Framework for accurate simulation and model-based control of hybrid manufacturing processes

Abstract Accuracy is one of the main metrics to evaluate a manufacturing system. The increasing of the current manufacturing technologies and processes accuracy is still an ongoing activity. In this direction, a model-based approach for robot and hybrid manufacturing processes is presented in this paper. The proposed framework focuses on the optimization of hybrid manufacturing processes by developing accurate simulation models of multi-arm robot and processes in order to increase throughput, energy and material efficiency, quality and accuracy, allowing the processing of large and complex geometries. This is achieved by evaluating real time closed loop control schemas with sophisticated sensorial and monitoring systems. Moreover, a machine programming module for allowing the user to easy program and re-configure the machine will also be described.

[1]  Rakesh Nagi,et al.  STEP-based feature extraction from STEP geometry for Agile manufacturing , 2000 .

[2]  Bruno Siciliano,et al.  Modelling and Control of Robot Manipulators , 1997, Advanced Textbooks in Control and Signal Processing.

[3]  Patrik Axelsson,et al.  Bayesian state estimation of a flexible industrial robot , 2012 .

[4]  Yu. Chivel,et al.  The system of laser sintering process monitoring and adaptive control , 2007, International Conference on Lasers, Applications, and Technologies.

[5]  Carl Deckard,et al.  Advances in modeling the effects of selected parameters on the SLS process , 1998 .

[6]  Bijan Shirinzadeh,et al.  A systematic technique to estimate positioning errors for robot accuracy improvement using laser interferometry based sensing , 2005 .

[7]  Sergio Bittanti.  Software Reliability Modelling and Identification , 1988, Lecture Notes in Computer Science.

[8]  David J. Murray-Smith,et al.  Nonlinear model structure identification using genetic programming , 1998 .

[9]  Stig Moberg,et al.  Modeling and Control of Flexible Manipulators , 2007 .

[10]  D. Mynors,et al.  A three-dimensional finite element analysis of the temperature field during laser melting of metal powders in additive layer manufacturing , 2009 .

[11]  Fakhri Karray,et al.  Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.

[12]  M. Doubenskaia,et al.  Selective laser melting process monitoring with high speed infra-red camera and pyrometer , 2008, Fundamentals of Laser Assisted Micro- and Nanotechnologies.

[13]  Michael F. Zaeh,et al.  Prioritization of Process Parameters for an Efficient Optimisation of Additive Manufacturing by Means of a Finite Element Method , 2013 .

[14]  Sotiris Makris,et al.  Cooperating robots for fixtureless assembly: modelling and simulation of tool exchange process , 2018, Int. J. Comput. Integr. Manuf..

[15]  Sotiris Makris,et al.  An approach for implementing power and force limiting in sensorless industrial robots , 2018 .

[16]  Eberhard Abele,et al.  Modeling and Identification of an Industrial Robot for Machining Applications , 2007 .

[17]  Sotiris Makris,et al.  Virtual Commissioning of an Assembly Cell with Cooperating Robots , 2012, Adv. Decis. Sci..

[18]  Jun Wu,et al.  Review: An overview of dynamic parameter identification of robots , 2010 .

[19]  Jan Bültmann,et al.  Design of an Optical system for the In Situ Process Monitoring of Selective Laser Melting (SLM) , 2011 .

[20]  K. Georgoulias,et al.  Methodology for enabling Digital Twin using advanced physics-based modelling in predictive maintenance , 2019, Procedia CIRP.

[21]  Antti Salminen,et al.  Monitoring and Adaptive Control of Laser Processes , 2014 .

[22]  Peter A. Fritzson,et al.  Principles of object-oriented modeling and simulation with Modelica 2.1 , 2004 .

[23]  Antti Salminen,et al.  Characterization of Process Efficiency Improvement in Laser Additive Manufacturing , 2014 .

[24]  J. De Schutter,et al.  Dynamic Model Identification for Industrial Robots , 2007, IEEE Control Systems.

[25]  Yuwen Zhang,et al.  Thermal Modeling of Metal Powder-Based Selective Laser Sintering , 2005 .

[26]  Zhibin Lin,et al.  Multiphysical Simulation of ns-Laser Ablation of Multi-material LED-structures☆ , 2014 .

[27]  Bharath Vasudevarao,et al.  Sensitivity of Rp Surface Finish to Process Parameter Variation , 2000 .

[28]  E. Reutzel,et al.  Thermo-mechanical model development and validation of directed energy deposition additive manufacturing of Ti–6Al–4V , 2015 .

[29]  H. Bruyninckx,et al.  An open embedded hardware and software architecture applied to industrial robot control , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[30]  E. Rodriguez,et al.  Approximation of absolute surface temperature measurements of powder bed fusion additive manufacturing technology using in situ infrared thermography , 2015 .

[31]  C. Taylor.  Direct laser sintering of stainless steel : thermal experiments and numerical modelling , 2004 .

[32]  George Chryssolouris,et al.  A RUL calculation approach based on physical-based simulation models for predictive maintenance , 2017, 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC).

[33]  George Chryssolouris,et al.  On an Empirical Investigation of the Structural Behavior of Robots , 2012 .

[34]  Masine Md. Tap,et al.  Data Transfer Between CAD System and RP System: A Report , 2002 .

[35]  Erik Wernholt,et al.  Multivariable Frequency-Domain Identification of Industrial Robots , 2007 .

[36]  Marina Indri,et al.  Friction modeling and identification for industrial manipulators , 2013, 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA).

[37]  Sotiris Makris,et al.  Digital twin for adaptation of robots’ behavior in flexible robotic assembly lines , 2019, Procedia Manufacturing.

[38]  Antonio Domenico Ludovico,et al.  3D Finite Element Analysis in the selective laser melting process , 2011 .

[39]  Jean-Pierre Kruth,et al.  Feedback control of Layerwise Laser Melting using optical sensors , 2010 .

[40]  M. Spong,et al.  Robot Modeling and Control , 2005 .

[41]  Svante Gunnarsson,et al.  Nonlinear identification of a physically parameterized robot model , 2006 .