Georges Dubourg

Ludivine Fadel-Taris

Isabelle Dufour

Claude Pellet

Cédric Ayela

Collective fabrication of all-organic microcantilever chips based on a hierarchical combination of shadow-masking and waferbonding processing methods

This paper describes a new collective microfabrication process of all-organic microcantilever chips. This method is based on the hierarchical combination of shadow-masking and wafer-bonding processes. The shadow-masking combines deposition and patterning in one step thanks to spray-coating through a polymer microstencil that gives the opportunity of patterning thermosensitive material such as PMMA. The shadow-masking parameters have been optimized to obtain suspended microcantilevers characterized by a convenient thickness profile. The resulting PMMA structures were then transferred onto SU-8 chips by using an SU-8 waferbonding process. The effect of the UV exposure dose of both SU-8 layers in contact on the bonding quality has been investigated and optimized. With the optimized bonding process we have achieved the large scale transfer of microstructures with a yield of 100% and a bond strength of 50 MPa. These microcantilevers were also tested at resonance, to determine Young's moduli of patterned polymers. The low values obtained (below 5 GPa) make these organic MEMS structures strong candidates for highly sensitive sensing applications when used in the static mode.

Introduction

Silicon-based free-standing microcantilevers are widely used as micro-and nanoelectromechanical systems. More specifically, silicon cantilevers have shown great potential for sensing applications such as, molecular recognition [1,2] or virus detection [3] in liquid media. These cantilevers generally operate in either the dynamic mode, where analytes binding on the cantilevers increase mass and thus decrease the resonant frequency, or in the static deflection mode, where analytes binding on one side of the cantilevers causes unbalanced surface stress resulting in a measurable mechanical deflection. In the dynamic mode, the operation of silicon microcantilevers in viscous fluids is limited by the fact that the quality factor (Q factor) is very low because of viscous damping and squeezing effects [4]. The static mode is usually preferred in liquid media but the deflection measurement may be difficult due to intrinsic rigidity of silicon, resulting in mechanical deflections that rarely exceed hundreds of nanometers. In this context, the introduction of polymers in microcantilever fabrication as an alternative solution to silicon is particularly promising. In fact, they are attractive for sensitive sensing applications in the static mode (bilayer effect) as single-use sensors due to their low cost, good processability, bio-compatibility [5] and tunable properties that may be achieved by an appropriate design of the materials [6]. Indeed, an organic free-standing structure is more flexible than a silicon one. Thus, the use of such low Young's modulus materials will enhance the transduction of molecular recognition using, for instance, an organic biomimetic sensitive layer, thus improving limits of detection of analytes. SU-8 epoxy negative photoresist is widely employed for organic microcantilever fabrication due to its high achievable aspect ratio, its chemical compatibility [7] and its versatility due to tunable properties obtained by mixing SU-8 with other materials. For instance, SU-8 may be made piezoresistive by mixing it with black carbon [8]. Generally, standard organic microcantilever fabrication methods are: (i) using a sacrificial layer, or (ii) sealing a free structure by transferring it to another layer (Figure 1) [9]. For (i), different polymers can be used as a sacrificial layer, including positive photoresists [10], metals [10] or thermally decomposable materials [11]. But this method is mostly limited by the chemical compatibility between the structural and sacrificial layers. For example, solvents used to remove the sacrificial layer [12] can also elute the structural one. Also, no concrete examples were found in the literature where thick films (> 50µm) of positive photoresists, metals or thermally decomposable materials were used as a sacrificial layer. This is not desirable for organic microcantilevers where large deflections are expected in the static mode. For this, using uncrosslinked SU-8 as the sacrificial layer shows great potential. Indeed, it can maintain a flat (doi: 10.1088/0960-1317/21/9/095021).

surface (when crosslinked SU-8 is used for the supporting layer) for subsequent surface patterning of the structural layer, and can be etched selectively in the presence of different organic materials. However, at the moment, methods using uncrosslinked SU-8 as the sacrificial layer require a metallic UV-blocking coating between the structural and the sacrificial layers to protect this layer from UV radiation [13], or the use of complementary light absorption properties for the structural polymer [START_REF] Dubourg | Proceedings of the MNE conference (Micro and Nano Engineering) Ginoa[END_REF]. Other reported fabrication methods that include the use of adhesive PET lamination to create three-dimensional flexible microfluidic networks [9]. Although the authors suggest that this technique could be used to fabricate suspended microstructures, no fabricated devices were reported. An alternative solution is to use the transfer method (ii) that can be combined with several processing methods such as nanoimprint lithography for the patterning of thermoplastics and biopolymers [9] or photolithography. However, a manual, one-by-one transfer of microstructures is most often observed in the literature [START_REF] Johansson | [END_REF]16]. Only two examples showed promising results for the wafer level fabrication of such organic devices. In a first approach, non-reticulated SU-8 was used to transfer SU-8 cantilevers, allowing an integrated readout of cantilevers behavior thanks to single-mode waveguides [17]. This process was then improved by using a partially reticulated SU-8 layer where the transferring layer could be patterned individually before bonding [18]. These pioneering works demonstrated the large scale fabrication of free-hanging organic microcantilevers. However, based on this approach, further improvements are necessary since: (i) a transparent wafer is mandatory for precise alignment of both substrates in contact during transfer, restricting the choice of substrates, (ii) qualitative characterization of bonding quality does not ensure a strong bond of transferred microstructures, with possible consequences on organic microcantilevers behavior, as already observed for resonant silicon ones [19,[START_REF] Fadel-Taris | Proceedings of IEEE International Frequency Control Symposium[END_REF] and (iii) the fabrication process was validated only on SU-8 microcantilevers. Thus, the wafer level microfabrication of organic free-standing structures clamped on a substrate must be optimized to define a standard fabrication process. In this context, we have combined two hierarchical processing methods to achieve the large-scale fabrication of all-organic microcantilever chips. First, the solution inspired from the wafer-level bonding of organic materials has been optimized for the rapid and collective microfabrication of organic freestanding cantilevers. Then, it is particularly interesting to combine this transfer method with a versatile patterning method where photosensitivity of materials is no longer mandatory for the structural layer. Indeed, some classes of organic materials such as biomaterials, gels, and thermoplastics are difficult or impossible to be structured with standard microfabrication methods based on optical lithography. To overcome this challenge, we have developed a shadow-masking approach dedicated to organic materials to create organic microcantilevers that remains compatible with other processing methods. Shadowmasking is a powerful method used for the micro-and nanopatterning of metals [START_REF] Selvarasah | [END_REF]. This method is based on the fabrication of a stencil where mechanical apertures allow the deposition and patterning of materials in one step. To our knowledge, only one example of the combination of shadow-masking with organic materials can be found in the literature for the fabrication of organic thin film transistors [22]. Thermal evaporation of an organic semiconductor was used for deposition, which is not compatible with higher molecular weight polymers, such as those used in the field of organic microsystems. In the present work, we propose a method where the organic structural material is spray-coated through a polymer microstencil, combining deposition and patterning in one step. This shadow masking approach does not require photo-masking of materials and allows patterning of most organic materials, i.e., photo-or thermosensitive ones, showing the great potential of this method in the field of organic microsystems. Moreover, using a flexible polymer microstencil, it can be easily adapted for the patterning on topographically rough, curved and unconventional surfaces [23,24]. Flexible stencils made of PDMS [24], parylene [START_REF] Atsuta | Proceedings of the 8 th International Conference on Miniaturized Systems for Chemistry and Life Sciences[END_REF] or SU-8 [23] have shown great potential for metals patterning. Also, hybrid parylene-SU-8 microstencils have been used for the patterning of nontraditional materials [START_REF] Pal | [END_REF]. In the present work, the microstencil is made of SU-8 photoresist and requires only two photolithography steps, making this solution consistent with the low-cost characteristic of organic MEMS. Also, SU-8 is a suitable material for microstencils fabrication since high aspect ratios can be obtained easily, combining high resolution with mechanical robustness. Thus, thanks to the hierarchical combination of shadow-masking and waferbonding processing methods, we have achieved the wafer level fabrication of PMMA suspended microcantilever chips characterized by a low Young's modulus and suitable for further integration in industrial processes, due to collective fabrication at low-cost. structures will be clamped and a temporary top substrate used for structural layer patterning. Single side polished 4 inches silicon wafers with a thickness of 525 µm were used as substrates. The two substrates were cleaned by using UV-Ozone for 10 minutes to improve adhesion of subsequent deposited layers (Figure 2.a and b). These substrates were rinsed with deionized water and dehydrated at 200 °C for 2 h. On the top substrate, two thin layers of Omnicoat TM were successively spun at 1000 rpm and baked at 200 °C for 1 min that result in an omnicoat thickness of 200 nm (Figure 2.c). This layer will act as a sacrificial layer to release microstructures after transferring step.

Shadow-masking process

The next step to be considered is the patterning of the structural layer on the top substrate. The present work proposes a versatile method to fabricate organic microcantilevers that does not require photomasking of materials as currently used [9] and thence, allows patterning of most organic materials, i.e., photo-or thermosensitive ones. For this, the proposed technique combines deposition and patterning in one step thanks to spray-coating through polymer microstencils.

