Comparative Analysis of the Production Process of a Flange-Type Product by the Hybrid and Traditional Method with the Use of Simulation Methods

• Autorzy: Terelak-Tymczyna Agnieszka , Bachtiak-Radka Emilia , Jardzioch Andrzej

Identyfikatory

DOI

10.12913/22998624/144309

• Języki publikacji: EN

Abstrakty

EN

In the era of Industry 4.0, the digitization of production processes is one of the important elements contributing to the reduction of uncertainty related to the implementation of new production methods. The worldwide epidemic situation and its constraints have resulted in supply chain continuity problems. These problems make enterprises look for the possibility of producing products that they need at the moment and which they cannot obtain from the market. In special cases, this may also apply to spare parts necessary to maintain the continuity of production. The main reason for research on comparing production processes is meeting the challenges related to the pandemic situation and problems in maintaining timeliness, flexibility, and continuity of the supply chain. The first stage of the research was to visualize the course of the process and determine the lead times for both production methods. For further analysis, a digital process model was used to compare the hybrid and the classical method to check the viability of the interchangeability of methods for the production process of the flange part. The interchangeability of production methods was dictated by problems related to the supply of components for the execution of orders. The article simulates the model for unit and small-lot production in batches of 10 and 100 pieces, considering such aspects as: order completion time, energy consumption of the process, production costs, taking into account the classic and hybrid methods. The conducted research was aimed at determining the profitability of the production of flange-type products by means of classical processing and hybrid and checking the interchangeability of production methods in accordance with quality requirements as well as reducing uncertainty with the implementation of new production systems in changing market conditions. The simulations show that the use of hybrid production is recommended for unit production. In the case of small-lot production, already with 10 items, production in the traditional process is 21% cheaper, and for the production of 100 items, the cost of traditional production is reduced by 33% compared to hybrid production.

Słowa kluczowe

EN

machining; hybrid machining; VSM; SLM; process simulation; cost of the production; energy analysis

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2022
• Tom: Vol. 16, no 1
• Strony: 231--242
• Opis fizyczny: Bibliogr. 31 poz., fig., tab.

Twórcy

autor

Terelak-Tymczyna Agnieszka

• Faculty of Mechanical Engineering and Mechatronics, Department of Production Management, West Pomeranian University of Technology in Szczecin, al. Piastów 19, 70-310 Szczecin, Poland

• https://orcid.org/0000-0002-6625-0067

autor

Bachtiak-Radka Emilia

• Faculty of Mechanical Engineering and Mechatronics, Department of Production Management, West Pomeranian University of Technology in Szczecin, al. Piastów 19, 70-310 Szczecin, Poland

• https://orcid.org/0000-0003-3366-0083

autor

Jardzioch Andrzej

• Faculty of Mechanical Engineering and Mechatronics, Department of Production Management, West Pomeranian University of Technology in Szczecin, al. Piastów 19, 70-310 Szczecin, Poland

