Study on Ontological Knowledge Integration of Micromachining for Collaborative Process

doi:10.21203/rs.3.rs-2074129/v1

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Study on Ontological Knowledge Integration of Micromachining for Collaborative Process

https://doi.org/10.21203/rs.3.rs-2074129/v1

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published 13 Jul, 2023

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To achieve the goal of manufacturing automation, the knowledge integration in collaborative process is very crucial, especially, the heterogeneous information distributed among various cooperative suppliers. Therefore, an ontology-based methodology of knowledge integration is proposed to solve the information heterogeneity in this study. By representing a collaborative business activities for cooperative suppliers, a framework of knowledge integration with ontological schema is generated. Aiming for the schema, an approach of ontology integration on the basis of ontological similarity is promoted. There are two portions included in this approach, mapping and combination, which are implemented through the analysis in similarity of universal and regional two ontologies. To integrate the two ontologies, the relations and restrictions among fields are definitely depicted by semantic web rule language. Eventually, the proposed framework is demonstrated by CNC-WEDM process, and the outcomes satisfy the needs of the knowledge integration to assure that validity of the proposed approach is confirmed.

Knowledge integration

Non-traditional manufacturing

Ontology

CNC-WEDM Collaborative process

In the era of global economy, Manufacturing firms are encountering great challenges, e.g. pressure of low-cost control, shortening of lead time, appeal of higher quality, etc. [1–2]. To deal with the increasing complexity and a large amount of manufacturing information, engineers performing in an unusual settings will surely generate the related domain terms. Therefore, there are two issues, syntax and semantic, discussed in sharing and exchange of information. The former means various vocabularies may be applied to indicate the same element. The latter shows the same vocabulary is being used to various notions [3]. Hence, the integration of heterogeneous information among various collaborative firms is considered as a potential enterprise strategy in the new generation of manufacturing [4–5].

Meanwhile, non-traditional micromachining technology (abbr. NTMT) is depicted as a process that subtract spare material through various energy sources, e.g. electrical, thermal, chemical energy or integrations of them but do not apply a sharp cutters as it requirements to be applied for typical fabrication process [6]. NTMT application follows design of component accuracy, refers component models, and is constrained by component and process. Development of collaborative NTMT (col-NTMT) permits manufacturing decisions to be made with a whole comprehension of the several portions: component, process, and fabrication. Due to the variety and intricacy of the collaborative procedure, cross-domain information at various stages are included, it is a vital topic to make information effectively applied, such as acquire, share, reuse, and extract. However, there are several challenges depicted regarding the knowledge management of col-NTMT. Firstly, the relations and restrictions have to be clearly expressed as a principle for inferring to aid knowledge integration and decision-making. Secondly, it is necessary to figure out applicable messages from preceding design traces to make appropriate decisions for implementing each task in the collaboration [7]. Finally, various vocabularies in heterogeneous forms may be utilized to indicate the same element, which could lead to message lost or misconception. As the above mentioned context, it is essential that a systematic method for knowledge integration to aid col-NTMT.

There are many researches presented in the approaches of knowledge integration for collaborative manufacturing. Hartman et al. [8] depicted a model-based enterprise (MBE) and the nature of collaborative data for various use cases. Meanwhile, this research also recommends a structure for estimating different collaborative product representations. Ziegler et al. [9] addressed integrated framework for dynamic collaborative manufacturing networks on the basis of semantic data, mobile apps, business process modelling and access control. A fuzzy approaches with CBR is discussed for knowledge representation, feature selection, and learning in CBR. Knowledge is then deduced applying fuzzy approaches that combined various knowledge sources [10]. A formalized heuristic framework to represent the essential knowledge properties unit that permitted users to search higher knowledge sources from knowledge repository, and then merge the sources in distributed environment [11]. Yet, these researches are concentrated mainly on merging knowledge with concrete or non-semantic entity, and rarely focused on the integration of semantic or conceptual knowledge in collaborative fabrication.

To deal with this issue, some methods with ontology mode are proposed [12–15]. Ontology has revealed allowing in eliminating interpretation situations through calculatingly obtaining the semantics of conceptions, assuring their consistence and hence offering a certifiable and shared contents over multiple fields [14]. Meanwhile, an ontology of regional field can only depict knowledge of a regional archive, not combined knowledge from various heterogeneous resource repositories. Hence, according to the investigation of col-NTMT features and real needs, to enhance the achievements of knowledge integration and let all members share knowledge in collaborative manufacturing environment, an ontology-based architecture of knowledge integration is presented to develop extensive field ontologies.

This study is organized as follows: The relevant work of the existing methods are depicted to develop field ontology in Section 2. The process for business collaboration and an architecture of knowledge integration with ontology mode are explained within collaborative manufacturing in Section 3. Meanwhile, Section 4 generates an ontology of knowledge and schema that states the framework of NTM knowledge. An approach of ontology integration is explored on the basis of the knowledge with ontological format. Section 5 demonstrates the proposed framework through an example. Eventually, the conclusions and future perspective are revealed in Section 6.

