AutoUniMo

Automotive Production Engineering Unified Perspective based on Data Mining Methods and Virtual Factory Model

Deliverables

  • D1.1 MES for Short Series Production - Functional Requirements and Selection of Best Available IT Technologies and Data Mining Methods
  • D1.2 Software Model for Short Series Production MES – Functional Model for Production Scheduling and Product Genealogy Tracing
  • D1.3 Software Framework for Short Series Production MES with Production Scheduling, Product Genealogy Tracing, Production Quality Management and Maintenance Management Functionalities
  • D2.1 Virtual Factory Components which Support Energy Efficient Production Design for Automotive Industry -methodology and supporting software tools.
  • D3.1 Strategies for Artificial Intelligence in Advanced Driver Assistance Systems
  • D3.2 Research Environment for Artificial Intelligence in Advanced Driver Assistance Systems Support
  • D4.1 Software Framework for Digital Factory Model for Automotive Production
  • D5.1 International Seminar on Emerging Technologies in Automotive Area, Germany
  • D5.2 International Seminar on Data Mining and Artificial Intelligence Methods for Automotive Systems, Poland
  • D5.3 International Conference on Data Mining and Artificial Intelligence Methods Applied for Automotive Systems – Gliwice, Poland
  • D5.4 International Conference on Virtual Factory in Automotive Production Systems Engineering – Ingolstadt, Germany

Milestones

  • MS1 Consortium Agreement Signature
  • MS2 AutoUniMo Holistic Approach Concept
  • MS3 Software Mockup Prototype
  • MS4 Final Software Prototype
  • MS5 Project end

AutoUniMo publications on researchgate

Publications in Journals:

[1]       R. Cupek, A. Ziebinski, D. Zonenberg, and M. Drewniak, “Determination of the machine energy consumption profiles in the mass-customised manufacturing,” International Journal of Computer Integrated Manufacturing, pp. 1–25, Jun. 2017. IF 1.949.

[2]       R. Cupek, A. Ziebinski, L. Huczala, and H. Erdogan, “Agent-based manufacturing execution systems for short-series production scheduling,” Computers in Industry, vol. 82, pp. 245–258, 2016. IF 2,691

[3]       A. Ziebinski and S. Swierc, “Soft Core Processor Generated Based on the Machine Code of the Application,” Journal of Circuits, Systems and Computers, vol. 25, no. 04, p. 1650029, Apr. 2016. IF 0,481

Publications cited in WoS

[4]       A. Ziebinski, R. Cupek, and M. Nalepa, “Obstacle avoidance by a mobile platform using an ultrasound sensor,” in Computational Collective Intelligence, B. Trawiński, Ed. Cham: Springer International Publishing, 2017.

[5]       A. Ziebinski, M. Bregulla, M. Fojcik, and Kłak, “Monitoring and controlling speed for an autonomous mobile platform based on the Hall sensor,” in Computational Collective Intelligence, B. Trawiński, Ed. Cham: Springer International Publishing, 2017.

[6]       D. Grzechca, A. Ziebinski, and P. Rybka, “Enhanced reliability of ADAS sensors based on the  observation of the power supply current and neural network application,” in Computational Collective Intelligence, B. Trawiński, Ed. Cham: Springer International Publishing, 2017.

[7]       R. Cupek, A. Ziebinski, M. Drewniak, and M. Fojcik, “Feasibility Study of the Application of OPC UA Protocol for the Vehicle-to-Vehicle Communication,” in Computational Collective Intelligence: 9th International Conference, ICCCI 2017, Nicosia, Cyprus, September 27-29, 2017, Proceedings, Part II, N. T. Nguyen, G. A. Papadopoulos, P. Jędrzejowicz, B. Trawiński, and G. Vossen, Eds. Cham: Springer International Publishing, 2017, pp. 282–291.

[8]       R. Cupek, A. Ziebinski, M. Drewniak, and M. Fojcik, “Application of OPC UA Protocol for the Internet of Vehicles,” in Computational Collective Intelligence: 9th International Conference, ICCCI 2017, Nicosia, Cyprus, September 27-29, 2017, Proceedings, Part II, N. T. Nguyen, G. A. Papadopoulos, P. Jędrzejowicz, B. Trawiński, and G. Vossen, Eds. Cham: Springer International Publishing, 2017, pp. 272–281.

[9]       D. Grzechca, K. Tokarz, K. Paszek, and D. Poloczek, “Using MEMS sensors to enhance positioning when the GPS signal disappears,” in Computational Collective Intelligence, B. Trawiński, Ed. Cham: Springer International Publishing, 2017.

[10]     M. Bregulla, S. Schrittenloher, J. Piekarz, and M. Drewniak, “Model of a Production Stand Used for Digital Factory Purposes,” in Computational Collective Intelligence: 9th International Conference, ICCCI 2017, Nicosia, Cyprus, September 27-29, 2017, Proceedings, Part II, N. T. Nguyen, G. A. Papadopoulos, P. Jędrzejowicz, B. Trawiński, and G. Vossen, Eds. Cham: Springer International Publishing, 2017, pp. 195–204.

[11]     M. Bregulla and F. Meltzer, “Improving the Engineering Process in the Automotive Field Through AutomationML,” in Computational Collective Intelligence: 9th International Conference, ICCCI 2017, Nicosia, Cyprus, September 27-29, 2017, Proceedings, Part II, N. T. Nguyen, G. A. Papadopoulos, P. Jędrzejowicz, B. Trawiński, and G. Vossen, Eds. Cham: Springer International Publishing, 2017, pp. 205–214.

