Synchronous communication in PLM environments using annotated CAD models

The connection of resources, data, and knowledge through communication technology plays a vital role in current collaborative design methodologies and Product Lifecycle Management (PLM) systems, as these elements act as channels for information and meaning. Despite significant advances in the area of PLM, most communication tools are used as separate services that are disconnected from existing development environments. Consequently, during a communication session, the specific elements being discussed are usually not linked to the context of the discussion, which may result in important information getting lost or becoming difficult to access. In this paper, we present a method to add synchronous communication functionality to a PLM system based on annotated information embedded in the CAD model. This approach provides users a communication channel that is built directly into the CAD interface and is valuable when individuals need to be contacted regarding the annotated aspects of a CAD model. We present the architecture of a new system and its integration with existing PLM systems, and describe the implementation details of an annotation-based video conferencing module for a commercial CAD application.This work was supported by the Spanish Ministry of Economy and Competitiveness and the FEDER Funds, through the ANNOTA project (Ref. TIN2013-46036-C3-1-R).Camba, JD.; Contero, M.; Salvador Herranz, GM.; Plumed, R. (2016). Synchronous communication in PLM environments using annotated CAD models. Journal of Systems Science and Systems Engineering. 25(2):142-158. https://doi.org/10.1007/s11518-016-5305-5S142158252Abrahamson, S., Wallace, D., Senin, N. & Sferro, P. (2000). Integrated design in a service marketplace. Computer-Aided Design, 32(2):97–107.Ahmed, S. (2005). Encouraging reuse of design knowledge: a method to index knowledge. Design Studies, 26:565–592.Alavi, M. & Tiwana, A (2002). Knowledge integration in virtual teams: the potential role of KMS. Journal of the American Society for Information Science and Technology, 53:1029–1037.Ameri, F. & Dutta, D. (2005). Product lifecycle management: closing the knowledge loops. Computer-Aided Design and Applications, 2(5):577–590.Anderson, A.H., Smallwood, L., MacDonald, R., Mullin, J., Fleming, A. & O'Malley, C. (2000). Video data and video links in mediated communication: what do users value? International Journal of Human-Computer Studies, 52(1):165–187.Arias, E., Eden, H., Fischer, G., Gorman, A. & Scharff, E. (2000). Transcending the individual human mind–creating shared understanding through collaborative design. ACM Transactions on Computer-Human Interaction (TOCHI) 7(1): 84–113.Barley, W.C., Leonardi, P.M., & Bailey, D.E. (2012). Engineering objects for collaboration: strategies of ambiguity and clarity at knowledge boundaries. Human Communication Research, 38:280–308.Boujut, J.F. & Dugdale, J. (2006). Design of a 3D annotation tool for supporting evaluation activities in engineering design. Cooperative Systems Design, COOP 6:1–8.Camba, J., Contero, M., Johnson, M. & Company, P. (2014). Extended 3D annotations as a new mechanism to explicitly communicate geometric design intent and increase CAD model reusability. Computer-Aided Design, 57:61–73.Camba, J., Contero, M. & Salvador-Herranz, G. (2014). Speak with the annotator: promoting interaction in a knowledge-based CAD environment built on the extended annotation concept. Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 196–201.Chudoba, K.M., Wynn, E., Lu, M. & Watson-Manheim, M.B. (2005). How virtual are we? Measuring virtuality and understanding its impact in a global organization. Information Systems Journal, 15(4):279–306.Danesi, F., Gardan, N. & Gardan, Y. (2006). Collaborative Design: from Concept to Application. Geometric Modeling and Imaging—New Trends, 90–96.Durstewitz, M., Kiefner, B., Kueke, R., Putkonen, H., Repo, P. & Tuikka, T. (2002). Virtual collaboration environment for aircraft design. Proceedings of the IEEE 6th International Conference on Information Visualisation, 502–507.Fisher, D., Brush, A.J., Gleave, E. & Smith, M.A. (2006). Revisiting Whittaker and Sidner’s email overload ten years later. Proceedings of the 2006 20th Anniversary Conference on Computer Supported Cooperative Work. ACM, BanffFonseca, M.J., Henriques, E., Silva, N., Cardoso, T. & Jorge, J.A. (2006). A collaborative CAD conference tool to support mobile engineering. Rapid Product Development (RPD’06), Marinha Grande, Portugal.Frechette, S.P. (2011). Model based enterprise for manufacturing. Proceedings of the 44th CIRP International Conference on Manufacturing Systems.Fu, W.X., Bian, J. & Xu, Y.M. (2013). A video conferencing system for collaborative engineering design. Applied Mechanics and Materials, 344:246–252.Fuh, J.Y.H. & Li, W.D. (2005). Advances in collaborative CAD: the-state-of-the art. Computer-Aided Design, 37:571–581.Fussell, S.R., Kraut, R.E. & Siegel, J. (2000). Coordination of communication: effects of shared visual context on collaborative work. Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, 21–30.Gajewska, H., Kistler, J., Manasse, M.S. & Redell, D. (1994). Argo: a system for distributed collaboration. Proceedings of the ACM Second International Conference on Multimedia, San Francisco, CA, USA. 433–440.Gantz, J., Reinsel, D., Chute, C., Schlichting, W., Mcarthur, J., Minton, S., Xheneti, I., Toncheva, A. & Manfrediz, A. (2007). The expanding digital universe: a forecast of worldwide information growth through 2010. IDC, Massachusetts.Gowan, Jr. J.A. & Downs, J.M. (1994). Video conferencing human-machine interface: a field study. Information and Management, 27(6):341–356.Gupta, A., Mattarelli, E., Seshasai, S. & Broschak, J. (2009). Use of collaborative technologies and knowledge sharing in co-located and distributed teams: towards the 24-h knowledge factory. The Journal of Strategic Information Systems, 18:147–161.Hickson, I. (2009). The Web Socket Protocol IETF, Standards Track.Hong, J., Toye, G. & Leifer, L.J. (1996). Engineering design notebook for sharing and reuse. Computers in Industry, 29:27–35.Isaacs, E.A. & Tang, J.C. (1994). What video can and cannot do for collaboration: a case study. Multimedia Systems, 2(2):63–73.Karsenty, L. (1999). Cooperative work and shared visual context: an empirical study of comprehension problems in side-by-side and remote help dialogues. Human Computer Interaction, 14(3): 283–315.Lahti, H., Seitamaa-Hakkarainen, P. & Hakkarainen, K. (2004). Collaboration patterns in computer supported collaborative designing. Design Studies, 25:351–371.Leenders, R.T.A., Van Engelen, J.M. & Kratzer, J. (2003). Virtuality, communication, and new product team creativity: a social network perspective. Journal of Engineering and Technology Management, 20(1):69–92.Levitt, R.E., Jin, Y. & Dym, C.L. (1991). Knowledge-based support for management of concurrent, multidisciplinary design. Artificial Intelligence for Engineering, Design, Analysis and Manufacturing, 5(2):77–95.Li, C., McMahon, C. & Newnes, L. (2009). Annotation in product lifecycle management: a review of approaches. Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC2009. Vol. 2. New York: ASME, 797–806.Li, W.D., Lu, W.F., Fuh, J.Y. & Wong, Y.S. (2005). Collaborative computer-aided design-research and development status. Computer-Aided Design, 37(9):931–940.Londono, F., Cleetus, K.J., Nichols, D.M., Iyer, S., Karandikar, H.M., Reddy, S.M., Potnis, S.M., Massey, B., Reddy, A. & Ganti, V. (1992). Coordinating a virtual team. CERC-TR-RN-92-005, Concurrent Engineering Research Centre, West Virginia University, West Virginia.Lubell, J., Chen, K., Horst, J., Frechette, S., & Huang, P. (2012). Model based enterprise/technical data package summit report. NIST Technical Note, 1753.May, A. & Carter, C. (2001). A case study of virtual team working in the European automotive industry. International Journal of Industrial Ergonomics, 27(3):171–186.Olson, J.S., Olson, G.M. & Meader, D.K. (1995). What mix of video and audio is useful for small groups doing remote real-time design work? Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM Press, Addison-Wesley Publishing Co.Ping-Hung, H., Mishra, C.S. & Gobeli, D.H. (2003). The return on R&D versus capital expenditures in pharmaceutical and chemical industries. IEEE Transactions on Engineering Management, 50:141–150.Sharma, A. (2005). Collaborative product innovation: integrating elements of CPI via PLM framework. Computer-Aided Design, 37(13):1425–1434.Shum, S.J.B., Selvin, A.M., Sierhuis, M., Conklin, J., Haley, C.B. & Nuseibeh, B. (2006). Hypermedia support for argumentation-based rationale: 15 Years on from Gibis and Qoc. Rationale Management in Software Engineering, 111–132.Siltanen, P. & Valli, S. (2013). Web-based 3D Mediated Communication in Manufacturing Industry. Concurrent Engineering Approaches for Sustainable Product Development in a Multidisciplinary Environment, 1181–1192. Springer London.Stark, J. (2011). Product Lifecycle Management. 1–16. Springer London.Tavcar, J., Potocnik, U. & Duhovnik, J. (2013). PLM used as a backbone for concurrent engineering in supply chain. Concurrent Engineering Approaches for Sustainable Product Development in a Multi-Disciplinary Environment, 681–692.Tay, F.E.H. & Ming, C. (2001). A shared multi-media design environment for concurrent engineering over the internet. Concurrent Engineering, 9(1):55–63.Tay, F.E.H. & Roy, A. (2003). CyberCAD: a collaborative approach in 3D-CAD technology in a multimedia-supported environment. Computers in Industry, 52(2):127–145.Toussaint, J. & Cheng, K. (2002). Design agility and manufacturing responsiveness on the web. Integrated Manufacturing Systems, 13(5):328–339.Tsoi, K.N. & Rahman, S.M. (1996). Media-on-demand multimedia electronic mail: a tool for collaboration on the web. Proceedings of the 5th IEEE International Symposium on High Performance Distributed Computing.Upton, D.M. & Mcafee, A. (1999). The Real Virtual Factory. Harvard Business School Press, 69–89.Vila, C., Estruch, A., Siller, H.R., Abellán, J.V. & Romero, F. (2007). Workflow methodology for collaborative design and manufacturing. Cooperative Design, Visualization, and Engineering 42–49, Springer Berlin Heidelberg.Wasiak, J., Hicks, B., Newnes, L., Dong, A., & Burrow, L. (2010). Understanding engineering email: the development of a taxonomy for identifying and classifying engineering work. Research in Engineering Design, 21(1):43–64.Wasko, M.M. & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly: Management Information Systems, 29:35–57.Yang, Q.Z., Zhang, Y., Miao, C.Y. & Shen, Z.Q. (2008). Semantic annotation of digital engineering resources for multidisciplinary design collaboration. ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 617–624. American Society of Mechanical Engineers.You, C.F. & Chao, S.N. (2006). Multilayer architecture in collaborative environment. Concurrent Engineering Research and Applications, 14(4):273–281.Yuan, Y.C., Fulk, J., Monge, P.R. & Contractor, N. (2010). Expertise directory development, shared task interdependence, and strength of communication network ties as multilevel predictors of expertise exchange in transactive memory work groups. Communication Research, 37: 20–47

