journal article·2021-04-06Open access

Mathematical Modeling of the Manufacturing Sector’s Dominant Part as a Base for Automation

Elena Grešová, Jozef Svetlík

Applied Sciences· Volume 11· Issue 7

Abstract

The current great expansion of automation and robotics affects a multiplicity of various fields. A prominent example is industry, where the different manufacturing processes and technologies embrace a certain level of automation and robotics. Thus, the use of robotics and automation implementation is part of a rapidly rising trend in industry. The presented paper deals with the manufacturing segment in the context of automation. The main subject is data analysis, with our own subsequent model building and final realization of the prediction corresponding to the machinery and electrical machinery sector as a highly relevant automation driver through the use of mathematical modeling. The design of the model is accompanied by optimization of the particular weights. Determination of the most suitable model is preceded by creating and testing a number of models to decide upon the final one. The construction of the mathematical model pursues the aim of making predictions relating to the machinery and electrical machinery sector for the specific national economy as the concluding investigation step. We apply a polynomial approximation as the research method. The software selected for our purposes is Matlab.
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Research context

OpenAlex topics: Manufacturing Process and Optimization · Mechanical and Thermal Properties Analysis · Advanced Manufacturing and Logistics Optimization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Control and Informatization of Production Processes, Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Kosice, 04200 Kosice, Slovakia

Department of Manufacturing Machinery and Robotics, Faculty of Mechanical Engineering, Technical University of Kosice, 04200 Kosice, Slovakia

References (49)

Show source-provided references
1. Modeling within national economy using industry-oriented indicators: Evidence from Czech Republic10.17973/MMSJ.2020_06_2019028 ↗
2. Economic Impact of Industrial Segment in the Selected Country
3. Cohen, Y., Shoval, S., Faccio, M., and Minto, R. (2021). Deploying cobots in collaborative systems: Major considerations and productivity analysis. Int. J. Prod. Res.10.1080/00207543.2020.1870758 ↗
4. Design and management of digital manufacturing and assembly systems in the Industry 4.0 era10.1007/s00170-019-04595-0 ↗
5. Non-contact Study of Surfaces Created Using the AWJ Technology
6. Control Model for Collaborative Manufacturing: An Integrated Opened Framework for Human-Robot Collaboration10.1007/978-3-030-55807-9_46 ↗
7. Boschetti, G., Bottin, M., Faccio, M., and Minto, R. (2021). Multi-robot multi-operator collaborative assembly systems: A performance evaluation model. J. Intell. Manuf.10.1007/s10845-020-01714-7 ↗
8. Designing dynamic and collaborative automation and robotics software systems10.1109/TII.2017.2786280 ↗
9. Software-defined industrial internet of things in the context of industry 4.0
10. Design, engineering and testing of an innovative adaptive automation assembly system10.1108/AA-06-2019-0103 ↗
11. Bortolini, M., Faccio, M., Galizia, F.G., Gamberi, M., and Pilati, F. (2021). Adaptive Automation Assembly Systems in the Industry 4.0 Era: A Reference Framework and Full-Scale Prototype. Appl. Sci., 11.10.3390/app11031256 ↗
12. Collaborative and traditional robotic assembly: A comparison model10.1007/s00170-018-03247-z ↗
13. A framework of indicators for assessing construction automation and robotics in the sustainability context10.1016/j.jclepro.2018.02.053 ↗
14. The future of construction automation: Technological disruption and the upcoming ubiquity of robotics10.1016/j.autcon.2015.07.022 ↗
15. Construction automation and robotics for high-rise buildings over the past decades: A comprehensive review10.1016/j.aei.2019.100989 ↗
16. Verification of floor planarity by trigonometrical measurement of heights on a 5-storey monolithic building10.3846/gac.2018.269 ↗
17. Oke, A., Aigbavboa, C., and Mabena, S. (2017). Effects of automation on construction industry performance. Second International Conference on Mechanics, Materials and Structural Engineering, Atlantis Press.10.2991/icmmse-17.2017.61 ↗
18. Jaluria, Y. (2018). Mathematical Modeling of Manufacturing Processes. Advanced Materials Processing and Manufacturing, Mechanical Engineering Series, Springer.10.1007/978-3-319-76983-7 ↗
19. Ren, W., Xu, J., Lian, Z., Yu, P., and Yu, H. (2020). Modeling and Experimental Study of the Localized Electrochemical Micro Additive Manufacturing Technology Based on the FluidFM. Materials, 13.10.3390/ma13122783 ↗
20. Kim, N., Yang, C., Lee, H., and Aluru, N.R. (2019). Spatial Uncertainty Modeling for Surface Roughness of Additively Manufactured Microstructures via Image Segmentation. Appl. Sci., 9.10.3390/app9061093 ↗
21. Liao, H., Deng, Q., and Wang, Y. (2017). Optimal Acquisition and Production Policy for End-of-Life Engineering Machinery Recovering in a Joint Manufacturing/Remanufacturing System under Uncertainties in Procurement and Demand. Sustainability, 9.10.3390/su9030338 ↗
22. Chiu, S.W., Kuo, J.-S., Chiu, V., and Chiu, Y.-S.P. (2016). Cost Minimization for a Multi-Product Fabrication-Distribution Problem with Commonality, Postponement, and Quality Assurance. Math. Comput. Appl., 21.10.3390/mca21030038 ↗
23. A Mathematical Programming Approach to the Optimal Sustainable Product Mix for the Process Industry10.3390/su71013085 ↗
24. Sentyakov, K., Peterka, J., Smirnov, V., Bozek, P., and Sviatskii, V. (2020). Modeling of Boring Mandrel Working Process with Vibration Damper. Materials, 13.10.3390/ma13081931 ↗
25. Orosz, T., Rassolkin, A., Kallaste, A., Arsenio, P., Panek, D., Kaska, J., and Karban, P. (2020). Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems. Appl. Sci., 10.10.3390/app10196653 ↗
26. Torreggiani, A., Bianchini, C., Davoli, M., and Bellini, A. (2019). Design for Reliability: The Case of Fractional-Slot Surface Permanent-Magnet Machines. Energies, 12.10.3390/en12091691 ↗
27. Kande, M., Isaksson, A.J., Thottappillil, R., and Taylor, N. (2017). Rotating Electrical Machine Condition Monitoring Automation—A Review. Machines, 5.10.3390/machines5040024 ↗
28. Nikitin, Y., Božek, P., and Peterka, J. (2020). Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support. Sensors, 20.10.3390/s20164429 ↗
29. Nguyen, T.K., Phung, L.X., and Bui, N.-T. (2020). Novel Integration of CAPP in a G-Code Generation Module Using Macro Programming for CNC Application. Machines, 8.10.3390/machines8040061 ↗
30. Dineva, A., Mosavi, A., Gyimesi, M., Vajda, I., Nabipour, N., and Rabczuk, T. (2019). Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification. Appl. Sci., 9.10.3390/app9235086 ↗
31. Yu, J., Yang, S., Kim, J., Lee, Y., Lim, K.-T., Kim, S., Ryu, S.-S., and Jeong, H. (2020). A Confidence Interval-Based Process Optimization Method Using Second-Order Polynomial Regression Analysis. Processes, 8.10.3390/pr8101206 ↗
32. Blume, C., Blume, S., Thiede, S., and Herrmann, C. (2020). Data-Driven Digital Twins for Technical Building Services Operation in Factories: A Cooling Tower Case Study. J. Manuf. Mater. Process., 4.10.3390/jmmp4040097 ↗
33. Cravero, C., and Ottonello, A. (2020). Uncertainty Quantification Methodologies Applied to the Rotor Tip Clearance Effect in a Twin Scroll Radial Turbine. Fluids, 5.10.3390/fluids5030114 ↗
34. Gamannossi, A., Amerini, A., Mazzei, L., Bacci, T., Poggiali, M., and Andreini, A. (2020). Uncertainty Quantification of Film Cooling Performance of an Industrial Gas Turbine Vane. Entropy, 22.10.3390/e22010016 ↗
35. Bhonsale, S., Munoz Lopez, C.A., and Van Impe, J. (2019). Global Sensitivity Analysis of a Spray Drying Process. Processes, 7.10.3390/pr7090562 ↗
36. Shi, W., Chen, P., Li, X., Ren, J., and Jiang, H. (2019). Uncertainty Quantification of the Effects of Small Manufacturing Deviations on Film Cooling: A Fan-Shaped Hole. Aerospace, 6.10.3390/aerospace6040046 ↗
37. Wang, W., Wang, J., Chen, Z., Wang, R., Lu, K., Sang, Z., and Ju, B. (2020). Research on Asymmetric Hysteresis Modeling and Compensation of Piezoelectric Actuators with PMPI Model. Micromachines, 11.10.3390/mi11040357 ↗
38. John, D.N., Schick, M., and Heuveline, V. (2019). Learning Model Discrepancy of an Electric Motor with Bayesian Inference. Proceedings, 33.10.3390/proceedings2019033011 ↗
39. Zheng, Z., Jing, Q., Xie, Y., and Zhang, D. (2019). An Investigation on the Forced Convection of Al2O3-water Nanofluid Laminar Flow in a Microchannel under Interval Uncertainties. Appl. Sci., 9.10.3390/app9030432 ↗
40. Lei, B., Chen, G., Liu, K., Wang, X., Jiang, X., Pan, J., and Shi, Q. (2019). Constitutive Analysis on High-Temperature Flow Behavior of 3Cr-1Si-1Ni Ultra-High Strength Steel for Modeling of Flow Stress. Metals, 9.10.3390/met9010042 ↗
41. (2020, September 25). World Integrated Trade Solution. Available online: https://wits.worldbank.org/.
42. World Bank Group (2020, September 25). Available online: https://www.worldbank.org/en/what-we-do.
43. World Trade Organization (2020, September 25). Available online: https://www.wto.org/english/thewto_e/thewto_e.htm.
44. Ministry of Foreign Affairs of the Republic of Lithuania (2020, September 25). Available online: https://urm.lt/default/en/foreign-policy/lithuania-in-the-region-and-the-world/lithuania-member-of-the-european-union/membership.
45. (2020, October 10). Polynomials. Available online: https://www.mathworks.com/help/matlab/polynomials.html?s_tid=srchtitle.
46. (2020, October 10). Matlab. Available online: https://www.mathworks.com/.
47. (2020, October 10). Curve Fitting Toolbox. Available online: https://www.mathworks.com/help/curvefit/.
48. (2020, October 10). Engineering Statistics Handbook, Available online: https://www.itl.nist.gov/div898/handbook/index.htm.
49. (2020, October 10). User’s Guide—Curve Fitting Toolbox for Use with Matlab. Available online: http://cda.psych.uiuc.edu/matlab_pdf/curvefit.pdf.