. Result: only case cs 1.Q4 For each machine type in the KB, retrieve the maximum laser power value. Result of (Q4)

	SELECT ?case WHERE
	{ ?case a Case;
	hasProductTemplate ?productTemplate;
	hasProcessTemplate ?processTemplate.
	{?productTemplate specifiesMaterialType nickelAlloy625Type.}
		UNION
	{?productTemplate specifiesMaterialType titaniumAlloyTi6A4VType.}
	?processTemplate specifiesMachineType formUpType. }
	SELECT ?machineType ?powerValue WHERE
	{?machineType a MachineType;
	hasMaxLaserPowerInWatt ?power.}
	ORDER BY (?powerValue)
	machineType	maxPowerValue
	proXDMP200Type	300
	eosIntM290Type	400
	formUpType	500

Under the pressure of AM, the discipline of AM Informatics is born as "the science of managing AM data across its lifecycle with full maintenance of the complex relationship between the part geometry, material, and individual processes used to create the final part"[START_REF] Mies | Overview of additive manufacturing informatics:"a digital thread[END_REF].

The reader can refer to[16, 28,

[START_REF] Mies | Overview of additive manufacturing informatics:"a digital thread[END_REF] for an overview of industrial experiences in AM.

The reader can refer to the Industrial Ontology Foundry (IOF) at: https://www. industrialontologies.org, last accessed March 2019.

http://www.obofoundry.org/ontology/ro.html, last accessed March 2019.

http://www.obofoundry.org/ontology/iao.html, last accessed March 2019.

https://www.cubrc.org/index.php/data-science-and-information-fusion/ontology, last accessed March 2019.

[START_REF] Borgo | Knowledge-based adaptive agents for manufacturing domains[END_REF] Similar concerns about the IAO have been raised at least in[START_REF] Sanfilippo | A foundational view on nominal and actual qualities in engineering[END_REF][START_REF] Schulz | An ontological analysis of reference in health record statements[END_REF]

].[START_REF] Borgo | Foundations for a core ontology of manufacturing[END_REF] Since most of the OWL ontologies presented in this section are not available for review or reuse, the table is mainly based on the materials presented in the papers.

The ontology is available at [link to be added after review is finalized].

https://protege.stanford.edu/, last accessed February 2019.

We prefix axioms with Ax, definitions with Def , formulas for examples with f , rules with R, and queries with Q. Also, we omit question marks from SWRL variables and PREFIX statements from SPARQL queries.

We use 'object' to replace DOLCE 'endurant' in order to use a terminology closer to manufacturing.

In order to distinguish a description from its (possibly multiple) supports (e.g., digital files), Phys-

We use the acronyms M f g and Am to label some classes, the former standing for manufacturing, the latter for additive manufacturing. Also, the modeling of roles is based on[START_REF] Guarino | An overview of OntoClean[END_REF][START_REF] Guizzardi | Ontological foundations for structural conceptual models[END_REF].

The representation of manufacturing processes is discussed at the end of the section.

Our purpose is not to dig into the representation of AM machines. The reason for having classes like EnergySource or BuildPlatform is to show how they can be accommodated within the ontology.

Differently from mechanismOf, inputOf expresses a form of 'passive' participation in a process, e.g., a material 'undergoing' (transformed by) a manufacturing process.

We already introduced various relationships to link objects or materials to processes.

Relations starting with the term specifies have Description as domain.

All data properties for parameters with numerical values cover both minima and maxima values.

The example is based on the data provided in[START_REF] Criales | Laser powder bed fusion of nickel alloy 625: experimental investigations of effects of process parameters on melt pool size and shape with spatter analysis[END_REF], which have been adapted to our purposes.

In order to avoid making the knowledge base inconsistent, an alternative approach can be adopted to infer that the selected machine type cannot be used for that specific purpose.