Measurement of the azimuthal anisotropy of Y(1S) and Y(2S) mesons in PbPb collisions at √S^{S}NN = 5.02 TeV

The second-order Fourier coefficients (υ$_{2}$) characterizing the azimuthal distributions of Υ(1S) and Υ(2S) mesons produced in PbPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV are studied. The Υmesons are reconstructed in their dimuon decay channel, as measured by the CMS detector. The collected data set corresponds to an integrated luminosity of 1.7 nb$^{-1}$. The scalar product method is used to extract the υ$_{2}$ coefficients of the azimuthal distributions. Results are reported for the rapidity range |y| < 2.4, in the transverse momentum interval 0 < p$_{T}$ < 50 GeV/c, and in three centrality ranges of 10–30%, 30–50% and 50–90%. In contrast to the J/ψ mesons, the measured υ$_{2}$ values for the Υ mesons are found to be consistent with zero

Performance of the CMS Level-1 trigger in proton-proton collisions at √s = 13 TeV

At the start of Run 2 in 2015, the LHC delivered proton-proton collisions at a center-of-mass energy of 13\TeV. During Run 2 (years 2015–2018) the LHC eventually reached a luminosity of 2.1× 10$^{34}$ cm$^{-2}$s$^{-1}$, almost three times that reached during Run 1 (2009–2013) and a factor of two larger than the LHC design value, leading to events with up to a mean of about 50 simultaneous inelastic proton-proton collisions per bunch crossing (pileup). The CMS Level-1 trigger was upgraded prior to 2016 to improve the selection of physics events in the challenging conditions posed by the second run of the LHC. This paper describes the performance of the CMS Level-1 trigger upgrade during the data taking period of 2016–2018. The upgraded trigger implements pattern recognition and boosted decision tree regression techniques for muon reconstruction, includes pileup subtraction for jets and energy sums, and incorporates pileup-dependent isolation requirements for electrons and tau leptons. In addition, the new trigger calculates high-level quantities such as the invariant mass of pairs of reconstructed particles. The upgrade reduces the trigger rate from background processes and improves the trigger efficiency for a wide variety of physics signals

Pileup mitigation at CMS in 13 TeV data

With increasing instantaneous luminosity at the LHC come additional reconstruction challenges. At high luminosity, many collisions occur simultaneously within one proton-proton bunch crossing. The isolation of an interesting collision from the additional "pileup" collisions is needed for effective physics performance. In the CMS Collaboration, several techniques capable of mitigating the impact of these pileup collisions have been developed. Such methods include charged-hadron subtraction, pileup jet identification, isospin-based neutral particle "δβ" correction, and, most recently, pileup per particle identification. This paper surveys the performance of these techniques for jet and missing transverse momentum reconstruction, as well as muon isolation. The analysis makes use of data corresponding to 35.9 fb$^{-1}$ collected with the CMS experiment in 2016 at a center-of-mass energy of 13 TeV. The performance of each algorithm is discussed for up to 70 simultaneous collisions per bunch crossing. Significant improvements are found in the identification of pileup jets, the jet energy, mass, and angular resolution, missing transverse momentum resolution, and muon isolation when using pileup per particle identification

Measurement of the Y(1S) pair production cross section and search for resonances decaying to Y(1S)μ⁺μ⁻ in proton-proton collisions at √s = 13 TeV

The fiducial cross section for Y(1S)pair production in proton-proton collisions at a center-of-mass energy of 13TeVin the region where both Y(1S)mesons have an absolute rapidity below 2.0 is measured to be 79 ± 11 (stat) ±6 (syst) ±3 (B)pbassuming the mesons are produced unpolarized. The last uncertainty corresponds to the uncertainty in the Y(1S)meson dimuon branching fraction. The measurement is performed in the final state with four muons using proton-proton collision data collected in 2016 by the CMS experiment at the LHC, corresponding to an integrated luminosity of 35.9fb$^{-1}$. This process serves as a standard model reference in a search for narrow resonances decaying to Y(1S)μ$^{+}$μ$^{-}$ in the same final state. Such a resonance could indicate the existence of a tetraquark that is a bound state of two bquarks and two b̅ antiquarks. The tetraquark search is performed for masses in the vicinity of four times the bottom quark mass, between 17.5 and 19.5GeV, while a generic search for other resonances is performed for masses between 16.5 and 27GeV. No significant excess of events compatible with a narrow resonance is observed in the data. Limits on the production cross section times branching fraction to four muons via an intermediate Y(1S)resonance are set as a function of the resonance mass