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Performance Evaluation of Machining Strategy for Engine-Block Manufacturing

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper will introduce a novel methodology for the performance evaluation of machining strategies of engine-block manufacturing. The manufacturing of engine components is vital to the automotive and vehicle manufacturing industries. Machining are a critical processes in the production of these parts. To survive and excel in the competitive manufacturing environment, companies need to improve as well as update their machining processes and evaluate the performance of their machining lines. Moreover, the lines and processes have to be robust in handling different sources of variation over time that include such examples as demand fluctuations, work-piece materials or even any changes in design specifications. A system dynamics modelling and simulation approach has been deployed to develop a methodology that captures how machining system parameters from the machining process are interacted with each other, how these connections drive performance and how new targets affect process and machine tool parameters through time. The developed model could provide an insight of how to select the crucial machining system parameters and to identify the effect of those parameters on the output of the system. In response to such an analysis, this paper provides (offers) a framework to examine machining strategies and has presented model that is useful as a decision support system for the evaluation and selection of machining strategies. Here a system dynamics methodology for modelling is applied to the milling operation and the model is based on an actual case study from the engine-block manufacturing industry.
Rocznik
Strony
81--102
Opis fizyczny
Bibliogr. 26 poz., tab., rys.
Twórcy
autor
  • KTH Royal Institute of Technology, Department of Production Engineering, Stockholm, Sweden
  • KTH Royal Institute of Technology, Department of Production Engineering, Stockholm, Sweden
autor
  • KTH Royal Institute of Technology, Department of Production Engineering, Stockholm, Sweden
autor
  • KTH Royal Institute of Technology, Department of Production Engineering, Stockholm, Sweden
Bibliografia
  • [1] ADANE T.F., NICOLESCU M., 2014, System dynamics analysis of energy usage: case studies in automotive manufacturing, Int. J. Manufacturing Research, 9/2, 131-156.
  • [2] ADEEL H.S., ISMAIL N., WONG S.V., JALIL N.A A., 2010, Optimization of cutting parameters based on surface roughness and assistance of workpiece surface temperature in turning process, American J. of Engineering and Applied Sciences 3/1, 102-108.
  • [3] ALWAISE A. M.A., USUBAMATOV R., ZAIN Z.M., SAIFULDDIN A., BHUVENESH R., 2011, Optimisation of machining parameters by criterion of maximum productivity rate, Australian Journal of Basic and Applied Sciences, 5/11, 543-548.
  • [4] ARCHENTI A., 2014, Prediction of machined part accuracy from machining system capability. CIRP Annals - Manufacturing Technology, 63, 505-508.
  • [5] ARCHENTI A., NICOLESCU C.M., 2013, Accuracy analysis of machine tools using elastically linked systems, CIRP Annals – Manufacturing Technology, 62, 503-506.
  • [6] ARONSON D., 1996, Overview of system thinking [online].
  • [7] URLhttp://www.thinking.net/Systems_Thinking/OverviewSTarticle.pdf (Accessed March 2014).
  • [8] BIANCHI M.F., 2014, Evaluation of machining strategies in cylinder-block manufacturing – dynamic modelling, Master’s thesis, Royal Institute of Technology Stockholm, Sweden.
  • [9] BLUMBERGA A., BLUMBERGA D., et al., 2011, System Dynamics for Environmental, Engineering Students, 351, ID: 9980. Riga: Rigas Tehniskas universitates Vides aizsardzibas un siltuma sistemu instituts.
  • [10] CRESPO-MARQUEZ, A., USANO, R.R., AZNAR R.D., 1993, Continuous and discrete simulation in a production planning system: a comparative study, Proceedings of International System Dynamics Conference, Cancun, Mexico, The System Dynamics Society, 58.
  • [11] FORRESTER J.W., 1997, Industrial dynamics. Journal of the Operational Research Society 48 (10), 1037-1041.
  • [12] HON K.K.B., 2005, Performance and evaluation of manufacturing systems, CIRP Annals - Manufacturing Technology, 54, 139-154.
  • [13] HU S.J., ZHU X., WANG H., KOREN Y., 2008, Product variety and manufacturing complexity in assembly systems and supply chains, CIRP Annals - Manufacturing Technology, 57, 45-48.
  • [14] JAWAHIR I.S., BRINKSMEIER E.M., SAOUBI R., et al., 2011, Surface integrity in material removal processes: Recent advances, CIRP Annals - Manufacturing Technology, 60, 603-626.
  • [15] KIBIRA D., SHAO G., LEE Y.T., 2009, Modeling and simulation analysis types for sustainable manufacturing, Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop, MD, USA, September 2010, 69-76.
  • [16] KIM C., LEE T., 2013, Modelling and simulation of automated manufacturing systems for evaluation of complex schedules, International Journal of Production Research, 51/12, 3734-3747.
  • [17] LIN C., BAINES T.S., O’KANE J., LINK D., 1998, A generic methodology that aids the application of system dynamics to manufacturing system modeling, International Conference on SIMULATION, IEE, Staffordshire University, UK.
  • [18] MAANI K.M., CAVANA R.Y., 2007, System Thinking, System Dynamics: Managing Changes and Complexity, 2nd ed., Pearson Education, Canada.
  • [19] NICOLESCU C. M., 1991, Analysis, identification and prediction of chatter in turning, Ph.D. thesis, KTH Royal Institute of Technology Stockholm, Sweden.
  • [20] PAPAKOSTAS N., EFTHYMIOU K., MOURTZIS D., CHRYSSOLOURIS G., 2009, Modelling the complexity of manufacturing systems using nonlinear dynamics approaches, CIRP Annals – Manufacturing Technology, 58/1, 437-440.
  • [21] ROHIT R.H., YUNG, C.S., 2011, Robust optimisation of machining conditions with tool life and surface roughness uncertainties, International Journal of Production Research, 49/13, 3963-3978.
  • [22] SMART J., CALINESCU A., HUATUCO L.H., 2013, Extending the information-theoretic measures of the dynamic complexity of manufacturing systems, International Journal of Production Research, 51/2, 362-379.
  • [23] STERMAN J., 2000, Business dynamics: System Thinking and Modeling for a Complex World, McGraw-Hill.
  • [24] TAKO A.A., ROBINSON S., 2009, Comparing model development in discrete event simulation and system dynamics, Proceedings of the 2009 Winter Simulation Conference, 979-991.
  • [25] TESFAMARIAM D., LINDBERG B., 2005, Aggregate analysis of manufacturing systems using system dynamics and ANP, Computers & Industrial Engineering, 49/1, 98-117.
  • [26] XUEHONG D., JIANXIN J., TSENG M., 2006, Understanding customer satisfaction in product customization, International Journal of Advanced Manufacturing Technology, 31/3-4, 396-406.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f2924ede-c302-450b-9991-10c57a09f67f
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