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

Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network

The energy and mass measurements of jets are crucial tasks for the Large Hadron Collider experiments. This paper presents a new calibration method to simultaneously calibrate these quantities for large-radius jets measured with the ATLAS detector using a deep neural network (DNN). To address the specificities of the calibration problem, special loss functions and training procedures are employed, and a complex network architecture, which includes feature annotation and residual connection layers, is used. The DNN-based calibration is compared to the standard numerical approach in an extensive series of tests. The DNN approach is found to perform significantly better in almost all of the tests and over most of the relevant kinematic phase space. In particular, it consistently improves the energy and mass resolutions, with a 30% better energy resolution obtained for transverse momenta pT > 500 GeV

The ATLAS trigger system for LHC Run 3 and trigger performance in 2022

The ATLAS trigger system is a crucial component of the ATLAS experiment at the LHC. It is responsible for selecting events in line with the ATLAS physics programme. This paper presents an overview of the changes to the trigger and data acquisition system during the second long shutdown of the LHC, and shows the performance of the trigger system and its components in the proton-proton collisions during the 2022 commissioning period as well as its expected performance in proton-proton and heavy-ion collisions for the remainder of the third LHC data-taking period (2022–2025)

Electron and photon energy calibration with the ATLAS detector using LHC Run 2 data

This paper presents the electron and photon energy calibration obtained with the ATLAS detector using 140 fb-1 of LHC proton-proton collision data recorded at √(s) = 13 TeV between 2015 and 2018. Methods for the measurement of electron and photon energies are outlined, along with the current knowledge of the passive material in front of the ATLAS electromagnetic calorimeter. The energy calibration steps are discussed in detail, with emphasis on the improvements introduced in this paper. The absolute energy scale is set using a large sample of Z-boson decays into electron-positron pairs, and its residual dependence on the electron energy is used for the first time to further constrain systematic uncertainties. The achieved calibration uncertainties are typically 0.05% for electrons from resonant Z-boson decays, 0.4% at ET ∼ 10 GeV, and 0.3% at ET ∼ 1 TeV; for photons at ET ∼ 60 GeV, they are 0.2% on average. This is more than twice as precise as the previous calibration. The new energy calibration is validated using J/ψ → ee and radiative Z-boson decays

Performance and calibration of quark/gluon-jet taggers using 140 fb−1 of pp collisions at √s = 13 TeV with the ATLAS detector

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Beam-induced backgrounds measured in the ATLAS detector during local gas injection into the LHC beam vacuum

Inelastic beam-gas collisions at the Large Hadron Collider (LHC), within a few hundred metres of the ATLAS experiment, are known to give the dominant contribution to beam backgrounds. These are monitored by ATLAS with a dedicated Beam Conditions Monitor (BCM) and with the rate of fake jets in the calorimeters. These two methods are complementary since the BCM probes backgrounds just around the beam pipe while fake jets are observed at radii of up to several metres. In order to quantify the correlation between the residual gas density in the LHC beam vacuum and the experimental backgrounds recorded by ATLAS, several dedicated tests were performed during LHC Run 2. Local pressure bumps, with a gas density several orders of magnitude higher than during normal operation, were introduced at different locations. The changes of beam-related backgrounds, seen in ATLAS, are correlated with the local pressure variation. In addition the rates of beam-gas events are estimated from the pressure measurements and pressure bump profiles obtained from calculations. Using these rates, the efficiency of the ATLAS beam background monitors to detect beam-gas events is derived as a function of distance from the interaction point. These efficiencies and characteristic distributions of fake jets from the beam backgrounds are found to be in good agreement with results of beam-gas simulations performed with theFluka Monte Carlo programme

Measurements of the production cross-section for a Z boson in association with b- or c-jets in proton–proton collisions at √s = 13 TeV with the ATLAS detector

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Observation of gauge boson joint-polarisation states in W±Z production from pp collisions at s=13 TeV with the ATLAS detector

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