Organic microstencil fabrication

Polymers are attractive materials to fabricate miniaturized microstencils for their low cost, good processability and flexibility [23][24][START_REF] Atsuta | Proceedings of the 8 th International Conference on Miniaturized Systems for Chemistry and Life Sciences[END_REF][START_REF] Pal | [END_REF]. Indeed, the polymer microstencil fabrication process proposed in this work requires only two photolithography steps. Also, flexible polymer microstencil can be adapted to several varieties of substrates such as flexible or non-flat ones. The process fabrication starts by the spincoating of a 200 nm omnicoat sacrificial layer on a silicon wafer to allow the release of microstencil from substrate after fabrication. The microstencil process results from the structuration of two levels of SU-8 epoxy-based negative photoresist (SU-8® 3005 and 3050) (Figure 3). SU-8 is a suitable material for this two steps device since several layers having different thicknesses (0.5-200 µm) can be stacked-up by multiple spin-coating and UV-exposures to define high aspect ratio structures. In the present work, a first thin film of SU-8 was patterned to create apertures with high resolution. The influence of microstencil thickness was studied and chosen in order to obtain the most suitable PMMA thickness uniformity used as structural layer. For this, an 8 µm thick SU-8 layer was spun, baked at 95 °C for 3 min and UV-exposed with a dose of 150 mJ/cm 2 by using a manual mask aligner (MJB4, Suss Microtec. Corp, Germany). After, the layer was post-exposure baked at 65 °C for 1min and 2 min at 95 °C. But with this thickness, microstencil handling without cracking is challenging. Thus, circular apertures characterized by a diameter of 1 mm were patterned with a 100 µm thickness of SU-8 that encircle the microstructure apertures on the thin layer, to strengthen the microstencil. This 100 µm thick layer of SU-8 was successively spun and baked at 95 °C for 45 min. Then, this layer was exposed with a dose of 250 mJ/cm 2 and baked at 65 °C for 1min and 3 min at 95 °C. After, both layers were simultaneously developed with propylene-glycol-monoether-acetate (PGMEA, Microchem Corporation). A low stress microstencil was required to allow a flat and conformal contact with the substrate during the spray-coating. In fact, a gap between the microstencil and the substrate would induce a loss of resolution in the geometries of the designed microstructures. Highly cross-linked SU-8 is known to induce residual stress in the patterned films. Several sources have been identified [27,28] including residual solvent in the polymeric matrix [27], UV exposure dose [29], heating temperature for soft [28] and post-exposure bakes [27] and induced stress due to CTE mismatch between SU-8 and the substrate [30]. This can result in buckling of polymer microstencil and thus, gap increase during deposition. An efficient solution was proposed by introducing a hard-bake at the end of the SU-8 process, reducing residual stress gradient in highly cross-linked SU-8 films [27]. The hard-bake must be adapted to the thickness layer. For our process, a hard-bake step at 150 °C for 7 min was optimal. To finish the microstencil fabrication, the substrate was dipped in the omnicoat developer (MF 319) for 2 h to release the organic microstencil.

Preprint (doi: 10

Sacrificial layer release

To finish the fabrication process, the top wafer must be removed to obtain chips composed of polymer free-standing microstructures clamped on the bottom substrate obtained by collective fabrication. The omnicoat layer initially deposited was etched by using MF319 developer (Figure 2.j). The low gap between both wafers (about 150 µm) can prevent the release. Thus, to accelerate structures release, channels characterized by a width of 500µm were introduced on the transferring layer to enhance access to the sacrificial layer, reducing the release duration from 24 h to 6 h. Last, the resulting wafer, containing free-standing microcantilevers, was cleaned according to a protocol where successive batches of omnicoat developer (to remove residual omnicoat), isopropanol, ethanol and deionized water were used. Cantilevers chips were then dried using a hotplate (40 °C), ensuring rapid evaporation of water and thus, avoiding stiction of cantilevers onto the transferring layer.

Results and discussion

Organic microcantilevers characterization

By combining shadow-masking and wafer-bonding processing methods, we have successfully achieved the fabrication of free-standing PMMA cantilevers (Figure 4 and5) characterized by a length ranging from 100 to 700 μm and a width ranging from 50 to 250 μm. The geometries are in agreement with the design (maximum standard deviation of 2% for geometry with respect to designed mask), but the thickness profile is not uniform: the bottom surface of the cantilevers is not perfectly flat. In fact, protrusions at the microcantilever edges appear during the spray-coating through the shadow-masks (Figure 6). This effect has already been observed with other printing methods, such as screen-printing, nanoimprint or microfountain pen [32]. In our case, one origin of the phenomenon is capillary interactions that occur between the PMMA solution and SU-8 due to the wettability of anisole used as solvent for the PMMA solution [START_REF] Low | United States Patent Application Publication[END_REF]. To confirm this wettability effect, the SU-8 microstencil surface was made hydrophilic by submitting it to a short plasma oxygen event, resulting in the formation of PMMA clusters in the microstencil apertures after subsequent spray-coating, showing repulsive interactions between anisole and SU-8. At the moment, no concrete solution was found to avoid completely the edge effect, but the combined spray-coating/ shadow-masking process was optimized to obtain a convenient PMMA profile. For this, by using a mechanical profiler, the influence of the microstencil thickness and the solid content of the PMMA solution on the polymerized PMMA thickness profile has been studied (Figure 6.a).

From this figure, it can be seen that at the center of the cantilever, the thickness is thin while at the edges it is increased, resulting in a thickness gradient with a parabolic profile. Also, the height of protrusions is defined by microstencil thickness: protrusions height increases with the microstencil thickness (Figure 6.a). A thin microstencil is thus required to limit this effect. But with a too thin SU-8 microstencil, there is not any PMMA material at the center of the structure and thus the whole microstructure surface cannot be covered. In this context, to restrict the protrusions height to a few micrometers, a microstencil characterized by a thickness of 8 µm has been used while apertures have been then filled thanks to ten successive spray-coatings. For this, a homemade polymer solution that contains 4.5 % of PMMA diluted in anisole has been used to reduce size of droplets. In this case, the thickness at the center of the cantilever is 5 μm, while at the edges it is 7.5 µm (figure 6.b). With the optimized process, the aspect ratio between the height at the edge and the height at the center of the structures was clearly reduced, since an improvement from 8.3 to 1.5 was obtained.

Preprint (doi: 10 values. These organic cantilevers, characterized by a low Young's modulus, have the potential to serve as highly sensitive transducing devices in biological and chemical sensing applications. Indeed, for a 200μm long bi-layered cantilever, the static deflection due to swelling effects in an organic sensitive layer will be thirty times higher when using these organic materials instead of silicon (theoretical calculation using a modified Stoney equation [37]). Based on this approach, work is now in progress for the development of tuned Young's modulus organic materials for highly sensitive biological and chemical sensing applications in static mode.

  of Micromechanics and Microengineering, vol. 21, 2011, 095021, 9 pages. (doi: 10.1088/0960-1317/21/9/095021).

  Figure 5 thicknes

Preprint (doi: 10 Figur 2. Fabri

	t -Journal o of Microme chanics and d Microengi ineering, vo
	0.1088/0960 0-1317/21/9 /095021).
	re 1. Genera al scheme of	organic free--standing mi
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	mechani ical self align nment of bot th wafers has s been introd
	accuracy y of 50 μm (F Figure 2.i). T To complete t the fabricatio
	onto the	top wafer (F Figure 2.c),	allowing the e release of m
	dissoluti ion (Figure 2 2. j). The nex xt sections de etail the proc
	Figure 2 2. Fabricatio on process of f organic mic crocantilever
	substrate es (c) Deposi ition of omn icoat sacrific cial layer (20
	of PMM MA microstruc ctures by spr ray-coating t through poly
	for 2 m minutes. (f) C Coating of	the transferr ring layer o
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	Remova al of omnicoa at layer for m microstructure res release
	2.1 Subs strate prepar ration
	Prior to	fabrication	process, two o silicon sub bstrates mus

  Preprint -Journal of Micromechanics and Microengineering, vol. 21, 2011, 095021, 9 pages. (doi: 10.1088/0960-1317/21/9/095021).