2.1 Issues of ontology development

In the past decade, there are many researches proposed for developing the generalized semantic of the professional knowledge to deal with the issue of information integration among various divisions and companies. Ontology can offer the criteria for information merging, interoperation and sharing among firms by designating notions and their relations. There are several portions involved in ontology development: (1) description – a formalized form is offered for the integration and sharing of information; (2) formation – the notions and relations among information are specified; (3) mapping – a process to articulate similarity among the notions or terms (e.g. users, regional items, or universal terms) belonging to different source ontologies (client tier or knowledge repository). They are depicted as follows:

In the aspect of ontology description, the logic languages, e.g. OWL and DAML + OIL, are applied to depict ontology due to the excellent inferring ability [16–18]. In addition, object-orient methods and graph-based methods are two major approaches for representing ontology [19–20]. The former is used to depict properties and elements of notions. For instance, a novel model of assembly-oriented design with ontology mode is proposed [21]. The model acts as a regular, definite standard of assembly design for converting assembly information into computer-interpretable format, the results are easy to share and reuse. The latter is able to offer a different aspect of ontology for users. Conceptual graphs [22–24] and WordNet [25–26], for example, are most commonly applied. Its characteristics are excellent inferring ability and clarity of representation. Yet, it is not easy to depict properties and elements of notions.

Regarding the aspect of ontology formation, there are several main approaches, category-based approach, cluster-based approach, and heuristic approach, developed in recent decade. Ontology is constructed by category-based approach with an incremental mode. For instance, Ranjini and Saruladha [27] propose two new notions which are depicted through a known notion by increasing two contrary variations. Hence, all notions consist of a binary branch in a top-down structure. Cluster-based approach can extract the notions from data with structural or semi-structural mode [28]. By NLP (natural language processing) tools, the terms can be identified. For example, Ahmed et al. [29] mention a K-means clustering algorithm which is applied to parse messages in heterogeneous architecture to obtain a candidate list of syntactically reasonable terms. Domain notions and their relations are caught through heuristic approach. For example, Rani et al. [30] propose two algorithms, LSI & SVD and Mr.LDA, for generation of semi-automatic topic ontology. They provide assistance to the member by means of recommending issues and examining them in real time. Ge and Li [31] address a novel Chinese domain ontology construction method to catch filed ontology from unstructured messages. There are several portions included in this method: (i) definition of the goal and field of ontology; (ii) formation of ontology; (iii) evaluation of ontology; and (iv) documentation. The field conception is vital for developing ontology, but the capture of field notions and their relations is an obstruction. The primary cause is that domain experts/engineers usually keep various aspects for the same notion. Besides, various notions deriving from heterogeneous repositories generally have the semantic.

For the aspect of ontology mapping, there are several feasible approaches, construct-time approach, implement-time approach, and BN (Bayesian network-oriented) approach, presented. In the portion of construct-time approach, Kozierkiewic and Pietranik [32] outline the evolution of ontology mapping, which computing the similarity between ontologies to mate notions and semantic depiction of exchanged messages. Asim et al. [33] summarize superior-notions, inferior-notions, and synonymous notions of member’s item from domain database of ontology. A solution is presented [34] to connect explicit knowledge and tacit knowledge, to create the same field ontology group for constructing the product knowledge topics, annotations, the member authority profile, and the activity context. Regarding the portion of implement-time approach, it is applied to deduce semantically correlative notions from the mapping between an implement-time representation/regional ontology and universal ontology. For instance, A key term with its superior-notions and inferior-notions is provided [35] for querying, which are compared to field ontology. Meanwhile, this approach is used to help users for making an online decision through personalized webpage grading sequence of e-commerce web connections. It interacts with customers to analyze their favorites and read behavior [36]. Saeidlou et al. [37] intend to improve the indicative effect in depiction of member’s query purpose and to permit members to query manufacturing messages on the basis of self-context. As for the portion of BN approach in the ontology mapping, it is an autonomous mode. Based on BN approach, for instance, Yuan et al. [38] build the information field ontology and create a data connect message ontology model by analyzing its inferior-field properties. Formica et al. [39] address a novel probabilistic way for ontology weighting on the basis of a BN approach, it only deals with a matching procedure to notion name, not including the other property messages in ontology. A methodology of ontology-mapping-oriented search is presented [40]. Based on BN and multi-dimensional similarity, its purpose is to seek out the items that have the same semantics with member’s keywords.