R3 Description(x) ∧ specifiesMachineType(x, y) ∧ hasMaxLaserPowerInWatt(y, v) ∧ specifiesMaxLaserPowerInWatt(x, z) ∧ greaterThan(z, v) → owl : Nothing(x)

Appendix

The knowledge base used in the examples contains various individuals, among which two cases, cs 1 and cs 2, such that cs 1 specifies information about a product made of nickel 625 by a FormUp machine, see (f6)-(f10), whereas cs 2 specifies information about a product made of titanium alloy Ti6A4V by a EOSINT M290 machine, see (f12)-(f16). Also, by (f11), the process template ptl 1 specifies a maximum scan speed value of 750 mm/s, whereas the scan speed value established by ptl 2 is of 875 mm/s by (f17).


Alexia Mattei
email: alexia.mattei@laposte.net

Thierry Legou

Adèle Cardeau

Julia Le Goff

Aude Lagier

Antoine Giovanni

Acoustic correlates of vocal effort: external factors and personality traits

Keywords: Dysphonia, Voice disorders, Acoustic analysis, Vocal effort, Personality

Objectives:

To develop an experimental protocol to study the vocal effort generated by introducing barriers to communication, and its relationship with certain personality traits.

Material and methods:

The experimental protocol consisted of an interactive game in which the subject gave an investigator instructions to adopt various body positions (semi-directed communication situation). The Control situation included no constraints on communication. Then a Distance Constraint (increased distance between subject and investigator) and a Time Constraint (generation of performance stress by putting the subject in competition with others) were introduced. The vocal parameters studied comprised vocal intensity and fundamental frequency in the middle of the vowel of 3 target phonemes. Subjects also took the NEO FFI-R personality test. Results: The study included 41 women aged between 18 and 52 years. Vocal intensity and fundamental frequency increased significantly with the introduction of the constraints (p <0.05), intensity passing from 75.5 dB to 81.8 dB and frequency from 249.4 Hz to 335.8 Hz. No correlations were found between these changes and results for the various personality traits. Conclusions: This ecological protocol enables the impact of both physical and emotional obstacles to communication to be studied. No correlations between vocal effort and personality traits emerged. A larger-scale study would be necessary to analyze the continuum between vocal effort and vocal forcing, to improve speech therapy for dysfunctional dysphonia.

Introduction

In healthy subjects, vocal effort is a natural response to situations of difficult communication. When chronic or involuntary or inappropriate, it may lead to vocal forcing and hence to certain forms of dysphonia [START_REF] Giovanni | Le forçage vocal et ses conséquences[END_REF]. Various factors are involved in onset and maintenance of vocal forcing: external factors concerning the environment and obstacles to communication [START_REF] Cheyne | Talker to listener distance effects on speech production and perception[END_REF], and internal factors concerning the individual and his or her perception of such obstacles and their importance. These perceptual factors and the resulting reactions are related to personality [START_REF] Dietrich | Vocal function in introverts and extraverts during a psychological stress reactivity protocol[END_REF][START_REF] Roy | Personality and Voice Disorder : A Superfactor Trait Analysis[END_REF][START_REF] Roy | Personality traits and psychological factors in voice pathology: a foundation for future research[END_REF].

Vocal forcing thus results from interactions between several factors that are inter-related in a way that makes them difficult to distinguish. Understanding these interactions and the resulting vocal behavior is essential for effective speech therapy in dysphonia involving vocal forcing. There is, however, no routine test assessing the respective impacts of the various factors underlying vocal effort.

The objective of the present study was firstly to draw up an experimental protocol to study disturbance of phonation by obstacles to communication, in an optimally ecological situation of vocal effort.

The second objective was to analyze the relation between such vocal effort and certain personality traits.

Material and methods

Forty-one healthy female volunteers, aged 18 to 70 years, were included. Exclusion criteria comprised known history of phonopathology, dysarthria, neurologic disorder or hearing loss. Subjects provided informed consent after clear, honest and adapted information on study objectives and methodology. Subjects were coded by number so that results would be anonymous.