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	layer. UV V-exposure d doses were th hus chosen i in order to ha ave the same e crosslinking g level for b oth layers in n
	contact.	From figure	7, it can be	seen that the e bond area d decreases wit th dose incre ement. This in ndicates that t
	a high yi ield was asso ociated to a l low crosslink king level of f SU-8 layers s before trans sfer that allow ws complete e 3.3 Preli iminary mech hanical char racterization n.
	SU-8 the ermal polym merization dur ring the trans sfer. Also, th he bond stren ngth has been n investigate ed by using a a
	Dage 20 000 shear st ation. Resul lts confirm t that the bon nd strength i is high for a a low expos sure dose. A A To confi irm the poten ntial of such organic mic crocantilever s as flexible devices, the e next step co oncerned the e
	5. Scanning s of 10 μm, a a length of 5 electron mi icroscopy (S 20 μm and a SEM) image a width of 10 e of PMMA 00 μm. this case strength supportin bond, a d to be opt Using la occur th robustne bonding layer du value aroun e, the exposu ng layer belo dose of 160 timal, since i ayers with lo hat damages ess, inducing . Figure 8.a ue to too low nd 10 MPa is ure dose for ow 270 mJ/c mJ/cm 2 for t it correspond ow crosslink the patterne g a possible shows the s w UV-exposu s recommend r the transfer ded to ensur rring layer m re strong bon must be belo microcantil lever charact terized by a a nd between t ow 240 mJ/c two SU-8 lay yers [31]. In n measure ment of me chanical pro operties of th he patterned d structural l layer. For th his, out-of-pl lane flexural l cm 2 and the one for the e bending resonant m modes were s studied usin ng a laser D oppler vibro ometer (Poly ytec MSA50 00); Young's s cm 2 . Howev the transferri ds to a bond s king levels du d layers. In damage of spreading of ure doses tha ver, to obtain ing layer and strength of 5 during the bo fact, a decre the structure SU-8 transf at are respect n a maximum d 190 mJ/cm 50 MPa. onding proce ease of cross es under the ferring layer tively 80 and m bonded are ea of 100% and a strong g modulus s of the struct tured layer w was then calc culated thank ks to equation n (1) [35]: m 2 for the sup ess, a reflow slinking leve e pressure an and the flatt d 110 mJ/cm pporting one w of the SU-el causes a l nd temperatu ttening of the m 2 . On the co e were found 8 layers can loss of SU-8 ure used for e supporting ontrary, with d E= 4π 2 f n 2 L 4 12ρ λ n 4 h 2 (1) n 8 r g h In this e mode, L density characte equation, E r L and h are r of the mate rized by a le represents th respectively erial, and λ ength of 200 he cantilever the length a r Young's m and thicknes value of the s the resonan nce frequenc cy of the n th h modulus, f n is ss of the mi crocantilever r, ρ represen nts the mass s e n th resona nce mode. For PMMA A cantilevers s n the eigenv µm and an average thic ckness of 16 µm, a reson nance frequen ncy of 122.8 8
	optimal kHz was exposure do s measured f ses, a clean a and strong bo ond occurs b between the t two SU-8 lay yers (figure 8 8.b). for the first f flexural mod de, giving a Y Young's mod dulus value o of 3.7±0.5 G GPa. Also, to o
	To summ strong bo marize, to im ond and a lim mprove the w mited reflow, wafer-bondin , the optimal ng process b l UV-exposu based on SU re doses mus U-8, with a b st be 160 mJ/ bonded area J/cm 2 and 190 of 100%, a a demonst trate the vers satility of the e transferring g process, SU U-8 cantilev vers (Figure 9 9) were fabr ricated using g 0 mJ/cm 2 for r standard d photolithog graphy. A re esonance fre equency of 10.7 kHz w was obtained d for a SU-8 cantilever r
	respectiv vely the trans sferring and characte rized by a le ength of 490	the supporti ing layers. W With these val lues, the ove erly level of µm and a th hickness of 8 8 µm, giving a Young's m modulus of 4 polymerized d 4.0±0.5 GPa. .
	layers is s avoided bef fore transfer A simila ar method w was already p proposed for that ensures s a strong ad dhesion of bo onded layers s during the r the determi ination of Yo oung's modu ulus of SU-8 transfer. But t 8 cantilevers s
	SU-8 lay yers are nev vertheless po olymerized e enough to p prevent their r damage (F Figure 8.b). T The transfer r [10]. Va alues obtaine ed ranged fro om 3.5 GPa a and 4.5 GP Pa, dependin ng on the thi ickness of S SU-8. Values s
	process h has been thu us optimized obtained d in present work are in	to standardiz ze large-scal e microfabri cation of org ganic microc antilevers. agreement with those p previously d described [10 0] and those obtained in n
	other wo orks [27, 36	], validating	the method d for both PM MMA and S SU-8 materia als. Also, the e low values s
	obtained d show a g great potenti ial for thes se organic f free-standing g microcant tilevers to o obtain large e
	mechani ical deflectio ons for a vari ety of applic cations, inclu uding chemic cal sensing in n static mode e.
		6. Profile me easurement b before transf fer of PMM A cantilever rs (a) Cantile ever fabricat ted with one e
	oating step b y using PMM MA commer rcial solution n and micros stencils char racterized by y thicknesses s
		m and 40 µ µm. (b) Can ntilever fabri icated with o optimal spra ay-coating p parameters an nd by using g
	encil charact terized by a	thickness of f 8 µm. (c)	Scanning el ectron micro oscopy (SEM M) image of f
	microcantile g electron m parameters b ever fabrica microscopy ( by using micr ated with a SEM) image rostencil cha microstenci e of PMMA aracterized by l characteriz A microcantil y a thickness zed by a th lever fabrica of 8 μm, , a length of 640 μm and a width of 14 40 μm. s of 8 µm. hickness of ated with op ptimal spray-40 µm. (d) ) Figure 9 9. Scanning electron mi icroscopy (S SEM) image of SU-8 mi icrocantileve ers. (a) Gene eral view of f -transferr red SU-8 mic crocantilever rs. (b) Zoom m view of SU U-8 microcan ntilevers char racterized by y a thickness s
	ding process Figure 7. Bonded ar characteriza ation rea and bond d strength ve ersus UV exp posure doses	of SU-8 sup pporting and	transferring
	he wafer-bon nding method d, we have a achieved the layer. 4. Concl lusion	large scale	fabrication o of clamped f free-standing g
	cantilevers o es since a fab s method, th sferring layer sure that en crosslinking ature and pr uidic chips [3 bonding temp mperature co 8 crosslinkin process. The ransfer quali et. Figure 7 s on 4-inches w brication yiel he transfer re rs to ensure sures contac g reaction be ressure have 34, 31]. Obta mperature of 1 ould be inter ng level, resu ence, it is pa ity. For this, shows the bo wafers. The t ld of 100% w equires a SU their bondin ct between la tween both s e already be ained results 110 °C is gen resting if the ulting mainly articularly im the exposur onded area a transferring was obtained U-8 crosslink ng. This react ayers, the te surfaces in co een investig recommend nerally used e structural m y from UV-e mportant to in re dose was and the bond technique is A versa atile all-orga anic processi ing method for the lar rge scale fa abrication of suitable for r integration f free-standi ing polymer in industrial l r (figure 4). king surface r tion results f emperature a reaction betw from associat and the SU-8 ween the sup tion of three 8 crosslinkin pporting and e parameters: ng level that microstru ructures was proposed. T This method d results fro m the assoc ciation of tw wo different approaches: : d shadow--masking and d wafer-bon nding. The fi first approach h is coheren nt for the pa atterning of thermo and d : photosen nsitive polym mer materia als. This me ethod consis sts in the sp pray-coating of the stru uctural layer r t through a flexible microstenci l. Thereby, deposition and pattern ning were p performed in n one step. . ontact. gated in prev d a pressure o d [7] that is c material is no exposure dos nvestigate th varied aroun d strength for vious works of 300 kPa to compatible w ot resistant at se, is also a e influence o nd the value r several UV s for the fa to obtain opt with PMMA. t 110 °C [34 critical para of the UV-ex e recommend V-exposure d abrication of imal bonded However, a ]. Moreover, ameter in the xposure dose ded by SU-8 doses applied Moreove er, introducti ion of polym mers in micros stencil fabric cation makes s this process s simple at lo ow-cost. The e f second a approach is p particularly a adapted for i industrial int tegration of o open microst tructure fabr rication such h d as micro ocantilevers, bridges, or m microchannel ls. Indeed, th he process tra ansfer has be een optimize ed in order to o a have a fa fabrication yi ield of 100 % % with a bon nd strength o of 50 MPa. A Also, this me ethod is com mpatible with h , several p patterning m ethods such as photolitho ography and nanoimprint ting. By com mbining both approaches, , e PMMA microcantile evers have b een successf fully obtaine ed. Then, to confirm the versatility o of the wafer--e bonding approach, S SU-8 microc cantilevers w were fabricate ed using stan ndard photol lithography. Preliminary y 8 d Figure 8 8. Scanning electron mi croscopy (SE EM) image of SU-8 tran nsferring an nd transferred d supporting g mechani ical character rization of re esulting PMM MA and SU--8 cantilevers s are in agree ement with t the literature e


Yongxin Liao
email: yongxin.liao@univ-lorraine.fr

Mario Lezoche
email: mario.lezoche@univ-lorraine.fr

Hervé Panetto
email: herve.panetto@univ-lorraine.fr

Nacer Boudjlida
email: nacer.boudjlida@loria.fr

Eduardo Rocha Loures
email: eduardo.loures@pucpr.br

M Lezoche

H Panetto

N Boudjlida

E Rocha Loures

Formal semantic annotations for models interoperability in a PLM environment

Keywords: Semantic Annotation, Product Lifecycle Management, Models, Semantic Interoperability, Formalization

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

In manufacturing enterprises, the Product Lifecycle Management (PLM) approach has been considered as an essential solution for improving the product competitive ability. It aims at providing a shared platform that brings together different enterprise systems at each stage of a Product Life Cycle (PLC) in or across enterprises [START_REF] Ameri | Product lifecycle management: closing the knowledge loops[END_REF]. Although the main software companies are making efforts to create tools for offering a complete and integrated set of systems, most of them have not implemented all of the systems. Finally, they do not provide a coherent integration of the entire information system. This results in a kind of "tower of Babel" managed by many stakeholders in an enterprise, or even in a network of enterprises. The different peculiarities of those stakeholders, who operate on those systems, are then over increasing the issue of interoperability.

The objective of this paper is to deal with the interoperability problems, mainly the issue of semantic interoperability, by proposing a formal semantic annotation method to support the mutual understanding of the semantics of the shared and exchanged models in a PLM environment. The remainder of this paper is organized as follows: Section 2 presents an overview of the research context and discusses the issue of semantic interoperability; Section 3 surveys the related works that made use of semantic annotations to deal with the interoperability issues and identifies the existing drawbacks among those researches. Section 4 presents the semantic annotation formalization proposals, suggestion and verification mechanisms, and a semantic annotation framework; Section 5 presents a case study to demonstrate the applicability and the use of the proposed solution; Section 6 concludes this paper and highlights future research directions.

RESEARCH CONTEXT

The concept of the Product Life Cycle (PLC) has been introduced since the 1950s [START_REF] Rink | Product life cycle research: A literature review[END_REF], and it is a biological metaphor that describes every phase a product goes through, from the first initial requirement until it is retired and disposed of. In the meantime, along with the advent and the evolution of Computer Aided Design (CAD) systems, the problems of locating the required data and losing control of change process associated with these data have gradually appeared. As a solution, Product Data Management (PDM) systems have been developed and introduced for supporting easy, quick and secured access to valid data during the product design phase [START_REF] Ameri | Product lifecycle management: closing the knowledge loops[END_REF]. However, as it is pointed out in [START_REF] Elgueder | Product-process interface for manufacturing data management as a support for DFM and virtual manufacturing[END_REF], the data produced by CAD systems do not cover all the information that is related to the whole product life cycle (from the requirement specification to dismantling information). The PLM solution, proposed during the 1990s, provides support to the processes of capturing, representing, retrieving and reusing both engineering and non-engineering aspects of knowledge along the entire product life cycle. It intends to facilitate the knowledge management in or across enterprises [START_REF] Ameri | Product lifecycle management: closing the knowledge loops[END_REF]. Therefore, the knowledge concerning the product life cycle, which we named PLCrelated knowledge, has become one of the critical concepts in a PLM solution.