2.2 Integration and share of information with ontology mode

As the mentioned in Section 2.1, ontologies serve as a carrier for integration and interchange of seamless information [41–42]. Therefore, it is not only suitable to represent and apply the relations among information, but it is very efficient in inferring [43]. An assembly relation model with ontology mode is presented to enhance assembly information share in collaborative environment [44]. An ontology model with product information and management semantic entity is generated, its goal is to execute ontology benefits and elements into the proposed model [45]. A method with ontology mode is mentioned for integrating CAD and CAAPP (computer-aided assembly process planning) [46].

Meanwhile, many approaches about ontology-based knowledge integration and share are also presented [47–50]. There are several goals achieved through ontology-based approach, including integration of multi-data resources, mapping of complicated relations among notions, and facilitation of decision-making with logic inferring. Nevertheless, few researches concentrate on supporting a complete services for integration and share of messages in fields of non-traditional manufacturing technology. This drawback makes the following situations difficult to implement, e.g. the effectively integration of beneficial messages and satisfaction of information needs for takeholders.

To conduct the issues about optimal knowledge integration in collaborative manufacturing, an ontological architecture for integrating knowledge is proposed in this study. Its objective is to converting the situation of knowledge integration into field ontology integration in collaborative environment. The presented method reaches the integration of field ontology and obtains excellent result of knowledge integration. The regional ontology is constructed to definitely depict field-specific messages with terms and their relations. The relevant criteria are elicited and transformed with SWRL mode to deduce the integrated relations among fields and support decision-making. Meanwhile, an approach of ontology sharing is generated on the basis of the universal ontology.

A manufacturing procedure is planned to explain processes of component development and interchange messages among distributed firms. Afterward, an architecture of knowledge integration with ontology mode is proposed to assist the manufacturing procedure in collaborative settings.

3.1 Process of col-NTMT

Figure 1 illustrates the conceptual col-NTMT process, it is composed of several tiers: component design, process proposal, and component fabrication. Various knowledge with distinct and implied mode are included and distributed among various collaborative firms in each tiers and its tasks. In this collaborative environment, they have to interchange different information and knowledge of product. For example, the specifications and report of manufacturability of product are discussed and exchanged between collaborators that design and process proposal of product. Meanwhile, the detailed process plans are communicated and shared between members that process plan and fabrication of product. In addition, a model of top-layer task for col-NTMT is shown with IDEF0 (see Fig. 2), involving component design (T₁), process proposal (T₂), and fabrication (T₃). This model permits recognizing the information modules required within such procedure, and the communications among the tasks [51]. First, the task of component design intends to arrange component model according to the utilizability of resource, technology, and finance as well as quality constraints. This task is conducted through quality constraints and design knowledge. The export is pattern of component information, containing the group of component elements in process proposal and fabrication. Afterward, by analyzing the component messages, the export of process proposal task is pattern process information, involving process order, manipulation parameters, and equipment. This task will also offer response to the superior tasks for the optimal component design in the aspect of process making. Eventually, applying the results from the two superior tasks, the fabrication technician recognizes the functional needs and the restrictions of the material, tools and presents fabrication solutions to assist manufacturing of component. The export is pattern of fabrication information, containing material and tool configuration. This task will offer designer and deviser with response on problems of fabrication.

Various knowledge with distinct and implied mode contains in each task to lead the col-NTMT, involving relevant knowledge, such as experience, criteria, and cases. Meanwhile, it is easy to upload the new data into the knowledge repository during the collaborative procedure.

3.2 Architecture of ontological manufacturing knowledge integration and share

An ontology-based architecture of manufacturing knowledge integration and share (ObMKIS) is proposed to focus on the issue of optimization of knowledge integration among distributed collaborators. All knowledge of different database can be represented by an integrated ontology. Besides, a member can search and obtain more complete and exact information through this integrated ontology. The key tiers of the proposed architecture are illustrated in Fig. 3.

In the tier of repository, it is composed of several knowledge, e.g. component, process and fabrication, which are created and distributed in various firms or departments at various design stages. In the tier of regional ontology, on the basis of the schema of regional ontology, an essential knowledge is constructed and converted by filed firms into ontology of field knowledge, such as ontology of component, process proposal, and fabrication. The specific-field data are expressed as ontology cases with the vocabularies and their associations contained in the regional field. As for the schema of ontology, it is designed (as illustrated in Fig. 4) based on the previous researches [52–53]. This proposed schema is applied by respective firm to construct its field ontology with index of the tangible knowledge: (i) Title of notion – means a definite or implied name of knowledge notion; (ii) Pointer – some homogeneous properties of a definite notion to assure that it is comprehended and designated easily. Resources are relative to each other through properties and succession in knowledge ontology; (iii) Relation – there are several types of relation included: portion_of, inferior_class, and equality; (iv) Same term – reveals the synonym utilizing various notion items; (v) Characteristics – indicates information relative to a notion. Import/Export are the messages needed and the outcomes for implementing a definite task. Restriction means the fundamental principle for implementing a definite task. Function indicates the activities that a notion can execute. Resource represents the manner or tool for implementing a definite task; (vi) Standardized knowledge – records the connecting address of in-depth knowledge, the related contents involve itemized describing files or cases of a definite notion.

