Application of Fuzzy-AHP and COPRAS in the selection of the best alternative for high speed machining of thin structures in Al 5083 aluminum alloys

Article Sidebar

portada volumen 33 numero 2
PDF (Spanish) MHT (Spanish)
Published : 2021-11-26
DOI : https://doi.org/10.37815/rte.v33n2.836
Keywords :
COPRAS, AHP Fuzzy, high speed machining, artificial intelligent
Hiovanis Castillo Pantoja
Angel Infante Haynes
Roberto Perez Rodriguez
Ricardo Avila Rondon
Abstract

The following research shows a methodology that combines the multi-criteria method COPRAS and the artificial intelligence method AHP Fuzzy, which seeks to improve decision making within the planning processes in machining shops. The first method allows us to determine the most important criterion to be fulfilled as a manufacturing requirement; the second method seeks the selection of the best alternative, with the values for high speed machining that will allow the manufacturing of the rectangular piece of aluminum alloy 5083. For the multi-criteria analysis, the parameters selected for the machining process of thin-structured aluminum parts are: surface roughness and part deformation. By applying the Fuzzy-AHP method, it is determined that the most important criterion is the deformation of the part in the thin structure. With the evaluation of the criteria, COPRAS was applied and the result of the utility index determined that alternative three is the best, therefore, by implementing the input parameters: S = 15000 rpm, doc= 0.30 mm, ts= 7.0 mm, F= 9000 m/min, surface quality and low deformation of the part is guaranteed. We conclude that the Fuzzy-AHP and COPRAS methodology is an excellent tool, with low cost and good reliability, as a solution to be applied in machine shops to improve decision making in process planning.

DOWNLOADS
Download data is not yet available.
How to Cite
Pantoja, H., Infante Haynes, A., Perez Rodriguez, R., & Avila Rondon, R. L. (2021). Application of Fuzzy-AHP and COPRAS in the selection of the best alternative for high speed machining of thin structures in Al 5083 aluminum alloys. Revista Tecnológica - ESPOL, 33(2), 109-121. https://doi.org/10.37815/rte.v33n2.836
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

Bhowmik, S., Jagadish, & Gupta, K. (2019). Modeling and Optimization of Advanced Manufacturing Processes. https://doi.org/10.1007/978-3-030-00036-3.

Ighravwe, D. E., & Oke, S. A. (2020). A two-stage fuzzy multi-criteria approach for proactive maintenance strategy selection for manufacturing systems. SN Applied Sciences, 2(10), 1683. https://doi.org/10.1007/s42452-020-03484-6

Isik, N. K. a. A. (2016). Integration of Macbeth and copras methods to select air compressor for a textile company. Decision Science Letters, 5, 381–394, Article 3. https://doi.org/ http://dx.doi.org/10.5267/j.dsl.2016.2.003.

Jasiulewicz-Kaczmarek, M., Antosz, K., Wyczółkowski, R., Mazurkiewicz, D., Sun, B., Qian, C., & Ren, Y. (2021). Application of MICMAC, Fuzzy AHP, and Fuzzy TOPSIS for Evaluation of the Maintenance Factors Affecting Sustainable Manufacturing. Energies, 14(5), 1436. https://doi.org/10.3390/en14051436.

Kaori Ota, N. T., Hayao Miyagi (2008). Group Decision-Making Model in Fuzzy AHP Based on the Variable Axis Method. In T. I. o. E. E. o. Japan (Ed.), (pp. 6).

Keshavarz-Ghorabaee, M., Govindan, K., Amiri, M., Zavadskas, E. K., & Antuchevičienė, J. (2019). An integrated type-2 fuzzy decision model based on WASPAS and SECA for evaluation of sustainable manufacturing strategies. Journal of Environmental Engineering and Landscape Management, 27(4), 187-200. https://doi.org/10.3846/jeelm.2019.11367.

Mehdi Ajalli, H. A., Abdolkarim Mohammadi Balani, Mahdi Rezaei. (2017). Application of Fuzzy AHP and COPRAS to Solve the Supplier Selection Problems. International Journal of Supply Chain Management, 6, 8.

Paker, F. A., Alppay, C., & Sertyeşilişik, B. (2018). Use of the AHP Methodology in Vehicle Design Process Dynamics: Determination of the Most Effective Concept Phases for the New Automotive Product. Journal of Transportation Technologies, 08(04), 312-330. https://doi.org/10.4236/jtts.2018.84017.

Patil, R. B., & Kothavale, B. S. (2020). Criticality Analysis of CNC Turning Center Using Analytic Hierarchy Process. In Reliability and Risk Assessment in Engineering (pp. 61-76). https://doi.org/10.1007/978-981-15-3746-2_6

Sen, B., Hussain, S. A. I., Gupta, A. D., Gupta, M. K., Pimenov, D. Y., & Mikołajczyk, T. (2020). Application of Type-2 Fuzzy AHP-ARAS for Selecting Optimal WEDM Parameters. Metals, 11(1), 42. https://doi.org/10.3390/met11010042

Singh, R. P., Tyagi, M., & Kataria, R. (2019). Selection of the Optimum Hole Quality Conditions in Manufacturing Environment Using MCDM Approach: A Case Study. In Operations Management and Systems Engineering (pp. 133-152). https://doi.org/10.1007/978-981-13-6476-1_8

Sivam Sundarlingam Paramasivam, S. S., Kumaran, D., Loganathan, G. B., Saravanan, K., Rajendran, R., & Sriram, H. (2019). Development and Influence of Setting Process Variables in Single Point Incremental Sheet Metal Forming of AA 8011 Using Complex Proportional Assessment and ANOVA. SAE Technical Paper Series,

Sofuoğlu, M. A. A new hybrid decision-making strategy of cutting fluid selection for manufacturing environment. Sādhanā, 46(2), 94. https://doi.org/10.1007/s12046-021-01618-z

Turskis, E. K. Z. a. Z. (2008). A new logarithmic normalization method in games theory. Informática, 19, 12.

Yadav, A., & Jayswal, S. C. (2018). Modelling of flexible manufacturing system: a review. International Journal of Production Research, 56(7), 1-24. https://doi.org/10.1080/00207543.2017.1387302

Yadav, G., Seth, D., & Desai, T. N. (2018). Application of hybrid framework to facilitate lean six sigma implementation: a manufacturing company case experience. Production Planning & Control, 29(3), 1-17. https://doi.org/10.1080/09537287.2017.1402134