Design framework for model-based self-optimizing manufacturing systems

Designing manufacturing systems requires a profound understanding of the manufacturing process and its challenges to meet final customer requirements. Considering future objectives already at an early design stage increases the flexibility of the manufacturing system and its robustness regarding changed boundary conditions. Today’s manufacturing systems rather control machine settings than process variables or even product quality. The major barrier for quality control is that in most manufacturing processes, quality cannot be measured on-line. Model-based self-sptimization (MBSO) has been developed to overcome this limitation. A combination of embedded process knowledge and tailored sensor integration enables for on-line quality estimation. The overall objective is to control key characteristics of product quality in a broad manufacturing landscape. This work describes a guideline of how to design an MBSO system with examples at each stage of the development process.

[1]  L. Daneshmend,et al.  Model Reference Adaptive Control of Feed Force in Turning , 1986 .

[2]  Uwe Reisgen,et al.  Optimierung von Prozessparametern beim automatisierten MSG-Schweißen durch die inverse Nutzung von Ersatzmodellen , 2015 .

[3]  A. Varela Self-Tuning Pressure Control in an Injection Moulding Cavity During Filling , 2000 .

[4]  Wen-Chin Chen,et al.  Application of advanced process control in plastic injection molding , 2008, 2008 IEEE International Conference on Service Operations and Logistics, and Informatics.

[5]  Ioannis Minis,et al.  Optimal Control of the Cutting Force in Metal Cutting Operations , 1990 .

[6]  Fritz Klocke,et al.  Concept for a Technology Assistance System to Analyze and Evaluate Materials and Tools for Milling , 2015 .

[7]  Wolfgang Schulz,et al.  Self-optimizing Production Technologies , 2017 .

[8]  J. Richalet,et al.  Industrial applications of model based predictive control , 1993, Autom..

[9]  Andrew G. Alleyne,et al.  Nonlinear control of an electrohydraulic injection molding machine via iterative adaptive learning , 1999 .

[10]  Rolf Isermann,et al.  Adaptive control of the cutting power for milling operation , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[11]  Dirk Abel,et al.  A self-optimising injection moulding process with model-based control system parameterisation , 2016, Int. J. Comput. Integr. Manuf..

[12]  Christian Brecher,et al.  Self-optimizing Production Systems☆ , 2016 .

[13]  S. Kamaruddin,et al.  Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review , 2013 .

[14]  Peter S. Maybeck,et al.  Stochastic Models, Estimation And Control , 2012 .

[15]  T. Gries,et al.  Weaving machine as cyber-physical production system: Multi-objective self-optimization of the weaving process , 2016, 2016 IEEE International Conference on Industrial Technology (ICIT).

[16]  Thomas Gries,et al.  Investigation of the Relations Between the Parameters in the Radial Braiding Process , 2016 .

[17]  Gunawan Dewantoro,et al.  Model reference adaptive control of cavity pressure in injection molding during filling and packing phases , 2011, 2011 2nd International Conference on Instrumentation Control and Automation.

[18]  Yoram Koren,et al.  Adaptive Control with Process Estimation , 1981 .

[19]  Yves-Simon Gloy,et al.  Integration of the vertical warp stop motion positioning in the model-based self-optimization of the weaving process , 2017 .

[20]  Thomas Auerbach,et al.  Comparative study on optimization algorithms for online identification of an instantaneous force model in milling , 2018, The International Journal of Advanced Manufacturing Technology.

[21]  Tullio Tolio,et al.  Design and management of manufacturing systems for production quality , 2014 .

[22]  Douglas J. Cooper,et al.  Pattern‐based closed‐loop quality control for the injection molding process , 1997 .

[23]  Yusuf Altintas,et al.  A general mechanics and dynamics model for helical end mills , 1996 .

[24]  George Chryssolouris,et al.  Manufacturing Systems: Theory and Practice , 1992 .

[25]  Avner Friedman,et al.  A Stefan-Signorini problem☆ , 1984 .

[26]  Sébastien Campocasso,et al.  Towards cutting force evaluation without cutting tests , 2017 .

[27]  Yves-Simon Gloy,et al.  Model based self-optimization of the weaving process , 2015 .

[28]  R. Saravanan,et al.  Comparative Analysis of Conventional and Non-Conventional Optimisation Techniques for CNC Turning Process , 2001 .

[29]  Christian Brecher,et al.  Integrative Production Technology—Theory and Applications , 2017 .

[30]  A. Bemporad,et al.  Model Predictive Control Design: New Trends and Tools , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[31]  Robert X. Gao,et al.  Cloud-enabled prognosis for manufacturing , 2015 .

[32]  Krzysztof Jemielniak,et al.  Advanced monitoring of machining operations , 2010 .

[33]  William M. Steen,et al.  Experimental study of the relationship between in-process signals and cut quality in gas-assisted laser cutting , 1990, Other Conferences.

[34]  Furong Gao,et al.  Cycle-to-cycle and within-cycle adaptive control of nozzle pressure during packing-holding for thermoplastic injection molding , 1999 .

[35]  Fritz Klocke,et al.  Model Predictive Control for Force Control in Milling , 2017 .

[36]  Ichiro Inasaki,et al.  Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application , 1995 .

[37]  R. Venkata Rao,et al.  Advanced Modeling and Optimization of Manufacturing Processes , 2010 .

[38]  Wolfgang Schulz,et al.  Mathematical modelling and linear stability analysis of laser fusion cutting , 2016 .

[39]  Zhongbao Chen,et al.  Injection molding quality control by integrating weight feedback into a cascade closed‐loop control system , 2007 .

[40]  B. Bulut,et al.  Industrial Application of Model Based Predictive Control , 2000 .

[41]  Stelios Psarakis,et al.  Multivariate statistical process control charts: an overview , 2007, Qual. Reliab. Eng. Int..

[42]  Derek Bingham,et al.  Handbook of Design and Analysis of Experiments , 2015 .

[43]  A. Galip Ulsoy,et al.  A comparison of model-based machining force control approaches , 2004 .

[44]  C.C.H. Ma,et al.  Direct Adaptive Control of Milling Force , 1990, Proceedings of the IEEE International Workshop on Intelligent Motion Control.

[45]  Wolfgang Schulz,et al.  Measurement of Cut Front Properties in Laser Cutting , 2014 .

[46]  Fritz Klocke,et al.  Model-based predictive force control in milling: determination of reference trajectory , 2017, Prod. Eng..

[47]  Dirk Abel,et al.  Model Predictive Feed Rate Control for a Milling Machine , 2016 .

[48]  Wolfgang Schulz,et al.  Determination of process variables in melt-based manufacturing processes , 2016, Int. J. Comput. Integr. Manuf..

[49]  Barry K. Fussell,et al.  Adaptive control of force in end milling operations— an evaluation of available algorithms , 1991 .