In the tier of universal ontology, the distributed regional ontologies are merged through approach of knowledge integration as a universal ontology. It can effectively seizes the conjunctions among regional ontologies. Eventually, in the tier of application, the collaborators at each tier of col-NTMT can query merged knowledge to obtain corresponding messages.

4.1 Development and ontology-based knowledge model

The regional ontologies are constructed to offer a general representation of knowledge in the several fields. Based on the classifications of ontology [52] and the identification of ontology features [54], the schema of regional ontology can be represented as

(Reg_O)_m = [N_m, P_m, L_m, C_m, T_m], m∈(u, v, w) (1)

Where u, v, and w represent component, process proposal, and fabrication respectively. Notion group N_m reveals the traditional notions applied in the mth regional field; Property group P_m indicates some homogenous properties of a definite notion to assure it is intelligible and specified; Relation group L_m depicts the relations between notions in the mth regional field; Case group C_m shows the actual examples for the notions; Criterion group T_m refers to the specific criteria, which depict the restrictions of the property values for notions or properties, and the approaches of assigning property values for notions or properties.

To express the ontology structure of non-traditional manufacturing-related element clearly, the partial notion hierarchies of regional ontology for the three specific fields are illustrated in Fig. 5. The notion hierarchy of component ontology is applied for depicting and modeling fundamental sorts of component-specific messages. The notion hierarchy of process ontology is the connection between component and fabrication two information and its superior tier depicts the specified process information. The notion hierarchy of fabrication ontology is utilized to depict fabrication-specific messages. The notions in the three fields are not restricted to the ones depicted above. By using the representation of ontology, it not only permit adding field-specific notions, but do farther categorization. In regional ontology, each notion has particular properties, such as identifier (id), title, content, etc. In addition to its own properties, the inferior-notion also has the general properties inherited from superior-notion.

4.2 Approach of ontology-based knowledge integration

As illustrated in the central portion of Fig. 5, the regional ontologies are able to be merged as a universal ontology by criteria of relevance, which can promote reuse and share of messages. Regarding the relations and restrictions among various fields, they can be represented as criteria of semantic and element relevance, which are translated in semantic web rule language (SWRL). In col-NTMT environment, each component is fabricated by at least one process proposal, and each manipulation in process proposal applies at least one item of non-traditional manufacturing. In the phase of initiatory design, each case of notion has the specific identifier (i.e. id), so the criteria of semantic relevance can be constructed to deduce the original relevance among cases of component, process proposal and non-traditional fabrication by the consistence of identifier. When the cases of process proposal and component have the identical id of component, is_fabricated_by, a relation, is able to be reasoned to depict that the component is fabricated through the process proposal, and the SWRL is defined as:

Component(?i),component_id(?i, ?j),ProcessProposal(?k), component_id(?k, ?j)

-> fab(?i, ?k)

When the cases of manipulation and nt-manufacturing have the same nt-fabrication id, a relation, utilize, can be reasoned to depict that the manipulation utilizes the nt-fabrication, and the SWRL is defined as:

Maniuplation(?i),nt-fabrication_id(?i, ?j),Fabrication(?k), nt-fabrication_id(?k, ?j)

-> utilize(?i, ?k)

Here, ?i, ?j, and ?k represent various variables, which indicate cases of component, id number, and process proposal based on the past principle [55].

As soon as the original relations among cases have been built, the cases with detailed elements are improved by engineers. When the itemized elements of cases have related restrictions in three fields, e.g. geometric type, material property, and availability of fabrication, the restrictions are constructed as the criteria of element relevance, and restriction, a relation, is assigned the relationship between two items. For instance, restriction(R_m, R_n) represents that specific elements of item R_n are decided through the relevant elements of R_m. Meanwhile, item R_m and R_n belong to two various fields which are linked by relations is_fabricated_by or utilize. If the relevant elements of item R_m are known variants, the criteria of element relevance can deduce specific element values for item R_n based on the elements of item X. In addition, the relation restriction is added between item R_m and R_n.