Knowledge is an awareness of things that brings to its owner the capability of grasping the meanings (semantics) from the information [START_REF] Ackoff | From Data to Wisdom[END_REF]. In this work, knowledge is considered as a kind of intangible thing, which has to be made perceptible and afterward to be expressed under various kinds of representations. Knowledge representation is the result of embodying the knowledge from its owner's mind into some explicit forms. We consider that all the relevant resources produced by different stakeholders through the variety of enterprise systems are all knowledge representations, such as requirement documents, product design models, control interface designs, process models, data models, observation videos and so on. Therefore, in a PLM environment, these multifaceted forms of knowledge representations act as the carriers of PLC-related knowledge and as the basis for collaboration activities along the product life cycle.

Interoperability serves as a foundational role to support collaboration. In the compilation of IEEE standard computer glossaries (IEEE, 1991), the interoperability is defined as "The ability of two or more systems or components to exchange information and to use the information that has been exchanged". Therefore, the systems need to unambiguously interpret the exchanged information [START_REF] Boudjlida | Annotation of enterprise models for interoperability purposes[END_REF]. [START_REF] Euzenat | Towards a principled approach to semantic interoperability[END_REF] categorized five possible levels of interoperability: encoding level, lexical level, syntactic level, semantic level and semiotic level. Semantic interoperability is the ability to ensure that the exchanged information has got the same meaning considering the point of view of both the sender and the receiver [START_REF] Pokraev | Semantic Service Modeling: Enabling System Interoperability[END_REF]. In the context of PLM, stakeholders with different background have to work together on the exchanged knowledge representations and take decisions based on them. In order to cope with this issue there are two important obstacles that need to be overcome: (1) The implicit semantics that is necessary for understanding a knowledge representation is not be made explicit; (2) The lack of semantics mechanisms to verify the correctness of explicit semantics in the exchanged knowledge representation.

The Ontology knowledge formalization [START_REF] Gruber | A translation approach to portable ontology specifications[END_REF], which is a kind of common agreement on the conceptualization of terms in a specific domain of interest, is usually considered as a possible solution to deal with these two obstacles (Boudjlida et al, 2001). Being a way to realize the semantic enrichment, the application of semantic annotation not only use the formal and shared knowledge that is represented in ontologies to make explicit implicit semantics, but also give the possibility to perform the semantics verification for those knowledge representations that are not initially designed with this ability. In this paper, there are two important aspects of the semantics that are made explicit by a semantic annotation: (1) The domain semantics, which describes the context and the meaning of an annotated element in a specific domain; (2) The structure semantics, which describes the interrelations between the annotated element and the other elements that related to it in a knowledge representation.

Before we proceed to the identification of problems and the proposition of some solutions we need to declare three hypotheses:

(H1) All the knowledge that is needed for the semantic enrichment of models has already been captured, represented and formalized into ontologies.

(H2) The corresponding interconnections among all the used ontologies have already been prepared through certain methods.

(H3) The semantic similarity between two objects can be compared through certain methods.

The support for these hypotheses can be provided by related researches in the corresponding domains. The research community, which are working on knowledge discovery [START_REF] Polanyi | The tacit dimension[END_REF], conversion [START_REF] Nonaka | A dynamic theory of organizational knowledge creation[END_REF], and formalization [START_REF] Gruber | A translation approach to portable ontology specifications[END_REF], can give support to the hypotheses H1. Taking advantages from the researches about ontology matching [START_REF] Maedche | Measuring Similarity between Ontologies[END_REF], mapping [START_REF] Doan | Learning to match ontologies on the Semantic Web[END_REF], and merging [START_REF] Stumme | Ontology merging for federated ontologies on the semantic web[END_REF], hypotheses H2 is possible to be achieved. A number of researchers, such as [START_REF] Patil | Meteor-s web service annotation framework[END_REF], have been committed themselves in the evaluation of semantic similarities. Based on these hypotheses, we focus our research work on proposing a solution to formalize the semantic annotation for the semantic enrichment of models in a PLM environment.

RELATED WORKS

In the face of various needs, different literatures have been proposed to use different ontologies to annotate various kinds of model in diverse ways. Enterprise modelling is a process that tries to capture and represent knowledge from different aspects of a system of interest and for activating the interoperations in or across enterprises. In this research work, we focus our inquiry on a PLM environment where all different types of models along the product life cycle are considered as the targets of semantic enrichments. These models are always created with particular perspectives and expressed in a given modelling notation (or description language). The interoperations among those systems not only require that models can be exchanged and operated on, but also demand an unambiguous understanding of the exchanged models.

From the representation point of view, a model "is often presented as a combination of drawings and text. The text may be in a modeling language or in a natural language" [START_REF] Miller | MDA Guide[END_REF]. The mutual understanding of a model requires not only the understanding of the semantics of "combination of drawing" (structure semantics) but also the semantics of the "text" (domain semantics). Therefore, the ontologies employed by the semantic enrichment need to capture and represent both aspects of knowledge. In this research work, two aspects of ontologies are categorised and can be used to support the semantic enrichment of models in a PLM environment: PLC-related ontologies and Meta-model ontologies. PLC-related ontologies represent the PLC-related knowledge. For example, to mention only a few, SCOR-Full ontology [START_REF] Zdravković | An approach for formalising the supply chain operations[END_REF], and MSDL Ontology [START_REF] Ameri | A web-based framework for semantic supplier discovery for discrete part manufacturing[END_REF]. Meta-model ontologies represent the model constructs knowledge. They can be used to express the structure semantics of annotated elements in a model that are related to the interrelations between their counterpart components in its meta-model. Such as Petri net Ontology [START_REF] Gašević | Petri net ontology[END_REF], BPMN Ontology [START_REF] Ghidini | A formalisation of BPMN in description logics[END_REF] and so on. Because the development of ontologies is not our research focus the MSDL ontology and BPMN ontology are employed to support the semantic enrichment in the validation of our proposition.

With the supports of the ontologies, semantic annotations could be widely used in many contexts. [START_REF] Uren | Semantic annotation for knowledge management: Requirements and a survey of the state of the art[END_REF] reviewed and classified the existing semantic annotation systems as four kinds: manual annotation, automatic annotation, integrated annotation environments, and Ondemand annotation. Task Group 4 of the INTEROP project [START_REF] Boudjlida | A practical experiment on semantic enrichment of enterprise models in a homogeneous environment[END_REF] proposed a general schema the semantic annotation of all enterprise models to enable both semanticbased and model-based interoperability between collaborating actors. Through the main purpose of Semantic Annotation for WSDL and XML Schema (SAWSDL) (W3C, 2005) is for the annotation of Web services, it can also be used to annotate the models that are stored in the format of XML. We found that despite lots of efforts have been made in semantic annotation researches, a number of existing drawbacks still need to be noted:

(1)

The formalization of semantic annotations is not the focus in research [START_REF] Bergamaschi | The Open Source release of the MOMIS Data Integration System[END_REF], where it is only considered as a kind of "is a" association between an annotated object and an ontology concept. Meanwhile, some specific semantic annotation models are proposed by research [START_REF] Attene | Characterization of 3D shape parts for semantic annotation[END_REF], [START_REF] Li | Ontology-Driven Semantic Annotations for Multiple Engineering Viewpoints in Computer Aided Design[END_REF][START_REF] Di Francescomarino | Semantic annotation of business process models[END_REF]. However, these models are difficult to be reused in other researches but the studied ones.

(2)

Making explicit the domain semantics is the only concern in research [START_REF] Bergamaschi | The Open Source release of the MOMIS Data Integration System[END_REF] and [START_REF] Li | Ontology-Driven Semantic Annotations for Multiple Engineering Viewpoints in Computer Aided Design[END_REF], where the structure semantics is ignored. The advantages of making explicit the structure semantics have been acquired by [START_REF] Boudjlida | A practical experiment on semantic enrichment of enterprise models in a homogeneous environment[END_REF], that used it to express modelling construct and support models transformations. In [START_REF] Attene | Characterization of 3D shape parts for semantic annotation[END_REF], it is used to support the automatic computation of relations between features in the model. However, among all these usages, the structure semantics and domain semantics are defined and used separately. There is a lack of research that combines both semantics together in the inference process.

(3)

The verification of the correctness of those semantic annotations normally needs human involved. The research [START_REF] Di Francescomarino | Semantic annotation of business process models[END_REF] is the only one that proposed a mechanism to assist this verification process. However, it only verifies the types of the annotated elements but not the semantics they contain.

After all, based on the investigation of related works, a number of requirements for our proposed solution can be identified: (1) It should provide a general semantic annotation structure model that is able be used to formalize semantic annotations for different kinds of models; (2) It should discover the possibility of using both structure and domain semantics together in the inference process; (3) It should provide some mechanisms to assist the detection of the inconsistencies between semantic annotations and the identification of the conflicts between annotated elements; (4) It should provide a way to guide annotators in how to apply the formal semantic annotations and how to benefit from those semantic annotations; (5) It should provide a framework to support the semantic enrichment of models along the product life cycle. In the next section, the proposed solution that follows these requirements is presented.

PROPOSED SOLUTION

In order to address existing drawbacks and meet the listed requirements, in this section, we propose a formal approach to assist the semantic enrichment of models in a PLM environment. The essential elements of a semantic annotation are not clearly identified in current semantic annotation researches. To better formalize semantic annotations, we first present a meta-model of the semantic annotation, then we will present two kinds of semantic blocks that can be used to support the formal definition of the semantic annotation and the creation of reasoning rules. At the end we will propose the semantic annotation formal definitions.

