Modelling information for the burnishing process in a cyber-physical production system

Patalas-Maliszewska, Justyna ; Posdzich, Marco ; Skrzypek, Katarzyna

International Journal of Applied Mathematics and Computer Science, Tome 32 (2022) no. 3, pp. 345-354.

Résumé

Currently, the manufacturing management board applies technologies in line with the concept of Industry 4.0. Cyber-physical production systems (CPSs) mean integrating computational processes with the corresponding physical ones, i.e., allowing work at the operational level and at the strategic level to run side by side. This paper proposes a framework to collect data and information from a production process, namely, the burnishing one, in order to monitor real-time deviations from the correct course of the process and thus reduce the number of defective products within the manufacturing process. The proposed new solutions consist of (i) the data and information of the production process, acquired from sensors, (ii) a predictive model, based on the Hellwig method for errors in the production process, relying on indications of a machine status, and (iii) an information layer system, integrating the process data acquired in real time with the model for predicting errors within the production process in an enterprise resource planning (ERP) system, that is, the business intelligence module. The possibilities of using the results of research in managerial practice are demonstrated through the application of an actual burnishing process. This new framework can be treated as a solution which will help managers to monitor the production flow and respond, in real time, to interruptions.

Détail

Keywords: cyber-pysical production system, production process, burnishing process, enterprise resource planning system
Mots-clés : cyberfizyczny system produkcji, proces produkcji, proces nagniatania, system planowania zasobów przedsiębiorstwa

Bibliographie

[1] [1] Alcácer, V. (2019). Scanning the Industry 4.0: A literature review on technologies for manufacturing systems, Engineering Science and Technology, an International Journal 22(3): 899–919.

[2] [2] Alpar, P. and Schulz, M. (2016). Self-service business intelligence, Business and Information Systems Engineering 58: 151–155, DOI: 10.1007/s12599-016-0424-6.

[3] [3] Borkowski, B., Dudek, H. and Szczęsny, W. (2017). Econometrics: Selected Issues, PWN,Warsaw, (in Polish).

[4] [4] Broy, M. (2010). Cyber-Phisical Systems. Innovation Durch Software-Intensive Eingebettete Systeme, Springer, Munich.

[5] [5] Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., Wang, H., Bian, H., Zhang, S., Pradhan, B. and Ahmad, B. (2020). Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods, Science of the Total Environment 701: 134979, DOI: 10.1016/j.scitotenv.2019.134979.

[6] [6] Chomienne, V., Valiorgue, F., Rech, J. and Verdu, C. (2016). Influence of ball burnishing on residual stress profile of a 15-5PH stainless steel, CIRP Journal of Manufacturing Science and Technology 13: 90–96.

[7] [7] DAT (2016). Kompetenzentwicklungsstudie Industrie 4.0: Erste Ergebnisse und Schlussfolgerungen, Acatech, Munich, https://www.acatech.de/publikation/kompetenzentwicklungsstudie-industrie-4-0-erste-ergebnisse-und-schlussfolgerungen/.

[8] [8] de Lacalle, L.N., Rodríguez, A., Lamikiz, A., Celaya, A. and Alberdi, R. (2011). Five-axis machining and burnishing of complex parts for the improvement of surface roughness, Materials and Manufacturing Processes 26(8): 997–1003.

[9] [9] Dumitrescu, R., Gausemeier, J. and Kühn, A. (2015). Auf dem Weg zu Industrie 4.0: Erfolgsfaktor Referenzarchitektur, Technical report TR-47, it‘s OWL Clustermanagement GmbH (Hrsg.), Paderborn, https://www.its-owl.de/fileadmin/PDF/Informationsmaterialien/2015-Auf_dem_Weg_zu_Industrie_4.0_Erfolgsfaktor_Referenzarchitektur.pdf.

[10] [10] Farzaneh, M., Isaai, M., Arasti, M. and Mehralian, G. (2018). A framework for developing business intelligence systems: A knowledge perspective, Management Research Review 41 (12): 1358–1374, DOI: 10.1108/MRR-01-2018-0007.