The elements are defined in ontology of component should be converted into a group of manipulations defined in ontology of process. Besides, the elements of component may be restricted by process capability. There is an element through-hole, for example, on the component, its accuracy requirements: D_h (diameter) = 4.5 mm; IT (international tolerance) grade = 4 ~ 6; R_a (surface roughness) = 1.25 ~ 2.5 µm. To achieve the above-mentioned demands, the process proposal for this through-hole should be Drilling, Fine boring, and HS-WEDM (High speed-wire electrical discharge machining) in order. The criteria of element relevant can be depicted as:

Component(?u),Through-hole(?th),has_element(?u,?th),Diameter(?th,?d),IT(?th,?it), SR(?th,?sr),equal(?d,4.5),less_than_or_equal(?it,6),greater_than_or_equal(?it, 4),less_than_or_equal(?sr,2.5),greater_than_or_equal(?sr,1.25),Process Proposal(?v),is_fabricated_by(?u,?v),Manipulation(?i),is_component_of(?i,?v),Manipulation(?j), is_component_of(?j,?v),Manipulation(?k),is_component_of(?k,?v),priors_to(?i,?j),priors_to(?j,?k)

-> Drilling(?i),Fine-boring(?j),HS-WEDM(?k),restrictions(?th, ?i),restrictions(?th,?j), restrictions(?th,?k)

Here, the item “?th” is an element through-hole of component “?u” and the “?u” is fabricated by process proposal “?v”. The “?v” includes three manipulations”?i”, “?j”, and “?k”. Hence, the deducted truth is that “?i”, “?j”, and “?k” are restricted through “?th”, and the cases are Drilling, Fine boring, and HS-WEDM separately. Also this mode can be applied to depict the restrictions of process proposal on element of component.

The elements are defined in ontology of component may decide the variables of the utilized nt-fabrication. If a fixture, for example, is applied in the manipulation for a component, the sizes (length and width) of the upper pressure plate are decided by increasing 37 mm length and 12.5 mm width on the existing sizes of component, and it can be represented as:

Component(?u),Dimension(?i),has_element(?u,?i),Length(?i,?j),Width(?i,?k),Process Proposal(?v),is_fabricated_by(?u,?v),Manipulation(?x),is_component_of(?x,?v),nt-fabrication(?y),utilize(?x,?y),Upperpressureplate(?upp),has_fixture_unit(?y,?upp), Upperpressureplate-variable(?var),has_fixture_variable(?upp,?var),increase(?p,?j,37),increase(?q,?k,12.5)

-> UpperpressureplateLength(?var,?j),UpperpressureplateWidth(?var,?k),restrictions(?i,?var)

Here, the item “?i” is an element dimension for component “?u” and the “?u” is fabricated by process proposal “?v”. The “?x” is a manipulation of “?v”, nt-fabrication “?y” is applied by “?x”. The “?y” has upper pressure plate “?upp” and “?upp” has variable “?var”. It can deduced that the dimension “?i”, restrictions “?var”, and values of “?p” and “?q” are specified to “?var” to depict the length and width of “?upp”. Furthermore, availability of nt-fabrication may restrict the component element and can be depicted as the identical mode as noted above.

The elements are clarified in ontology of process proposal depict the fabrication demands beforehand and restrict the group of fabrication capable of implementing the process. If a Die-sinking EDM (wire electrical discharge machining) is applied in the rough machining of blind hole, then the total amount of electrical corrosion for the electrode is decided by the two electrical variables, pulse width and peak voltage. It can be depicted as:

Roughmachining(?rm),Manipulationvariable(?mv),has_manipulation_variable(?rm,?mv),Pulsewidth(?mv,?pw),Peakvoltage(?mv,?pv), EDM(?edm)utilize(?x,?edm),Electrode(?e),has_EDM_unit(?edm,?e),Totalamountelectricalcorrosion(?aec),has_EDM_variable(?e,?aec),union(?s,?pw,?pv)

-> Totalamountelectricalcorrosion(?aec,?s),restrctions(?mv,?aec)

Here, the rough machining “?rm” applies Die-sinking EDM “?edm”, “?rm” contains pulse width “?pw” and peak voltage “?pv”; “?edm” has “?e”, and “?e” includes variable “?aec”. The “?pw” and “?pv”, two relation restrictions, can be deduced, and the value “?s” is imported to “?aec” as the total amount of electrical corrosion for “?e”. Furthermore, the restriction of nt-fabrication availability on process element can be depicted as the same mode as noted above.

Different criteria of element relevance are extended in col-NTMT, which are usually elicited on the basis of existing experience and analytical knowledge from domain members (e.g. engineers, technicians, and experts, etc.) and documents (such as technical journals, manuals, articles, and so on). They also offers support of decision making with the reasoned truths except integration of information.

4.3 Approach of similarity mating

To enable the proper empirical information can be extracted for sharing to instruct relevant tasks, the semantic mating method also has to be developed except the representation and integration of information. In general, the procedure of ontology mating guided on similarities of titles and contents of notion and its relations [37, 56–57]. The contents of notion are represented as properties of notion. Furthermore, the messages regarding component, process proposal, and fabrication can be extracted with titles and default properties of notion. The extraction of ontology deals with a process of similarity for titles and properties of notions based on the previous method presented [58–59]. The procedure of similarity calculation is shown in Fig. 6 and the details are depicted below.