The knowledge understanding represented by a model needs, not only the domain semantics, embedded in the model contents, but also the structure semantics, embedded in the modelling constructs. In order to define the meta-model of the semantic annotation, several important concepts that are used throughout this section need to be reviewed. Models in a PLM environment are always expressed in some kinds of modelling languages with designer's specific peculiarities. This results in the implicit, or possibly ambiguously explicit, semantics that is not easily intelligible by the humans or the machines. In this research work, all kinds of models throughout a product life cycle are considered as Target Knowledge Representations (TKRs) for the semantic enrichment. Ontology represents a real-world semantics that enables human to use meaningful terminologies as machine processable contents. The meta-model of the semantic annotation is presented in Figure 1, which describes the main components of a semantic annotation and their relationships.

Taking advantages from this meta-model we propose a semantic block delimitation method that will be used as a basis to support the proposition of formal definitions and the creation of reasoning rules.

The concept of "semantic block" is adopted from the research of [START_REF] Yahia | Semantics enactment for interoperability assessment in Enterprise Information Systems[END_REF], in which, it represents a kind of aggregation of semantics. In their research, a semantic block is composed by a minimal number of mandatory concepts that are needed to express the full semantics of an appointed concept. In this work, we extend the semantic block definition to cover the relations among those selected concepts. A semantic block is considered as a shape (segment) of a model that contains a number of selected entities and corresponding relations among them. Two kinds of semantic blocks can be categorized based on their objectives:

1. Semantic Blocks for Semantics Description: the delimitation method supports the creation of a "Domain Semantics" through delimitating one or more "Element of a PLC-related Ontology" from one or more "PLCrelated Ontology". The generated semantic block is used to describe the domain semantics of an "Element of a TKR" based on the semantics that it aggregates.

2. Semantic Blocks for Semantics Substitution: the delimitation method supports the creation of a substitute through delimitating one or more "Element of a TKR" from one "Target Knowledge Representation" based on the "Structure Semantics" that they express. The produced semantic block is used as a substitute of those "Element of a TKR" it aggregates and acts as a new entity or a new relation in the "Target Knowledge Representation".

Let 𝐴 be a set of entities in a model. Let 𝐵 ⊆ 𝐴×𝐴 be a set of binary relations. Given 𝑎 ! , 𝑎 ! ∈ 𝐴, we say that 𝑎 ! is relative to 𝑎 ! through 𝑏 !,! = 𝑎 ! , 𝑎 ! ∈ 𝐵. We call 𝑎 ! the domain of 𝑏 !,! and 𝑎 ! the range of 𝑏 !,! .

Since the relations among entities in ontologies are already explicit, the delimitation of semantic blocks can be applied directly. Let a ! ! ∈ A be a selected entity, which is named as the "main concept", and A ! ! ! ⊆ A be a set of selected entities that are associated to a ! ! . Let B ! ! ! ⊆ B be a set of relations among those selected entities. Mathematically, A ! ! ! is defined as:

𝐴 ! ! ! ,! = {𝑎 ! ! }; 𝐴 ! ! ! ,! = {𝑎 ! ! ∈ 𝐴|∃𝑏 ! ! ,! ! ∈ 𝐵 ! ! ! , 𝑎 ! ! ∈ 𝐴 ! ! ! ,! , 𝑎 ! ! , 𝑎 ! ! = 𝑏 ! ! ,! ! }; 𝐴 ! ! ! ,! = {𝑎 ! ! ∈ 𝐴|∃𝑏 ! ! ,! ! ∈ 𝐵 ! ! ! , 𝑎 ! ! ∈ 𝐴 ! ! ! ,! , 𝑎 ! ! , 𝑎 ! ! = 𝑏 ! ! ,! ! }; … 𝐴 ! ! ! ,! = {𝑎 ! ! ∈ 𝐴|∃𝑏 ! !!! ,! ! ∈ 𝐵 ! ! ! , 𝑎 ! !!! ∈ 𝐴 ! ! ! ,!!! , 𝑎 ! !!! , 𝑎 ! ! = 𝑏 ! !!! ,! ! }; 𝐴 ! ! ! : = 𝐴 ! ! ! ,! !
According to user define selection methods that are applied during the creation of this kind of semantic blocks, an appropriate subset of the 𝐴 can be determined. Then the semantic block of the entity 𝑎 ! ! is defined as a pair:

𝑆𝐵 ! ! ! : = (𝐴 ! ! ! , 𝐵 ! ! ! ),
where every entity in 𝐴 ! ! ! can be attained by 𝑎 ! ! through, at least, one path and all the relations in the paths are contained in 𝐵 ! ! ! .

The (a) in the Figure 2 depicts a part of an ontology that contains explicit relations and the (b) in the Figure 2 shows the semantic block 𝑆𝐵 ! !!"" of the main concept 𝑎 !!"" , which can be used to describe the domain semantics of the element it annotates. Taking advantage from this kind of semantic blocks, the annotators can, with a certain degree of freedom, delimitate an appropriate semantics that they needed in the OKRs. Due to the relations among the entities are implicit in enterprise models, the delimitation cannot be applied directly.

As shown in the Figure 3, two processes are proposed as follows:

(1) Relation Explicitation.

There are two general rules in the relation explicitation process:

Every model element is represented as an entity of the set 𝐴.

A relation 𝑏 !,! = (𝑎 ! , 𝑎 ! ) ∈ 𝐵 is created between 𝑎 ! ∈ 𝐴 and 𝑎 ! ∈ 𝐴, when the model element that is represented by 𝑎 ! is related to the model element that is represented by 𝑎 ! .

(2) Semantic Block Delimitation

This kind of semantic blocks can be further divided into two categories depending on the role it acts: as an entity or as a relation. In this paper, we only use and introduce the latter category. The restrictions for the delimitation are generated as follows:

A semantic block that acts as a new relation between 𝑎 ! ∈ 𝐴 and 𝑎 ! ∈ 𝐴. Let 𝐴 ! ! ,! ! ⊆ 𝐴 be a set of selected entities and let 𝐵 ! ! ,! ! ⊆ 𝐵 be a set of relations among 𝑎 ! , 𝑎 ! and the entities in 𝐴 ! ! ,! ! . In order to substitute the semantics of its contents, it needs to satisfy the following three conditions:

𝐴 ! ! ,! ! does not contain 𝑎 ! and 𝑎 ! . That is 𝑎 ! , 𝑎 ! ∉ 𝐴 ! ! ,! ! .
For every entity 𝑎 ! in the 𝐴 ! ! ,! ! , at least one entity

𝑎 ! exists in 𝐴 ! ! ,! ! that has a relation 𝑏 !,! in 𝐵 ! ! ,! ! to 𝑎 ! . That is ∀𝑎 ! ∈ 𝐴 ! ! ,! ! , ∃𝑎 ! ∈ 𝐴 ! ! ,! ! , ∃𝑏 !,! ∈ 𝐵 ! ! ,! ! , 𝑠. 𝑡. 𝑎 ! , 𝑎 ! = 𝑏 !,! .
Beside 𝑎 ! and 𝑎 ! , for every binary relation 𝑏 ! ! ,! ! in the 𝐵 ! ! ,! ! , the entities that appear in the domain and range of 𝑏 ! ! ,! ! are the entities in the 𝐴 ! ! ,! ! . That is

∀𝑏 ! ! ,! ! ∈ 𝐵 ! ! ,! ! , 𝑎 ! ! , 𝑎 ! ! = 𝑏 ! ! ,! ! ⇒ 𝑎 ! ! , 𝑎 ! ! ∈ 𝐴 ! ! ,! ! ∪ 𝑎 ! ∪ 𝑎 !
Then the semantic block 𝑆𝐵𝑅 ! ! ,! ! is defined as a pair:

𝑆𝐵𝑅 ! ! ,! ! : = (𝐴 ! ! ,! ! , 𝐵 ! ! ,! ! )
The (a) in the Figure 3 shows a part of a process model that contains implicit relations. The (b) in the Figure 3 shows the represented entities and explicit relations. The (c) in the Figure 3 shows the semantic block 𝑆𝐵𝑅 ! ! ,! ! that merges the semantics of its contents and acts as a new relation between 𝑎 ! and 𝑎 ! . Taking advantages from this kind of semantic block, a combination of elements in the TKR can be delimitated and act new entities relations the creation of rules.

Example the Semantic Block for Semantics Substitution

Based on the formal definitions of semantic annotation that are proposed in [START_REF] Liao | Semantic Enrichment of Models to Assist Knowledge Management in a PLM environment[END_REF] an improved version is presented in this section. Let 𝐸 be the set of elements in a TKR and 𝑒 ! be one of the elements in 𝐸.

Definition 1. An ontology is a formal and shared understanding of some domains of interest, which specifies the concepts and the relationships that can exist for an agent or a community of agents [START_REF] Gruber | A translation approach to portable ontology specifications[END_REF]. Let 𝑜 ! represent an ontology, which is formalized by a triple:

𝑜 ! ≔ (C ! ! , R ! ! , A ! ! ),
where

C ! ! is a set of concepts; R ! ! is a set of relationships; A ! ! is a set of axioms.
Let 𝑜𝑎𝑙𝑙 ! ! be the set that contains all the elements from the set 𝐶 ! ! and 𝑅 ! ! . An ontology element 𝑜𝑒 ! ! ! is represented as:

𝑜𝑎𝑙𝑙 ! ! = {𝑜𝑒 ! ! ! |𝑜𝑒 ! ! ! ∈ 𝐶 ! ! ∪ 𝑅 ! ! }.
Definition 2. A meta-model is a model that specifies the concepts, relationships and rules to model a model. Let 𝑚𝑚 ! denote a meta-model, which is defined as a triple:

mm ! ≔ (C !! ! , R !! ! , RU !! ! ),
where 𝐶 !! ! is a set of concepts; 𝑅 !! ! is a set of relationships; 𝑅𝑈 !! ! is a set of rules.

Let 𝑚𝑚𝑜 ! be an ontology that represents the meta-model mm ! , which is defined as:

𝑚𝑚𝑜 ! ≔ (𝐶 !!" ! , 𝑅 !!" ! , 𝐴 !!" ! ).
Definition 3. The domain semantics of a TKR is made explicit by one or more PLC-related ontologies. Let 𝑃𝑂 be the set of PLC-related ontologies and 𝑃 be the set of selected ontology element sets from the powerset of all ontology elements of 𝑃𝑂, which is defined as:

𝑜𝑎𝑙𝑙 ! ! ! ! ∈!" = {𝑜𝑒 ! ! ! |(∃𝑜 ! )(𝑜 ! ∈ 𝑃𝑂⋀𝑜𝑒 ! ! ! ∈ 𝑜𝑎𝑙𝑙 ! ! )}, 𝑃 ⊆ 𝒫 𝑜𝑎𝑙𝑙 ! ! ! ! ∈!
.

Definition 4. The structure semantics of a TKR is made explicit by a meta-model ontology 𝑚𝑚𝑜 ! . Let MME be the set that contains all the elements from the set 𝐶 !!" ! . An ontology element 𝑚𝑚𝑒 ! is defined as:

𝑀𝑀𝐸: = {𝑚𝑚𝑒 ! |𝑚𝑚𝑒 ! ∈ 𝐶 !!" ! }.
𝑠𝑟 ⊂ : = 𝑒 ! , 𝑝 ! 𝑠𝑒𝑚 𝑒 ! 𝑖𝑠 𝑙𝑒𝑠𝑠 𝑔𝑒𝑛𝑒𝑟𝑎𝑙 𝑡ℎ𝑎𝑛 𝑠𝑒𝑚(𝑝 ! )}; 𝑠𝑟 ∩ : = ! , 𝑝 ! 𝑒 ! 𝑎𝑛𝑑 𝑝 ! ℎ𝑎𝑣𝑒 𝑐𝑜𝑚𝑚𝑜𝑛 𝑠𝑒𝑚𝑎𝑛𝑡𝑖𝑐𝑠, 𝑒 ! , 𝑝 ! ∉ 𝑠𝑟 ∼ ⋃𝑠𝑟 ⊃ ⋃𝑠𝑟 ⊂ }; 𝑠𝑟 ! : = 𝑒 ! , 𝑝 ! 𝑒 ! 𝑎𝑛𝑑 𝑝 ! ℎ𝑎𝑣𝑒 𝑛𝑜𝑡 𝑐𝑜𝑚𝑚𝑜𝑛 𝑠𝑒𝑚𝑎𝑛𝑡𝑖𝑐𝑠}.
Definition 7. 𝑀𝑅 !,!!" is a set of binary relations that describe the semantic relations from 𝐸 to 𝑀𝑀𝐸. Given 𝑒 ! ∈ 𝐸 and 𝑚𝑚𝑒 ! ∈ 𝑀𝑀𝐸, one subset of 𝑀𝑅 !,!!" is defined as follow:

𝑚𝑟 !" : = 𝑒 ! , 𝑚𝑚𝑒 ! 𝑒 ! 𝑖𝑠 𝑎𝑛 𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒 𝑜𝑓 𝑚𝑚𝑒 ! }.
Finally, with all above-mentioned definitions, we are now ready to formally define the semantic annotation.

Definition 8. Let TKR, 𝑃𝑂 and 𝑚𝑚𝑜 ! be given, the semantic annotation 𝑆𝐴 that is associated to them is defined by a 5tuple:

𝑆𝐴 ≔ (𝐸, 𝑃, 𝑆𝑅, 𝑀𝑀𝐸, 𝑀𝑅), where 𝐸 is a set of elements from a TKR;

𝑃 is a set of selected ontology element sets from a set of PLC-related ontologies 𝑃𝑂, which makes explicit the domain semantics aspect of 𝐸;

𝑀𝑀𝐸 is a set of ontology elements from a meta-model ontology 𝑚𝑚𝑜 ! , which makes explicit the structure semantics aspect of 𝐸;

𝑆𝑅 ≔ 𝑆𝑅 !,! ; 𝑀𝑅 ≔ 𝑀𝑅 !,!!" .
These formal definitions not only can be used to construct a semantic annotation schema, but also can be used as the basis for the creation of reasoning mechanisms.

In this work, the formal semantic annotations are mainly contributing in two main aspects: for assisting the creation of models and for supporting the identification of possible mistakes. Therefore, three main stages with their corresponding mechanisms are proposed for achieving these two purposes: (1) the suggestion of semantic annotations; (2) the inconsistency detection between semantic annotations; and (3) the conflict identification between annotated objects in a model.

The essence of an inconsistency is the contradictory among two or more facts that describe one common object. With the same principle, the inconsistency detection between semantic annotations is based on the comparison of two or more semantic annotations that describe the semantics of the same "Element of a TKR". Therefore, to cope with this premise, two types of semantic annotations are classified: Initial Semantic Annotations, which are directly annotated on an "Element of a TKR" by an annotator; Inferred Semantic Annotations, which are annotate "Element of TKR" action is based on its related element's semantic annotations and reasoning rules. Both "Structure Semantics" and "Domain Semantics" are contributing in the annotation suggestion stage. The "Structure Semantics" is used to make explicit the implicit relations between the annotated "Element of a TKR" and its related elements. The "Domain Semantics" is used as the basis for the annotation suggestion. Two remarks need to be pointed out: (1) only the semantic relationship 𝑠𝑟 ⊂ and 𝑠𝑟 ! can produce suggestions; (2) the semantic blocks that are nested within each other are not taken into account.

The detection of inconsistencies can be performed on the annotated element that has two or more semantic annotations. Using the case of inconsistency detection between two semantic annotations as the basis, let 𝑒 ! be annotated by 𝑠𝑎 ! and 𝑠𝑎 ! , in which, 𝑝 ! and 𝑝 ! are used to make explicit the domain semantics of 𝑒 ! . The semantic similarity comparison results between 𝑝 ! and 𝑝 ! is defined.

Definition 9. 𝑃𝑅 is a binary relation that describes the semantic relationships from 𝑃 to 𝑃. Given 𝑝 ! , 𝑝 ! ∈ 𝑃, and let 𝑠𝑒𝑚(𝑝 ! ) represent the semantics of 𝑝 ! and 𝑠𝑒𝑚(𝑝 ! ) represent the semantics of 𝑝 ! , five subsets of 𝑃𝑅 are defined as follows:

𝑝𝑟 ∼ : = 𝑝 ! , 𝑝 ! 𝑠𝑒𝑚(𝑝 ! ) 𝑎𝑛𝑑 𝑠𝑒𝑚(𝑝 ! ) 𝑎𝑟𝑒 𝑒𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡}; 𝑝𝑟 ⊃ : = 𝑝 ! , 𝑝 ! 𝑠𝑒𝑚 𝑝 ! 𝑖𝑠 𝑚𝑜𝑟𝑒 𝑔𝑒𝑛𝑒𝑟𝑎𝑙 𝑡ℎ𝑎𝑛 𝑠𝑒𝑚(𝑝 ! )}; 𝑝𝑟 ⊂ : = 𝑝 ! , 𝑝 ! 𝑠𝑒𝑚 𝑝 ! 𝑖𝑠 𝑙𝑒𝑠𝑠 𝑔𝑒𝑛𝑒𝑟𝑎𝑙 𝑡ℎ𝑎𝑛 𝑠𝑒𝑚(𝑝 ! )}; 𝑝𝑟 ∩ : = 𝑝 ! 𝑝 ! ! 𝑝 ! , 𝑝 ∉ 𝑝𝑟 ∼ ⋃𝑝𝑟 ⊃ ⋃𝑝𝑟 ⊂ }; 𝑝𝑟 ! : = 𝑝 ! , 𝑝 ! 𝑝 ! 𝑎𝑛𝑑 𝑝 !
As shown in the Table 1, according to the similarity comparison between two domain semantics of a common annotated element, three types of results can be identified as follows: result (a) expresses that 𝑠𝑎 ! and 𝑠𝑎 ! are with each other; result (b) expresses that 𝑠𝑎 ! and 𝑠𝑎 ! are possible consistent with each other; result (c) expresses that there is an inconsistency between 𝑠𝑎 ! and 𝑠𝑎 ! . To be more succinct, we use the concept "Others" to replace the rest of the semantic relationships in the 𝑃𝑅 besides the one or several that are shown in a grid of the table. The inconsistency detection results not only point out the inconsistencies (or possible inconsistencies) between two (or more) semantic annotations, but also can be used to identify the possible conflicts between those annotated elements in a TKR. Using the case of conflict identification between two annotated elements in a TKR as the basis, let 𝑒 ! , 𝑒 ! and 𝑒 ! be three elements in a TKR and there is an inconsistency between 𝑠𝑎 ! and 𝑠𝑎 ! that are both used to annotate 𝑒 ! . As shown in the Table 2, the possible conflicts between two annotated elements in the TKR can be identified. In order to apply the above-mentioned semantic annotation proposal in a PLM environment we propose a semantic annotation framework that capture, represent and manage the knowledge related to the system of interest through the semantic annotation. An overview of the procedure to apply semantic annotations is presented in the Figure 4. This workflow is divided into three phases as follows:

The Preparation Phase. During this phase, all the elements that are needed by both the annotation phase and the reasoning phase are prepared: (1) Creation of a TKR by a modelling system. The set of elements 𝐸 in this TKR are the output of this process; (2) Collection and Formalization of OKRs, in which, the ontologies are captured and formalized. The output of this process is a number of PLC-related ontologies (𝑃𝑂) and a meta-model ontology (𝑚𝑚𝑜 ! ); (3) Customization of the SA Solution, in which, the formal definitions of semantic annotations and the reasoning mechanisms are used as the foundation to customize a semantic annotation schema and corresponding reasoning rules. The former one is used as a repository to conserve the semantic annotations; the letter one is used to support the inference process in the reasoning phase.