• https://orcid.org/0000-0003-1285-8330

Bibliografia

• 1. Sarkis J. Supply chain sustainability: learning from the COVID-19 pandemic. IJOPM. 2020; 41(1): 63–73.
• 2. Sarkis J., Cohen M.J., Dewick P., Schröder P. A brave new world: Lessons from the COVID-19 pandemic for transitioning to sustainable supply and production. Resources, Conservation and Recycling. 2020; 159: 104894.
• 3. Kimura F., Thangavelu S.M., Narjoko D., Findlay C. Pandemic (COVID ‐19) Policy, Regional Cooperation and the Emerging Global Production Network. Asian Economic Journal. 2020; 34(1): 3–27.
• 4. Karmaker C.L., Ahmed T., Ahmed S., Ali S.M., Moktadir Md.A, Kabir G. Improving supply chain sustainability in the context of COVID-19 pandemic in an emerging economy: Exploring drivers using an integrated model. Sustainable Production and Consumption. 2021; 26: 411–427.
• 5. Chowdhury P., Paul S., Kaisar S., Abdul Moktadir Md. COVID-19 pandemic related supply chain studies: A systematic review. Transportation Research. Part E, Logistics and Transportation Review. 2021; 148: 102271–102271.
• 6. Chromjaková F., Bobák R., Hrušecká D. Production process stability – core assumption of INDUSTRY 4.0 concept. In: Proc of IOP Conference Series: Materials Science and Engineering. 2017: 215
• 7. Geissdoerfer M., Vladimirova D., Evans S. Sustainable Business Model Innovation: A Review. Journal of Cleaner Production. 2018; 198: 401–416.
• 8. Breier M., Kallmuenzer A., Clauss T., Gast J., Kraus S., Tiberius V. The role of business model innovation in the hospitality industry during the COVID-19 crisis. International Journal of Hospitality Management. 2021; 92: 102723.
• 9. Haaker T., Ly P.T., Nguyen-Thanh N., Nguyen H.T. Business model innovation through the application of the Internet-of-Things: A comparative analysis. Journal of Business Research. 2021; 126: 126–136.
• 10. Martinez-Hernandez E., Leung Pah Hang M.Y., Leach M., Yang A. A framework for modeling local production systems with techno-ecological interactions: modeling local techno-ecological interactions. Journal of Industrial Ecology. 2017; 21(4): 815–828.
• 11. Hulkó G., Belavý C., Ondrejkovič K., Bartalský L., Bartko M. Control of technological and production processes as distributed parameter systems based on advanced numerical modeling. Control Engineering Practice. 2017; 66: 23–38.
• 12. Zakoldaev D.A., Shukalov A.V., Zharinov I.O., Zharinov O.O. The e-objects family in simulation of mechatronic facilities. IOP Conf Ser: Mater Sci Eng. 2020; 862: 042003.
• 13. Akpan I.J., Shanker M. The confirmed realities and myths about the benefits and costs of 3D visualization and virtual reality in discrete event modeling and simulation: A Descriptive Meta-Analysis of Evidence from Research and Practice Computers & Industrial Engineering. 112: 197–211.
• 14. Goh M., Goh Y. M. Lean production theory-based simulation of modular construction processes. Automation in Construction. 2019; 101: 227–244.
• 15. Lugaresi G., Aglio G., Folgheraiter F., Matta A. Real-time validation of digital models for manufacturing systems: A novel signal-processing-based approach. In: IEEE 15th International Conference on Automation Science and Engineering (CASE). Vancouver, BC, Canada. 2019: 450–455.
• 16. Lugaresi G., Matta A. Real-time simulation in manufacturing systems: Challenges and research directions. In: 2018 Winter Simulation Conference (WSC). Gothenburg, Sweden 2018, 3319–3330.
• 17. Malega P., Gazda V., Rudy V. Optimization of production system in plant simulation. SIMULATION. August 2021.
• 18. Gola A., Pastuszak Z., Relich M., Sobaszek Ł., Szwarc E. Scalability analysis of selected structures of a reconfigurable manufacturing system taking into account a reduction in machine tools reliability. Eksploatacja i Niezawodność – Maintenance and Reliability. 2021; 23(2): 242–252.
• 19. Kikolski M. Study of production scenarios with the use of simulation models, Procedia Engineering. 2017; 182: 321–328.
• 20. Kłos S., Patalas-Maliszewska J. An analysis of the efficiency of a parallel-serial manufacturing system using simulation. In: Burduk A., Chlebus E., Nowakowski T., Tubis A. (eds) Intelligent Systems in Production Engineering and Maintenance. Advances in Intelligent Systems and Computing. 2018; 835: 32–43.
• 21. Grabowik C., Ćwikła G., Kalinowski K., Kuc M. A comparison analysis of the computer simulation results of a real production system. In 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019), Seville, Spain 2019, 344–354.
• 22. Zhang L., Zhou L., Ren L., Laili Y. Modeling and simulation in intelligent manufacturing. Computers in Industry. 2019; 112: 103123.
• 23. Strnad D., Fedorko G., Molnár V., Fialek P. Simulations as an assessment tool for the feasibility of logistics innovations motivated by the emergence of supply chain risk. Advances in Science and Technology Research Journal. 2021; 15(3): 66–75.
• 24. Roszkowski A., Piórkowski P., Skoczyński W., Borkowski W., Jankowski T. Study on the impact of cutting tool wear on machine tool energy consumption. Advances in Science and Technology Research Journal. 2020; 14(3): 158–164.
• 25. Liu D., Wang W., Wang L. Energy-efficient cutting parameters determination for NC machining with specified machining accuracy. Procedia CIRP. 2017; 61: 523–528.
• 26. Al Hazza M.H.F., Adesta, E.Y.T., Riza, M., Suprianto, M.Y. Power consumption optimization in CNC turning process using multi objective genetic algorithm. AMR. 2012; 576: 95–98.
• 27. Datta R., Majumder A. Optimization of turning process parameters using Multi-objective Evolutionary algorithm. In: Proc of IEEE Congress on Evolutionary Computation, Barcelona, Spain 2010, 1–6.
• 28. Newman ST., Nassehi A., Imani-Asrai R., Dhokia V. Energy efficient process planning for CNC machining. CIRP Journal of Manufacturing Science and Technology. 2012; 5(2): 127–136.
• 29. Zhang H., Deng Z., Fu Y., Lv L., Yan C. A process parameters optimization method of multi-pass dry milling for high efficiency, low energy and low carbon emissions. Journal of Cleaner Production. 2017; 148: 174–184.
• 30. Shin S-J., Woo J., Rachuri S. Energy efficiency of milling machining: Component modeling and online optimization of cutting parameters. Journal of Cleaner Production. 2017; 161: 12–29.
• 31. Grzesiak D., Terelak-Tymczyna A., Bachtiak-Radka E., Filipowicz K. Technical and economic implications of the combination of machining and additive manufacturing in the production of metal parts on the example of a disc type element. In: Królczyk G., Niesłony P., Królczyk J. (eds) Industrial Measurements in Machining. IMM 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. 2020; 128–137.