In this study, the similarity of notion title is computed through the past researches [60–61]. The titles of notion are split as item groups of bunch words. The similarity for item groups of two various notion titles is then computed by the following equation.

Sim _N(N^q, N^u) = $\left|{N}_{mg}^{q}\cap {N}_{mg}^{u}\right|$ / $\left|{N}_{mg}^{q}\cup {N}_{mg}^{u}\right|$ = h / (i + j – h) (2)

Here, N^q means the query notion offered through the member; N^u indicates the title of notion in universal ontology; ${N}_{mg}^{q}$ shows the item group of the notion N^q; ${N}_{mg}^{u}$ means the item group of notion N^u; i and j represent the amount of ${N}_{mg}^{q}$ and ${N}_{mg}^{u}$ words respectively; h reveals the amount of similar groups between ${N}_{mg}^{q}$ and ${N}_{mg}^{u}$.

According to the features of ontology for col-NTMT, there are three data sorts of properties classified to calculate similarity of property definitely. In the similarity of pure numerical property, suppose ${A}_{m}^{q}$shows the mth pure numerical property for query notion N^q and ${A}_{m}^{u}$ means the same pure numerical property for notion N^u in universal ontology. The ${P}_{m}^{q}$ and ${P}_{m}^{u}$ are represented for values of ${A}_{m}^{q}$ and ${A}_{m}^{u}$ respectively. The equation of similarity is shown below:

Sim _PN(${A}_{m}^{q}$, ${A}_{m}^{u}$) = 1 $-$ $\left|{P}_{m}^{q}- {P}_{m}^{u}\right|$ / ${P}_{c}$ (3)

Where Function Sim_PN( ) reveals the similarity of pure numerical property; P_c means the value gauge of property.

In the similarity of interim numerical property, suppose ${B}_{m}^{q}$shows the mth interim numerical property for query notion N^q and ${A}_{m}^{u}$, the one in universal ontology, the ${B}_{m}^{q}$and ${A}_{m}^{u}$ (interim numerical values) are [${e}_{m}^{q}, {g}_{m}^{q}$], [${e}_{m}^{u}, {g}_{m}^{u}$], respectively. The computation of similarity is listed below:

Sim _IN(${B}_{m}^{q}$, ${A}_{m}^{u}$) = 1 $-\left\{\frac{1}{2\text{max}\left(g\right)}\right\}\{{[{\left({e}_{m}^{u}-{e}_{m}^{q}\right)}^{2}+{\left({g}_{m}^{u}-{g}_{m}^{q}\right)}^{2}]}^{1/2}$} (4)

Where Sim_IN( ) reveals the similarity of interim numerical property; the extreme value of the property, max(g), applied to regularized the messages. Furthermore, this value is decided through the technical viability.

In the similarity of cord property, suppose ${C}_{m}^{q}$shows the mth cord property for query notion N^q and ${A}_{m}^{u}$, the one in universal ontology, the ${C}_{m}^{q}$and ${A}_{m}^{u}$ (cord values) are ${CP}_{m}^{q}$, ${CP}_{m}^{u}$, separately. The computation of similarity is listed below:

Sim _CP(${C}_{m}^{q}$, ${A}_{m}^{u}$) = $\left\{\begin{array}{c}0, {CP}_{m}^{q}\ne {CP}_{m}^{u}\\ 1, {CP}_{m}^{q}={CP}_{m}^{u}\end{array}\right\}$ (5)

To compute the similarity of universal property for goal notion N^q and N^u in universal ontology, the closest method is used [58]. The similarity of universal property contains the above-mentioned equations (from Eq. (3) to Eq. (5)). Therefore, the equation can be represented as:

Sim _U(N^q, N^u) = [$\sum _{m=1}^{h}{w}_{m}\times {Sim}_{PN}({A}_{m}^{q},{A}_{m}^{u})]$/$[\sum _{m=1}^{n}{w}_{m}]$ +

[$\sum _{m=h+1}^{r}{w}_{m}\times {Sim}_{IN}({B}_{m}^{q},{A}_{m}^{u})]$/$[\sum _{m=1}^{n}{w}_{m}]$ +

[$\sum _{m=r+1}^{n}{w}_{m}\times {Sim}_{CP}({C}_{m}^{q},{A}_{m}^{u})]$/$[\sum _{m=1}^{n}{w}_{m}]$ (6)

Here, w_m means the weight for the mth property.

According to the previous approach presented [3], the eventual similarity between N^q and N^u is calculated by using the method of weighted sum and the equation can be revealed as:

Sim (N^q, N^u) = σ ⋅ Sim_N(N^q, N^u) + τ ⋅ Sim_U(N^q, N^u), σ, τ∈(0,1) (7)

Here, σ and τ indicate the weighting coefficients for similarity of notion title and property respectively. They could be computed in accordance with the steps (as shown in Fig. 7). Firstly, the third-order matrix of decision is built by applying the significance scale of property proposed [62–63].