[12]     M. Drewniak, K. Tokarz, and M. Rędziński, “ADAS device operated on CAN bus using PiCAN module for Raspberry Pi,” in Computational Collective Intelligence, B. Trawiński, Ed. Cham: Springer International Publishing, 2017.

[13]     R. Cupek, J. Duda, D. Zonenberg, L. Chlopas, G. Dziedziel, and M. Drewniak, “Data Mining Techniques for Energy Efficiency Analysis of Discrete Production Lines,” in Computational Collective Intelligence: 9th International Conference, ICCCI 2017, Nicosia, Cyprus, September 27-29, 2017, Proceedings, Part II, N. T. Nguyen, G. A. Papadopoulos, P. Jędrzejowicz, B. Trawiński, and G. Vossen, Eds. Cham: Springer International Publishing, 2017, pp. 292–301.

[14]     R. Cupek, A. Ziebinski, and M. Fojcik, “An ontology model for communicating with an Autonomous Mobile Platform,” Kozielski, Kasprowski, Mrozek, Małysiak-Mrozek, Kostrzewa (eds.) BDAS 2017 CCIS Springer, 2017.

[15]     I. Postanogov and T. Jastrząb, “Ontology Reuse as a Means for Fast Prototyping of New Concepts,” presented at the International Conference: Beyond Databases, Architectures and Structures, 2017, pp. 273–287.

[16]     D. Krasnokucki, G. Kwiatkowski, and T. Jastrząb, “A New Method of XML-Based Wordnets’ Data Integration,” presented at the International Conference: Beyond Databases, Architectures and Structures, 2017, pp. 302–315.

[17]     M. Skrzewski and P. Rybka, “The Possibilities of System’s Self-defense Against Malicious Software,” in Computer Networks, vol. 718, P. Gaj, A. Kwiecień, and M. Sawicki, Eds. Cham: Springer International Publishing, 2017, pp. 144–153.

[18]     R. Cupek, A. Ziebinski, and M. Drewniak, “An OPC UA server as a gateway that shares CAN network data and engineering knowledge,” 18th IEEE International Conference on Industrial Technology, 2017.

[19]     A. Ziebinski, R. Cupek, H. Erdogan, and S. Waechter, “A Survey of ADAS Technologies for the Future Perspective of Sensor Fusion,” in Computational Collective Intelligence, vol. 9876, N. T. Nguyen, L. Iliadis, Y. Manolopoulos, and B. Trawiński, Eds. Cham: Springer International Publishing, 2016, pp. 135–146.

[20]     R. Cupek, H. Erdogan, L. Huczala, U. Wozar, and A. Ziebinski, “Agent Based Quality Management in Lean Manufacturing,” in Computational Collective Intelligence, vol. 9329, M. Núñez, N. T. Nguyen, D. Camacho, and B. Trawiński, Eds. Cham: Springer International Publishing, 2015, pp. 89–100.

[21]     R. Cupek and L. Huczala, “OData for service-oriented business applications: Comparative analysis of communication technologies for flexible Service-Oriented IT architectures,” presented at the IEEE International Conference on Industrial Technology (ICIT), Seville, Spain, 2015, pp. 1538–1543.

WoS publications in print

[22]     A. Ziebinski, R. Cupek, D. Grzechca, and L. Chruszczyk, “Review of Advanced Driver Assistance Systems (ADAS),” presented at the 13th International Conference of Computational Methods  in Sciences and Engineering (ICCMSE), 2017.

[23]     R. Cupek, A. Ziebinski, and M. Drewniak, “Ethernet-based test stand for a CAN network,” presented at the 13th International Conference of Computational Methods  in Sciences and Engineering (ICCMSE), 2017.

Other publications:

[24]     A. Ziębiński, R. Cupek, M. Kruk, M. Drewniak, and H. Erdogan, “Lidar technology in general purpose applications,” Studia Informatica, vol. 37, no. 4A, pp. 15–32, 2016.

[25]     P. Rybka et al., “Power management and sensors handling on the autonomous mobile,” Studia Informatica, vol. 37, 2016.

[26]     W. Czernek, W. Margas, R. Wyżgolik, S. Budzan, A. Ziębiński, and R. Cupek, “GPS and ultrasonic distance sensors for Autonomous Mobile Platform,” Studia Informatica, vol. 37, 2016.

[27]     K. Tokarz, P. Czekalski, and R. Raszka, “Raspberry Pi based lap counter for amateur car racing,” Studia Informatica, vol. 37, no. 4A, pp. 7–13, 2016.

[28]     U. Wozar, H. Erdogan, R. Cupek, and S. Ziemek, “Application of ISA95 data models in manufacturing execution systems for lean production,” Studia Informatica, vol. 37, no. 4A, pp. 33–50, 2016.

[29]     R. Cupek, A. Ziebinski, L. Huczala, D. Grossmann, and M. Bregulla, “Object-Oriented Communication Model for an Agent-Based Inventory Operations Management,” in INTELLI 2015, St. Julians, Malta, 2015, pp. 80–85.

[30]     A. Ziębiński, R. Cupek, P. Piękoś, and L. Huczala, “A frame filter IP core for RT-Ethernet monitoring,” Przeglad Elektrotechniczny, vol. 90, no. 10, pp. 219–225, 2014.

Patent application

[31]     D. Grzechca, A. Ziębiński, R. Cupek, and P. Rybka, “P.422834 Method and system for identifying electronic ADAS devices (Sposób i układ do  identyfikacji elektronicznych podsystemów ADAS),” Sep-2017.