The Annotation Phase. During this phase, a number of semantic annotations are generated for supporting the reasoning phase: (1) Explicitation of Structure Semantics, in which, the structure semantics of a TKR, namely the interrelations between the model elements, are made explicit.

(2) Explicitation of Domain Semantics, in which, the of a TKR, namely the of model contents in a domain of interest, are made explicit.

The Reasoning Phase. During this phase, the reasoning is performed based on the outputs of the above-mentioned two phases: (1) Configuration of Reasoning Parameters, in which, based on the semantic annotation schema and the practical situations of different TKRs, the operations that support the configuration of reasoning parameters are performed. (2) Reasoning on Semantic Annotations, in which, the reasoning is performed based on the semantic annotations, the parameters and the reasoning rules to produce inference results.

The semantic annotation procedure describes the application of the semantic enrichment solution within one single TKR. So to deal with the multiple TKRs in a PLM environment we propose a semantic annotation framework. As shown in the Figure 5, on the left side, there are a series of processes that describe a linear product life cycle, which represent the TKR Creation and Management module. On the right side, there are four main modules of this framework: the OKR Creation and Management module, the Knowledge Cloud module the Semantic Annotation and Processing Agent (SAPA) module and the Reasoning Engine module. Between the Reasoning Engine and the modelling Systems: It submits the inference requests from the stakeholders to the Reasoning Engine for performing the reasoning actions and then provides the corresponding results as feedbacks.

CASE STUDY

Based on the formalization of semantic annotations, a prototype annotation tool, SAP-KM (Semantic Annotation Plugin for Knowledge Management), has been developed to assist the demonstration of the proposed solution. This section first introduces the context of case study and then the application of formal semantic annotations in the chosen application scenario is presented So as to show how formal semantic annotations can contribute to the semantic interoperability in a PLM environment, the life cycle of an educational combination product that is produced in a local technical production centre, named AIPL1 , has being chosen as the context of this case study. The Figure 7 shows the components of this product, which are designed to be assembled and disassembled easily. The requirements of this product are coming from the needs of reusability of the educational materials. The mechanical engineers at AIPL conceived and designed the educational combination product using the CATIA 2 Computer-Aided Design software (we name it as CATIA in the remaining paper), which generates the product technical information into a so-called Engineering Bill of Material (EBOM). However, the information in the EBOM represents the product structure from the designer's point of view, which does not contain all the information that is needed by the systems at the production stage. For this reason, a Bill of Process (BOP) needs to be combined together with EBOM. These processes are defined and modelled using the MEGA modelling environment (we name it as MEGA in the remaining paper). The Figure 8 gives a brief overview of the manufacturing processes of this product: (1) Bases turning process, which is in charge of chipping an aluminium bar into a number of designed bases. (2) Discs cutting process, which is in charge of cutting galvanized plates and magnetic plates into a number of designed discs. (3) Parts sticking process, which is in charge of using glues to stick the galvanized or magnetic discs to the corresponding bases for producing four kinds of designed parts (on the right hand side of the Figure 7). (4) Products assembling process, which is in charge of assembling different kinds of parts into six kinds of the designed products (on the left hand side of the Figure 7). The EBOM and the BOP are used as basis to support the parameterization of the enterprise systems in production stage. For example, the Sage X3 ERP system 3 (we name it as Sage X3 in the remaining paper), which takes customer orders as inputs and generates work orders for supporting the purchasing of raw materials, the outsourcing of some processes and the manufacturing of the components and the related products. At the end, after some quality examination, all the qualified products are packed in boxes and dispatched to the production engineering teaching group. The information in the product life cycle is not just simply passed from one system to another in a linear unique direction. In order to differentiate the systems that are used in a selected information flow, we define three kinds of systems: the current system, which is used in a specific point of the selected information flow; the upstream system, based on the selected information flow, which is the system that is placed before the current system; the downstream system, based on the selected information flow, it is the system that is placed after the current system. So as to determine a clear-cut information flow and to show the interoperation between those systems, we choose MEGA as the current system, together with its upstream system (CATIA) and downstream system (Sage X3) as the application scenario. As shown in the Figure 9, the process model at the bottom shows all the processes that in the product lifecycle of this product. CATIA, MEGA and Sage X3 are one of the system that are used in corresponding processes respectively, which represent the TKR creation and management module in the Semantic Annotation Framework. For the other part of the framework, the Protégé is used as the OKR Creation and Management module, the Microsoft windows folder system is used as the Knowledge Cloud module, the SAP-KM is the Semantic Annotation and Processing Agency, and the Jena Reasoner employed as the Reasoning Engine Module. In the current version of SAP-KM, there are two developed interfaces. One is between MEGA and SAP-KM to assist the annotation on the model diagram, and the other one is between the Jena Reasoner and SAP-KM to support the inference process. In order to avoid the unnecessary repetition with several research literatures ( [START_REF] Attene | Characterization of 3D shape parts for semantic annotation[END_REF], [START_REF] Li | Ontology-Driven Semantic Annotations for Multiple Engineering Viewpoints in Computer Aided Design[END_REF], and [START_REF] Bergamaschi | The Open Source release of the MOMIS Data Integration System[END_REF]) which already showed the possibility of developing an annotation plug-in for product design models and data models we didn't developed the interface between CATIA/Sage X3 and SAP-KM. In the case study, we assume that the corresponding plug-ins for these two systems exists.

Based on the semantic annotation procedure, the application of the formal semantic annotation is divided into three phases: the preparation phase, the annotation phase and the reasoning phase.

Concerning the TKR part, we take into account two models: the product design model created by CATIA, which is considered as the model from upstream system that is already been annotated; and the process model created by MEGA, which is considered as the model that needs semantic enrichment.

To be more specific, the process model in the Concerning the OKR part, two domain level ontologies (the MSDL ontology [START_REF] Ameri | A web-based framework for semantic supplier discovery for discrete part manufacturing[END_REF] and the BPMN ontology [START_REF] Ghidini | A formalisation of BPMN in description logics[END_REF]) are employed. Based on them, one top level ontology (the general ontology) and two application level ontologies (the AIPL product ontology and the MEGA BPMN ontology) are created to fulfil the needs of annotation from different levels. As shown in the Figure 11, the contents in black colour are the extracted parts of these five ontologies. A number of pre-processes are carried out on these five ontologies. As shown in the Figure 11, the contents in green colour show some results of the pre-processes. To be more specific, these pre-processes are used to: Add additional relationships: a set of additional relationships is added between the concepts in different ontologies. For example, the Object Property "hasShape" is added from the Individual "P0110" to the Individual "Cylinder".

Complete the top-level hierarchy: a set of "subClassOf" is added from the top-level classes to the Class "Thing", which are omitted by Protégé. This action is used to support the ontology loading in the Jena Reasoner.

Enrich the semantics of existing ontologies: two aspects of the semantics are formalized and added into the PLC-related ontologies (in both domain level and application level): (1) the semantics of a concept that is embedded in a general context is selected from the WordNet4 service; (2) the semantics of a concept that is embedded in a specific context acquired from the special environment in the AIPL.

Store the ontologies: these five ontologies are stored in RDF/XML format to facilitate the ontology loading in the Jena Reasoner.

These five ontologies together the pre-process results are stored in the Knowledge Cloud. They have their own namespaces, which are different from each other. To ease the reading, the namespaces are omitted in the figure. Based on the formal definitions of semantic annotations, a schema is designed to store the annotation results. In order to use the existing reasoning engines to assist the annotation and reasoning processes, this schema is structured as an ontology, named Semantic Annotation Schema. It uses appropriate Classes, Properties and Individuals to represent the five main elements of the Semantic Annotation and some additional Properties to assist the creation of reasoning rules. Once the preparation of OKRs, TKRs and the Semantic Annotation Schema is finished, the annotation process can be performed.

The semantic annotations that participate in the case study are divided into two parts: the received semantic annotations from the upstream system and the created semantic annotations in the current system. In order to avoid the massive details for each semantic annotation and also to ease the explanation and reading, we represent a semantic annotation in the syntax of "namespace; local name of an ontology element". The "namespace" represents the namespace of an ontology. The abbreviation namespace for General Ontology, MSDL Ontology, BPMN Ontology, AIPL Product Ontology, MEGA BPMN Ontology and Semantic Annotation Schema are respectively &GO, &MSDL, &BPMN, &AIPL, &MEGA and &SANS. The "local name" can be the local name of a class, an individual or a property.

The semantic annotations from the upstream system are imported through the SAP-KM to assist the model creation and verification in MEGA. Table 3 shows a list of model elements from the product design model with their corresponding domain semantics. In order to demonstrate the applicability of proposed solution three main operations in the manufacturing process of Prod3 ("Bases Turning", "Parts Sticking" and "Prods Assembling") together with their inputs and outputs are selected as the candidates for semantic enrichment. Concerning the explicitation of the structure semantics the internal relationships between the selected model elements are made explicit through using BPMN Ontology and MEGA BPMN Ontology. Concerning the explicitation of the domain semantics, the SAP-KM provides two possibilities: (1) to reuse the domain semantics of the imported semantic annotations through its Elements Matching function, and (2) to create new domain semantics for the selected model elements. So as to reuse existing semantic annotations the matching between the annotated elements in the former model and the selected elements in the current model need to be implemented. After the matching process, the matched elements in the product design model have their domain semantics related to their corresponding matched elements in the process model. On the other hand the domain semantics of the selected model elements are made explicit through using General Ontology, MSDL Ontology and AIPL Product Ontology. All the semantic annotations are stored in the Semantic Annotation Schema, which are used as one of the basis for the reasoning phase.

In the case study, the reasoning phase is mainly in charge of (1) suggesting inferred semantic annotations; (2) detecting some inconsistencies between several semantic annotations of an annotated model element; and (3) identifying the possible mistakes, namely conflicts, among annotated model elements.

As shown in the Figure 11, three semantic block delimitation rules are used in the case study to make explicit the internal relationships among the annotated elements in the process model. The rule "Operation_to_DataObject" and the rule "DataObject_ to_Operation" are used to create the semantic blocks that supersede the semantics between an operation and the data objects that are related to it. The rule "Operation1 _to_Operation2" is used to create the semantic blocks that substitutes the semantics between two operations, which are connected by a sequence flow. These rules only show three possible situations between two appointed types of model elements. However they are enough for supporting the SBR delimitation in the case study. After the semantic block delimitation, these three object propeties are added between the corresponding ontology elements in the Semantic Annotation Schema. The property association process is performed between the properties, which are made explicit in the semantic block delimitation process, and the properties in the PLC-related ontologies. Based on the inferred semantic annotations suggestion algorithm corresponding inferred semantic annotations are suggested. After the suggestion, the comparison of the similarity between two domain semantics of a common annotated model element can be performed. SAP-KM queries all the individuals that have both initial and inferred semantic annotations in the Class "&SANS;E" and it generates all the possible comparison pairs between an initial one to an inferred one. The similarity comparison results and the inconsistency detection rules are used as inputs of the reasoning engine to produce the inconsistency detection results. The inconsistency detection results are used as inputs of the model conflict identification rules and algorithms. The possible conflicts between annotated model elements are used to draw attention of modellers for examining the correctness of two annotated elements in the process model. Ideally, the model content conflicts identification results are supposed to contain the reason why two model elements have conflicts and how to solve these possible mistakes. However, these kinds of suggestions highly rely on the power of the reasoning engine. The current reasoning engines are only able to deal with some simple reasoning, such as classification (class subsumption and individual memberships) and class consistency (whether a class can have individuals or not), but they cannot deal with sub-ontologies.

The process model and the created semantic annotations are sent to Sage X3 to assist the parameterization. Let us take the table of "process planning" in Sage X3 as an example. The "process", "work centre", "preparation time" and "execution time" are the four of its main elements in the parameterization. Concerning the operation "Bases Turning" (𝑒 ! ), "Parts Sticking"(𝑒 !" ) and "Product Assembling" (𝑒 !" ), the corresponding that are need by Sage X3 are contained in their the semantic annotations 𝑝 !" , 𝑝 !" and 𝑝 !" respectively. Once the semantic annotations are created in the Sage X3 data model, the corresponding elements matching in the Sage X3 plug-in is able to assist the stakeholder to fill the right data into the right fields of the "process planning" table. This case study shows how the formal semantic annotations are contributing in: (1) acquiring the initial semantics that the stakeholders, who manipulate the upstream system, wanted to express; (2) verifying, semi-automatically, the semantic consistency between the contents in the received models and in the developing models; and (3) guaranteeing the correctness of the embedded semantics in the under development models for the stakeholders, who manipulate the downstream system.

CONCLUSION

The proposed solution in this paper provides some fundamental contributions, which are remarked as follows:

(1) A semantic annotation meta-model that unambiguously describes the major components of a semantic annotation and their interrelations; (2) The definition of the Semantic blocks for the semantics description and substitution; (3) Formal definitions of the semantic annotation; (4) Three reasoning mechanisms that show and validate the usages of semantic enrichments; (5) A semantic annotation procedure; (6) A semantic annotation framework. Concerning the hypothesis one, although the research focus of this work is not in capturing, representing and formalizing knowledge into ontologies, the richness of OKRs in the Knowledge Cloud influences the precision of the semantic annotations. Concerning the hypothesis two, the interconnections among ontologies are the fundamental of using multiple ontologies together to perform semantic enrichment and perform inference on semantic annotations. Reasoning engines are not able to perform reasoning on concepts coming from different ontologies which have not relationships (directly or indirectly) between each other. Concerning the hypothesis three, the semantic similarities between two domain semantics are used as the basis to support the inconsistency detection. The more precise semantic similarities are the more precise results can be produced. From the practical point of view, the prototype implementation and validation shows the possibility of using the formalization of semantic annotations for system interoperability in a PLM environment. Furthermore, in the context of a PLM environment, three interesting directions can also be considered as future works. (1) To enable the traceability of requirements. With the assistance of semantics annotation, it is possible to trace the validation of each requirement in every stage of the product lifecycle, from the initial design until the final deposit of. (2) To make explicit the relationships between the TKRs. (3) To address the versioning of models. The issue about the versioning of models in a PLC is difficult to be avoided. Semantically enriching models gives the possibility to ensure that the modified model contents is not semantically in conflict with existing ones.

In a nutshell, the purpose of this work is to deal with the issue of semantic interoperability. Despite some limitations, as discussed in this section, we are convinced that the proposed formalization of semantic annotations is able to support and guarantee the models interoperability in a PLM environment.

Figure 1 .

 1 Figure 1. The Meta-model of the Semantic Annotation In this research work, two kinds of ontologies (PLC-related and Meta-model ontologies) are considered as Ontologybased Knowledge Representations (OKRs) to support the semantic enrichment of models. The Semantic Annotation is acting as a bridge to formally describe the semantic relationships between TKRs and OKRs. In this research work, two aspects of semantics (domain and structure semantics) are made explicit through the semantic enrichment.

Figure 2 .

 2 Figure 2. An Example of the Semantic Block for Semantics Description

Definition 5 .

 5 Let 𝐴 and 𝐵 be two sets, any subset of 𝑏𝑟 ⊆ 𝐴×𝐵 is a binary relation from A to B. Given 𝑎 ∈ 𝐴 and 𝑏 ∈ 𝐵, the 𝑏𝑟 in the notation 𝑎 𝑏𝑟 𝑏 is defined as, 𝑏𝑟: = 𝑎, 𝑏 𝑎 𝑖𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛 𝑏𝑟 𝑤𝑖𝑡ℎ 𝑏}. Let 𝑑𝑜𝑚(𝑏𝑟) represent the domain of the 𝑏𝑟 and 𝑟𝑎𝑛(𝑏𝑟) represent the range of the 𝑏𝑟, which are defined as 𝑑𝑜𝑚 𝑏𝑟 : = 𝑎 ∈ 𝐴 ∃𝑏 ∈ 𝐵, (𝑎, 𝑏) ∈ 𝑏𝑟}, 𝑟𝑎𝑛 𝑏𝑟 : = 𝑏 ∈ 𝐵 ∃𝑎 ∈ 𝐴, (𝑎, 𝑏) ∈ 𝑏𝑟}. Definition 6. 𝑆𝑅 !,! is a set of binary relations that describe the semantic relationships from 𝐸 to 𝑃. Given, 𝑒 ! ∈ 𝐸 and 𝑝 ! ∈ 𝑃, and let 𝑠𝑒𝑚(𝑒 ! ) represent the semantics of 𝑒 ! and 𝑠𝑒𝑚(𝑝 ! ) represent the semantics of 𝑝 ! , five subsets of the 𝑆𝑅 !,! are defined as follows: 𝑠𝑟 ∼ : = 𝑒 ! , 𝑝 ! 𝑠𝑒𝑚(𝑒 ! ) 𝑎𝑛𝑑 𝑠𝑒𝑚(𝑝 ! ) 𝑎𝑟𝑒 𝑒𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡}; 𝑠𝑟 ⊃ : = 𝑒 ! , 𝑝 ! 𝑠𝑒𝑚 𝑒 ! 𝑖𝑠 𝑚𝑜𝑟𝑒 𝑔𝑒𝑛𝑒𝑟𝑎𝑙 𝑡ℎ𝑎𝑛 𝑠𝑒𝑚(𝑝 ! )};

Figure 4 .

 4 Figure 4. The General Semantic Annotation Procedure.

Figure 5 .

 5 Figure 5. The Semantic Annotation Framework in a PLM Environment.The TKR Creation and Management module is composed by a number of enterprise systems. Stakeholders in or across enterprises, during a product lifecycle use those systems to create and manage TKRs. Those systems need to provide sufficient APIs to enable the communications between themselves and the SAPA module.The OKR Creation and Management module is in charge of capturing, formalizing and managing PLC-related knowledge and model constructs knowledge into a knowledge base, namely, Knowledge Cloud. The OKRs are supposed to be in

Figure 7 .

 7 Figure 7. The Overview of the Educational Combination Product in the AIPL

Figure 8 .

 8 Figure 8. The Main Manufacturing Processes of the Combination Product

Figure 9

 9 Figure 9 The Application Scenario of the Case Study

  Figure 10 contains five main participants: (1) The application participant, Sage X3, which produces different kinds of orders for other participants and collects the corresponding feedbacks; (2) The warehouse, which is in charge of delivering raw materials to the work centre US (Aluminium Bars) and to the work centre CO (Galvanized Discs and Magnetic Discs). It also stores the finished component (Prod3); (3) the work centre US, which is in charge of the bases turning operation. It takes the aluminium bars as inputs and it produces two kinds of bases (P0110 and P0960); (4) the work centre CO that is in charge of the parts sticking operation. It takes the outputs of the previous operation and the raw materials from the warehouse to produce two kinds of parts (the PAL01 and the PAL60); (5) the work centre AS that is in charge of prod assembling operation. It takes the outputs from the sticking operation to produce the component (Prod3). At the end, this component is sent to the warehouse.

Figure 10 .

 10 Figure 10. The Process Model from the MEGA

Figure 11

 11 Figure 11 Three Rules to define a SBR for making explicit the Relations

Table 1 .

 1 The Possible Results of the Inconsistency Detection

Table 2 .

 2 The Possible Results of Conflict Identification

Table 3 .

 3 The Domain Semantics of the Annotated Elements

AIPL (Atelier Inter-Etablissements de Lorraine): http://www.aip-primeca.net/

Sage X3 http://www.sage.com/

WordNet http://wordnet.princeton.edu/