To study phonation disturbance by obstacles to communication, an experimental protocol of vocal effort induction by modification of external factors was designed. The protocol involved 3 participants: the experimental subject, and a first and a second investigator. An interactive game was set up between subject and first investigator, in a semi-directed communication situation. The subject instructed the investigator to adopt various body positions, based on a set of 16 cards (cf. Figure 1) showing a person from behind in various positions with 3 accessories (ball, hat, glove), guiding the investigator (whose back was turned) in assuming the correct position; the first investigator followed the instructions until the second investigator approved the body position, at which point the subject went on to the next position. The experiment was conducted in a room with an opening onto the outside. The subject underwent 4 conditions, in the following order:

-Control situation (C), without communication constraint, with a 2-meter distance between subject and investigator; -Distance Constraint situation (DC), with distance between subject and investigator increased to 15 meters; -Temporal Constraint situation (TC), with stress induced by social desirability by putting the subject in competition with others to achieve as many investigator positions as possible in 90 seconds; -Distance + Temporal Constraint situation (DC-TC), associating the two.

The session began with non-analyzed trial run to check the subject's understanding of the protocol and the functioning of the recording equipment. Only the C and DC-TC conditions were then used for statistical analysis and comparison with personality traits. Acoustic vocal parameters were extracted from target phonemes (French /a/ as in "ballon" (ball) and /o/ and /e/ as in "bonnet" (hat)): mid-vowel vocal intensity in dB and mid-vowel fundamental frequency in Hz. For recording, the subject was equipped with a micro-helmet, connected to an EVA2™ assisted vocal assessment device, with analysis by Phonedit® software (SQLab, LPL, Aix-en-Provence, France).

Individual differences in mean vocal intensity and fundamental frequency between the C and DC-TC situations were calculated.

The relation between the induced vocal effort and certain personality traits (internal factors) was assessed using the NEO FFI-R personality test (NEO Five Factor Inventory, short form of the NEO PI-R or Revised NEO Personality Inventory [START_REF] Costa | Domains and facets: hierarchical personality assessment using the revised neo personality inventory[END_REF][START_REF] Costa | A contemplated revision of the NEO Five Factor Inventory[END_REF]), comprising 60 items in the form of statements on which the respondent gives her opinion as Strongly disagree (SD), Disagree (D), Neutral (N), Agree (A) or Strongly agree (SA). The 5 traits thus assessed are: neuroticism, extraversion, openness to experience, agreeableness and conscientiousness [START_REF] Digman | Personality structure: emergence of the five-factor model[END_REF], each ranked as low, average or high.

Analysis of variance was used to assess whether Intensity and Frequency were influenced by DC and TC. The Mann-Whitney test was used to compare mean values. The significance threshold was set at p<0.05.

Results

Subjects comprised 41 healthy female volunteers aged 18-52 years.

1) Vocal intensity and fundamental frequency according to communication situation

(Table 1)

Vowel intensity and fundamental frequency increased significantly when distance and time constraints were introduced (p<0.05): intensity from 75.5 dB to 81.8 dB, and frequency from 249.4 Hz to 335.8 Hz.

2) Acoustic parameters and personality traits

Intensity was greater in DC-TC than C for all subjects except 1. Mean increases were grouped by personality trait levels for analysis (Table 2). One-factor analysis of variance found no significant difference in vowel intensity according to level of any personality trait, although there was a trend toward an association between extraversion and increased intensity under constraint (p=0.07).

Fundamental frequency was higher in DC-TC than C for all subjects. Mean increases were grouped by personality trait levels for analysis (Table 3). One-factor analysis of variance found no significant difference in vowel intensity according to level of any personality trait.