[11] [11] Hassan, A. and Maqableh, A. (2000). The effects of initial burnishing parameters on non-ferrous components, Journal of Materials Processing Technology 102(1–3): 115–121.

[12] [12] Jaworski, M. (2018). Regression function and noise variance tracking methods for data streams with concept drift, International Journal of Applied Mathematics and Computer Science 28(3): 559–567, DOI: 10.2478/amcs-2018-0043.

[13] [13] Jeehyeong, K., Guejong, J. and Jongpil, J. (2019). A novel CPPS architecture integrated with centralized OPC UA server for 5G-based smart manufacturing, Procedia Computer Science 155: 113–120.

[14] [14] Kagermann, H., Wahlster,W. and Helbig, J. (2013). Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0, Technical report, Acatech, Frankfurt/Main, https://www.din.de/blob/76902/e8cac883f42bf28536e7e8165993f1fd/recommendationsfor-implementing-industry-4-0-data.pdf.

[15] [15] Kowalik, P. (2012). The use of spreadsheets to select response variables using the information capacity index method (Hellwig’s method), in Z.E. Zieliński (Ed), The Role of Informatics in the Economic and Social Sciences: Innovation and Interdisciplinary Implications, Vol. 2, WSH Publishing House, Kielce, pp. 168–178, (in Polish).

[16] [16] Krupitzer, C., Muller, S., Lesch, V., Zufle, M., Edinger, J., Lemken, A., Schafer, D., Kounev, S. and Becker, C. (2020). A survey on human machine interaction in industry 4.0, ArXiv: abs/2002.01025.

[17] [17] Meissner, H. and Aurich, J.C. (2019). Implications of cyber-physical production systems on integrated process planning and scheduling, Procedia Manufacturing 28: 167–173.

[18] [18] Mourtzis, D. (2020). Simulation in the design and operation of manufacturing systems: State of the art and new trends, International Journal of Production Research 58(7): 1927–1949.

[19] [19] Posdzich, M., Stöckmann, R., Witt, M. and Putz, M. (2010). Determination of surface shape deviation by using force-controlled burnishing, Procedia CIRP 93: 1275–1280.

[20] [20] Rojas, R., Rauch, E., Vidoni, R. and Matt, D.T. (2017). Enabling connectivity of cyber-physical production systems: A conceptual framework, Procedia Manufacturing 11: 822–829.

[21] [21] Roldán, J., Crespo, E. and Martín-Barrio, A. (2019). A training system for Industry 4.0 operators in complex assemblies based on virtual reality and process mining, Robotics and Computer-Integrated Manufacturing 59: 305–316, DOI: 10.1016/j.rcim.2019.05.004.

[22] [22] Tonelli, F., Demartini, M., Pacella, M. and Lala, R. (2021). Cyber-physical systems (CPS) in supply chain management: From foundation to practical implementation, Procedia CIRP 99: 598–603.

[23] [23] Trabesinger, S., Pichler, R., Schall, D. and Gfrerer, R. (2019). Connectivity as a prior challenge in establishing CPPS on basis of heterogeneous IT-software environments, Procedia Manufacturing 31: 370–376.

[24] [24] Tutunea, M. and Rus, R. (2012). Business intelligence solutions for SME’s, Procedia Economics and Finance 3: 865–870.

[25] [25] Wang,W., Zhang, Y. and Zhong, R. (2020). A proactive material handling method for CPS enabled shop-floor, Robotics and Computer-Integrated Manufacturing 61: 101849, DOI: 10.1016/j.rcim.2019.101849.

[26] [26] Wojnakowski, M., Wiśniewski, R., Bazydło, G. and Popławski, M. (2021). Analysis of safeness in a Petri net-based specification of the control part of cyber-physical systems, International Journal of Applied Mathematics and Computer Science 31(4): 647–657, DOI: 10.34768/amcs-2021-0045.

[27] [27] Yli-Ojanpera, M., Sierla, S., Papakonstantinou, N. and Vyatkin, V. (2019). Adapting an agile manufacturing concept to the reference architecture model Industry 4.0: A survey and case study, Journal of Industrial Information Integration 15: 147–160, DOI: 10.1016/j.jii.2018.12.002.