Development of plasma cutter machine as a teaching support tool in engineering careers
Keywords:
automation, industrial process efficiency, industrial processAbstract
The use of automated equipment such as the plasma cutter reduces manufacturing times, in addition to being more accurate in the cutting of a wide range of metals, the use of this type of device optimizes manufacturing processes in the manufacture of equipment for processing raw materials from the agricultural sector, as well as electromechanical equipment and implements used in the agro-industrial sector. From the academic point of view, the use of this type of machine familiarizes future professionals with new generation equipment and reduces the duration of laboratory practices, which decreases the stress and anxiety conditions of the engineering students, improving their academic performance. Therefore, the objective of this research was to design a prototype plasma cutter, which was used for the practical activities of the electromechanical engineering student, for the evaluation of the efficiency of the prototype, the duration of the academic activities was quantified, as well as the degree of satisfaction of the students in the elaboration of the academic activities. The results found reveal that the practice time was considerably reduced compared to practices carried out under conventional educational approaches, in addition to the fact that the level of stress and anxiety among the students who used the plasma cutter in their practical activities was lower, which positively influenced their academic performance.
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References
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Trivedi,K., Kundaliya, D. (2017). Analysis of Process Parameters Used in Plasma Cutting Machine: A Review. Trends in mechanical engineering & technology 17 (2), 18-21.
Węglowski M.S., Pfeifer T. (2014). Influence of cutting technology on properties of the cut edges. Advances in Manufacturing Science and Technology, 38(2): 63-73.
Wu, D., Connor, J., Terpenny, J., gao, R., Kumara, S. (2017). A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests. J. Manuf. Sci. Eng. Jul 2017, 139(7): 071018 (9 pages)
Zambrano, C. (2016). Autoeficacia, Prácticas de Aprendizaje Autorregulado y Docencia para fomentar el Aprendizaje Autorregulado en un Curso de Ingeniería de Software. Formación universitaria, 9(3), 51-60
Zamora, R., Coba, J. (2018). Trends in Modeling and Simulation in the Automotive Industry Concerning the Bond Graph framework. International Journal of Engineering & Technology, 7(4.16), 160-163
Anzola, G. (2019). Innovación tecnológica en la gestión universitaria. Revista UDCA Actualidad & Divulgación Científica, 22(2) e1380, 1-4,
Alducin, J., Vázquez,Al. (2017). Estilos de aprendizaje, variables sociodemográficas y rendimiento académico en estudiantes de Ingeniería de Edificación. Revista Electrónica Educare, 21(1), 350-380.
Basterretxea, I., Charterina, J., Landeta, J. (2019). Coopetition and innovation. Lessons from worker cooperatives in the Spanish machine tool industry. Journal of Business & