Real-time evolutionary optimization of metallurgical processes using ARM microcontroller

Adam Mrozek¹ , Wacław Kuś², Łukasz Sztangret¹

¹AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland.

²2Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland.

DOI:

https://doi.org/10.7494/cmms.2016.1.0556

Abstract:

The real time (RT) computations with the use of microcontrollers have been present in everyday life for years. They are very useful in e.g. online control of processes due to the ability to determine the proper control in case of any environment changes. The algorithms employed in RT computation must be as simple as possible to meet the imposed time limits. On the other hand, the continuous increase in computational power of modern microcontrollers and embedded platforms causes that more complex algorithms can be performed in the real time. However, during implementation of any algorithm the specific structure and requirements of the microcontroller must be taken into consideration. Another way of fulfilling the time limits of the RT computations is application of metamodel instead of model of controlling process. Within this paper the possibility of application of evolutionary algorithm (EA) to solve three chosen optimization problems in real time using microcontroller of ARM architecture was considered. Analyzed optimization problems were as follows aluminum alloy anti-collision side beam hot stamping process, laminar cooling of dual phase (DP) steel sheets and minimization of the potential energy of the atomic clusters. All computations were performed using two different approaches i.e. low-level and object- oriented approach. Obtained results and drawn conclusions are presented.

Cite as:

Mrozek, A., Kuś, W., Sztangret, Ł. (2016). Real-time evolutionary optimization of metallurgical processes using ARM microcontroller. Computer Methods in Materials Science, 16(1), 20 – 26. https://doi.org/10.7494/cmms.2016.1.0556

Article (PDF):

Keywords:

Real time computation, Microcontroller, Embedded systems, Optimization of metallurgical processes, Evolutionary algorithm, Metamodel

References:

Berkley, J., Turkiyyah, G., Berg, D., Ganter, M., Weghorst, S,2004, Real-Time Finite Element Modeling for SurgerySimulation: An Application to Virtual Suturing, IEEETransactions on visualization and computer graphics, 10(3),314-325.

Duriez, C., 2013, Control of elastic soft robots based onreal-time finite element method, Robotics and Automation(ICRA), 3982-3987.

Girifalco, L.A.,Weizer, V.G., 1959, Application of the MorsePotential Function to Cubic Metals, Physical Review, 114(3), 687-690.

Hohl, W., Hinds, Ch., 2015, ARM Assembly Language. Fundamentalsand Techniques, CRC Press.

Isobe, Y., Watanabe, H., Yamazaki, N., XiaoWei, L., Kobayashi,Y., Miyashita, T., Hashizume, M., Fujie, M. G., 2013, Real-Time Temperature Control System Based on the Finite ElementMethod for Liver Radiofrequency Ablation: Effect ofthe Time Interval on Control, Engineering in Medicine andBiology Society (EMBC), 35th Annual International Conferenceof the IEEE, 392-396.

Jaramillo, A.E., Boulanger, P, Prieto, F., 2011, On-line 3-D systemfor the inspection of deformable parts, The InternationalJournal of Advanced Manufacturing Technolology, 57,1053-1063.

Kim, S. K., Cho, D. W., 1997, Real-Time Estimation of TemperatureDistribution in a Ball-Screw System, InternationalJournal of Machine Tools and Manufacture, 37, 4, 451-464.

Kuehner, J., 2008, Expert .NET Micro Framework, Apress,Berkeley.Kusiak, J., Sztangret, Ł., Pietrzyk, M., 2015, Effective strategiesof metamodelling of industrial metallurgical processes, Advancesin Engineering Software, 89, 90-97.

Kuś, W., Burczyński, T., 2008, Parallel bioinspired algorithms inoptimization of structures, Lecture Notes on ComputationalScience, 4967, 1285-1292.

Kuś, W, Długosz, A., Burczyński, T., 2011, OPTIM – library ofbioinspired optimization algorithms in engineering applications,Computer Methods in Materials Science, 11, 9-15.

Lapeer, R.J., Gasson, P.D., Karri, V., 2010, Simulating plasticsurgery: From human skin tensile tests, through hyperelasticfinite element models to real-time haptics, Progress in Biophysicsand Molecular Biology, 103, 208-216.

Mrozek, A., Kuś, W., Burczyński, T., 2010, Searching of stableconfigurations of nanostructures using computational intelligencemethods, Czasopismo Techniczne. Mechanika, 107,4-M, 85-97.

Mucha, W., 2016, Real-time hybrid simulation using materialstesting machine and FEM, Advances in Mechanics: Theoretical,Computational and Interdisciplinary Issues, Proc. ofthe 3rd Polish Congress of Mechanics (PCM) and 21st InternationalConference on Computer Methods in Mechanics(CMM), Gdańsk, 419-422.

Myers, R. H., Montgomery, D. C., 1995, Response Surface Methodology;Process and Product Optimization Using DesignedExperiments, Wiley, New York.

Pietrzyk, M., Kuziak, R., 1999, Coupling the thermo-mechnicalfinite-element approach with phase transformation, ComputationalMaterials Science, 44, 783-791Pietrzyk, M., Kusiak, J., Kuziak, R., Zalecki, W., 2009, Optimizationof laminar cooling of hot rolled DP steels, XXVIII VerformungskundlichesKolloquium, Planneralm, 285-294.

Pietrzyk, M., Madej, Ł., Rauch, Ł., Gołąb, R., 2010, Multiscalemodeling of microstructure evolution during laminar coolingof hot rolled DP steel, Achieves of Civil and MechnicalEngineering,10, 57-67.

Schraudolph, N. N, 1999, A fast, compact approximation of theexponential function, Neural Computation, 11, 853-862.

Shin, E., 2000, Real-time recovery of impact force based on finiteelement analysis, Computers and Structures, 76, 621-635.

Vigneron, L. M., Verly, J. G., Warfield, S. K., 2004, ModellingSurgical Cuts, Retractions, and Resections via Extended FiniteElement Method, MICCAI, 2004, LNCS 3217, Springer-Verlag Berlin Heidelberg, 311-318.

Yiu, J., 2014, The Definitive Guide to ARM Cortex-M3 and Cortex-M4 Processors, Third Edition, Elsevier.Zhou, J., Wang, B., Lin, J., Fu, L., 2013, Optimization of an aluminumalloy anti-collision side beam hot stamping processusing a multi-objective genetic algorithm, Archives of Civiland Mechanical Engineering, 13, 401-411.