D ₃ =$\left[\begin{array}{cc}1& 2\\ 1/2& 1\end{array}\right]$

Afterward, by using the Eq. (8) to compute the weight coefficient w.

w _m = ${w}_{m}^{\#}/{\sum }_{m}^{2}{w}_{m}^{\#}$, m ∈+N (8)

Here, ${w}_{m}^{\#}=\sqrt[2]{\prod _{n=1}^{2}{d}_{mn}}$, m∈+N

Next, the maximum eigenvalue ${\eta }_{max}$ is calculated through Eq. (9).

$${\eta }_{max}=\sum _{m=n=1}^{2}{w}_{m}\bullet {Sim}_{n}$$

Here,${Sim}_{n}=\sum _{m=1}^{2}{d}_{mn}$

Finally, the consistence test is essential for checking the weight coefficients. When ${\eta }_{max}$ ≥ ${\eta }_{th}$ (threshold eigenvalue), the values in the matrix of decision have to be modified, until the condition, ${\eta }_{max}$ < ${\eta }_{th}$, is satisfied. In the real utilization, the weight coefficient could be modified properly.

By the above computations, the notion with the similarity grade can be obtained. Besides, its relevant messages can also be tracked by the constructed relations in the universal ontology.

A prototype framework, named ObMKIS, is generated by utilizing the above-mentioned approaches and techniques. The implementation of proposed architecture and its performance are explained and discussed in this section.

5.1 Software architecture

As illustrated in Fig. 8, there are several tiers involved in this proposed architecture. They are individually depicted as follows. In the tier of application, it offers a formal platform for collaboration among members through several techniques, such as component modeling, process proposal, and evaluation to reach querying and communication of information. In the tier of entry, it is utilized to assure the information security in collaborative settings by several modes, e.g. authentication and management regarding ID, character and permission. In the tier of administration, a regular mechanism is used for management of enterprise, process and information in collaborative environment. Management of enterprise involves ontology querying, knowledge capturing, and edition domination; Management of process consists of modeling, access, implementation, and evaluation; Management of information contains storages, documents, and fundamental data. In the tier of synthesis, an information integrated mechanism is developed in heterogeneous and distributed environment. It is composed of two portions: web and ontology. The former helps the integration and the management of enterprise application to reach construction of enterprise services by the web-based form. The latter offers the semantic similarity of distributed information by the construction, mapping, integration, deduction and management of ontology to reach the goals of reducing repetitions and loose-relation in the collaborative fabrication environment. Eventually, in the tier of repository, it is used to store and access different messages and knowledge, e.g. resource repository, knowledge repository, file transfer protocols, and JavaBeans.

5.2 Procedure of implementation

As illustrated in Fig. 9, there are several steps involved for implementing the proposed framework. They are depicted in order:

Step I

According to the approach of Section 4.1, the ontology-based knowledge model is generated by applying the ontology editor (protégé tool). This proposed model defines the notional structures of component, process proposal, and fabrication. The notions of the three fields are represented with classes and arranged as hierarchy of class;

Step II

Based on the method of Section 4.2, the criteria of relevance are elicited from specialists’ knowledge and expressed with SWRL. This format permits members to represent criteria in the light of defined notions. Meanwhile, the criteria make up an intention between consequent and antecedent. One or more notions are represented by these two portions;

Step III

the definite messages of three fields are imported into the system. They can be represented a group of objects, properties, and relations. The cases (or individuals) of classes are represented by objects; the relations among cases are expressed through properties of objects. The properties of data can be applied to depict relations between cases and values, that is, attributes of cases;

Step IV

the semantic reasoner implements the pre-built criteria and add recently relations and properties to the cases. When the condition designated in the antecedent of criterion kept, the condition designated in the consequent can be deduced;

Step V

By using the proposed ontology model, the query processor deals with user’s queries applying the approach of similarity mating presented in Section 4.3 and exports the outcomes to the user interface.

5.3 Example

The collaborative procedure of the component Flower-type extrusion mold, process proposal Flower-type extrusion mold process, and fabrication WEDM is utilized as an instance. This example is simplified in order to plainly describe. Figure 10 shows the messages and relations about the three portions. Firstly, the engineers clarify the fundamental messages without taking relations among these fields into consideration in the collaborative procedure. There are several data attributes included in Component Flower-type extrusion mold: id, title, edition, material and size. Furthermore, Process Flower-type extrusion mold is depicted with id, title, and id of component. In addition, the manipulations, blanking 010, milling 011, and wire cutting 012, are also involved in Process proposal. The Wire cutting 012 consists of id of manipulation, id of fixture, and variables of manipulation (Pulse width and Peak voltage). The original messages of HS-WEDM involves title of fabrication, id of fabrication, wire, and fixture (clamper, base plate, middle vise, mounting bolt, etc.). The original messages are imported into the system as cases of notions pre-designated and they are shown in Fig. 11.