Discussion

The present study sought to shed light on the vocal forcing that is implicated in many dysfunctional dysphonias. It provides data on the vocal effect of introducing communication constraints. Subjects make a vocal effort in order to be heard or understood more effectively, both speaking louder and modulating other parameters such as timbre and respiratory and postural behavior. Intensity increases and, with it, frequency [START_REF] Huang | Measures of vocal function during changes in vocal effort level[END_REF]. Increases range between 2 and 8 dB and between 3 and 12 Hz per distance doubling [START_REF] Cheyne | Talker to listener distance effects on speech production and perception[END_REF][START_REF] Pelegrin-Garcia | Vocal effort with changing talker-tolistener distance in differents acoustic environments[END_REF][START_REF] Traunmüller | Acoustic effects of variation in vocal effort by men, women, and children[END_REF]. Findings vary, as other environmental factors interfere, notably the acoustic conditions of the room (anechoic chamber, long narrow corridor, meeting room, room with resonance, etc.), affecting auditory feedback of the speaker's voice, with significant alteration of sound level [START_REF] Pelegrin-Garcia | Vocal effort with changing talker-tolistener distance in differents acoustic environments[END_REF][START_REF] Cipriano | Combined effect of noise and room acoustics on vocal effort in simulated classrooms[END_REF][START_REF] Bottalico | Speech Adjustments for Room Acoustics and Their Effects on Vocal Effort[END_REF].

Although these were not analyzed, we also noticed changes in prosody and syntax. Under constraint, some subjects stopped trying to speak in sentences, over-articulated (i.e., shortening consonants in favor of vowels), hesitated or on the contrary became authoritarian or aggressive. Posture also changed: some subjects froze, while others mimed the movements they were asking the investigator to make. It would also have been interesting to analyze posture in the anteroposterior plane, known to change under vocal effort [START_REF] Giovanni | Vocal forcing and posture: experimental studies on normal subject[END_REF][START_REF] Lagier | Coordination between posture and phonation in vocal effort behavior[END_REF].

The present study provides a protocol to induce ecological vocal effort by inducing stress while maintaining the spontaneity of communication without sacrificing rigor and reproducibility. The semi-directed situation led the subject to speak in her own words, without restriction. The instructions were simple and relatively non-directive, leaving room for natural communication. The presence of the first investigator provided visual feedback of success or failure of transmission of the message, allowing the subject to adjust phonation to maintain efficacy despite the constraints introduced. However, being fitted with recording equipment connected up to a computer may have introduced bias in the spontaneity of communication, which needs to be taken into account in interpreting the results. That the order of the conditions was not randomized may also have introduced bias, with the subject progressively getting used to the exercise Although 2 of the 3 study vocoids came after a voiced bilabial occlusive consonant, they were followed by different consonants depending on the word, and thus had differing phonetic contexts. It would be useful to supplement the present analyses by comparing the respective increases in intensity and frequency according to the vowel being produced. Lastly, physical or mental fatigue was not taken into account, although it could affect behavior. A test-retest design (e.g., evening versus morning) could check the consistency of the subjects' behavior.

The present pilot study was performed with female subjects, as dysfunctional dysphonia mainly affects women [START_REF] Hantzakos | Exudative lesions of Reinke's space: a terminology proposal[END_REF]. The words "bonnet" and "ballon" were chosen on phonologic criteria: continuous voicing, little occlusion, and no silent final [e].

No significant correlation with personality emerged, probably due to lack of power, with many factors and few subjects. There was nevertheless a trend toward an association between extraversion and increased vocal intensity under constraint: extraverted subjects tended to increase intensity more than did introverts in situations where communication became difficult. This was shown in other studies comparing healthy versus dysphonic subjects with or without lesions [START_REF] Roy | Personality and Voice Disorder : A Superfactor Trait Analysis[END_REF][START_REF] Uali Abeida | Study of the influence of psychological factors in the etiology of vocal nodules in women[END_REF][START_REF] Roy | Personality and voice disorders: a multitrait-multidisorder analysis[END_REF][START_REF] Mattei | Personality traits inventory in patients with vocal nodules[END_REF]. If vocal production is expected to get a "result", then considerations of social desirability will inspire extraverted subjects to do whatever it takes to "meet objectives". Personality also affects how the individual reacts in an anxiogenic situation, either absorbing the stress or exacerbating it. Acoustic parameters are modulated by emotional state [START_REF] Mchugh-Munier | Coping strategies, personality, and voice quality in patients with vocal fold nodules and polyps[END_REF]. Personality is likely to affect vocal effort especially in the TC condition, in which the obstacle is largely "mental": emotion probably plays a major role in what the subject makes of this communication stress. A comparative study between the C and TC situations would be interesting, to determine the role of emotion and its relation to personality.