Next, there are several criteria utilized to deduce the implied messages and relations (see Fig. 10). Criterion 1 can deduce that Flower-type extrusion mold is fabricated by Flower-type extrusion mold process. Criterion 2 can deduce that Wire cutting 012 utilizes HS-WEDM. Criterion 3 can acquire the relation restrictions among size of Flower-type extrusion mold and Fixture variable of Middle vise 020 and adjust the length and width of fixture by reducing 20 and 80 mm to the machining component, respectively. As for the height, it is kept at the center of the component height. Criterion 4 can get the relation restrictions among Manipulation variables of Wire cutting 012 and Wire variable of Electrode head and create a new element minimum cutting speed for Electrode head decided through two variables: pulse width and peak voltage. The detailed messages of the criteria depicted in SWRL and the reasoned truths are illustrated in Fig. 11. Actually, more messages, criteria, and relations are required in the collaborative environment and can be conducted in the same approach as stated above.

Based on this proposed integrated knowledge framework, the member can inquire the needed messages. The query results and the detailed message of the acquired cases about Flower-type extrusion mold are illustrated in Fig. 12. Besides, the member can also get the relevant messages of process, fabrication (particularly, nt-fabrication), and criteria of relevance tracking the attributes to relevant items.

5.3 Analysis of Strength

There are several main technical strengths achieved by the proposed methods. They are depicted as follows:

The criteria of relevance make information possible to be integrated effectively by the semantic relations and restrictions among notions in three kernel fields and can offer assistance for collaborative product development by reasoning information for specific design cases. Furthermore, the consistence of information can be assured with the restricted relations deduced through the criteria of relevance.
The merged universal ontology connects different sorts of messages systematically and offers and efficient manner to quickly obtain the appropriate and existing messages utilizing the approach of similarity mating. Moreover, the reuse of information is vital to reduce the lead time, cost, and changes in product development.
By using the systematic, definite specification of items in various fields and of the relationships among them, the ontology-based modeling can represent the messages in a high-level calibration with flexible form. Besides, it also improves information sharing in the collaborative circumstance.

Yet, the management of information is extremely complex and needs appropriate tools in collaborative product generation. Therefore, to improve the functionality of the presented approach as an all-round solution of information, several issues have to be considered, e.g. management of knowledge, working process, and design change, etc.

The requirements of non-traditional manufacturing knowledge integration and share among field of component, process and fabrication are emphasized in this study. An ObMKIS framework is proposed under communications among the three fields. By generating proper regional ontologies, the field-specific messages with higher semantics of words and their relations are represented. In addition, it also promotes the accuracy and interpretability of information. Furthermore, the regional ontologies are merged with restrictions and relations and are definitely depicted through SWRL. The sharing of messages is achieved by the approach of similarity mating on the basis of universal ontology. The results, the integration and querying of the essential knowledge, reveal the remarkably contribution to col-NTMT so as to enhance several capabilities, e.g. effective integration of individual knowledge, improvement of NTMT knowledge reuse as well as reduction of the cycle time and cost of product development.

This presented framework and approach have been executed in field of collaborative manufacturing. However, the efficiency about ontology mapping still needs to be enhanced in this study. Therefore, there are several research issues considered in the future, generation of automatic ontology needs to be explored for improving the efficiency of ontology development. Meanwhile, in-depth study regarding different restrictions among component, process, and fabrication could be activated to promote further cross-discipline collaboration. In addition, more indicators should be taken into account in the matching of ontology. Eventually, it is beneficial way to expand the component field to assembly or entire product field.

Availability of data and materials: No availability of data and materials.

Competing interests: No conflict of interest.

Funding: This study is not supported by any funding.

Authors’ contribution: This whole study, including texts and figures, is finished by single author.

Acknowledgement: This study is propped in portion through the Wenzhou Polytechnic in China for the administrative support and encouragement.

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Study on Ontological Knowledge Integration of Micromachining for Collaborative Process

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Abstract

Figures

1. Introduction

2. Review Of Related Work

2.1 Issues of ontology development

2.2 Integration and share of information with ontology mode

3. Architecture Of Collaborative Non-traditional Manufacturing Technology

3.1 Process of col-NTMT

3.2 Architecture of ontological manufacturing knowledge integration and share

4. Approaches Of Ontology-based Knowledge Integration And Share

4.1 Development and ontology-based knowledge model

4.2 Approach of ontology-based knowledge integration

4.3 Approach of similarity mating

5. Execution

5.1 Software architecture

5.2 Procedure of implementation

5.3 Example

5.3 Analysis of Strength

6. Conclusion And Future Perspective

Declarations

References

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