The NEO FFI-R is a short form of the NEO PI-R test [START_REF] Costa | Domains and facets: hierarchical personality assessment using the revised neo personality inventory[END_REF][START_REF] Costa | A contemplated revision of the NEO Five Factor Inventory[END_REF]. Although standardized and validated, it is less precise than the full form, and provides only a general profile, neglecting the facets of the traits and taking little account of interindividual differences in personality. Moreover, it is a self-administered questionnaire, so that social desirability factors come into play. The timing of administration is also debatable: right after the experimental session, possibly in a situation of stress, with risk of context bias.

Conclusion

The vocal effort induced by two types of constraint (talker-to-listener distance and stress) is close to the natural communication situation. The ecologic nature of the protocol was of good quality, as the subject was free to express herself (semi-directed situation), with both a physical (distance) and an emotional (time) obstacle. Many other factors involved in vocal effort require more precise analysis, such as target vowel timbre, and postural and respiratory behavior. No correlation between vocal effort and personality traits could be shown, although this would be worth studying in larger-scale investigations. Studying the continuum between vocal effort and vocal forcing would shed light on speech therapy modalities in dysfunctional dysphonia.

Disclosure of Interest:

The authors declare that they have no conflicts of interest concerning this article

Figure 1 :

 1 Figure 1: Example of card

Table 1 :

 1 Mean intensity and fundamental frequency according to communication situation

			Mean	Minimum	Maximum	Standard
						deviation
	Intensity (dB)	C	75.5*	65.8	88.7	5.5
		DC-	81.8*	73.3	91.1	5.2
		TC
	Fundamental frequency	C	249.4*	211.8	304.1	20.3
	(Hz)	DC-	335.8*	253.4	424.2	37.6
		TC
	C = control situation; DC-TC= distance constraint and time constraint
	*p<0.05 between C and DC-TC

Table 2 :

 2 Correlation between variation vowel intensity and personality traits

			Low	Averag e	High	p
	Neuroticism	n	13	22	6
		Mean (dB)	5.6	6.7	6.2	0.569
		Standard deviation (dB)	3.5	2.8	2.9
	Extraversion	n	6	24	11
		Mean (dB)	4.3	6.1	7.7	0.0743
		Standard deviation (dB)	1.8	3.4	2.0
	Openness	n	10	15	16
		Mean (dB)	6.4	6.8	5.7	0.589
		Standard deviation (dB)	4.5	3.1	1.6
	Agreeability	n	10	14	17
		Mean (dB)	8.0	5.6	5.8	0.111
		Standard deviation (dB)	2.5	2.9	3.2
	Conscientious ness	n	9	7	25
		Mean (dB)	6.2	6.5	6.2	0.973
		Standard deviation (dB)	2.9	2.4	3.4

Table 3 :

 3 Correlation between variation vowel frequency and personality traits

			Low	Average	High	p
	Neuroticism	n	13	22	6
		Mean (Hz)	80.6	90.1	85.4	0.702
		Standard
		deviation (Hz)	46.7	23.5	20.9
	Extraversion	n	6	24	11
		Mean (Hz)	83.4	83.3	94.7	0.606
		Standard
		deviation (Hz)	16.4	35.9	29.5
	Openness	n	10	15	16
		Mean (Hz)	100.4	88.5	75.6	0.149
		Standard
		deviation (Hz)	31.9	34.5	27.0
	Agreeability	n	10	14	17
		Mean (Hz)	101.0	79.1	83.8	0.229
		Standard
		deviation (Hz)	34.8	28.4	31.9
	Conscientiousn ess	n	9	7	25
		Mean (Hz)	97.6	92.0	80.3	0.335
		Standard
		deviation (Hz)	26.7	35.0	32.5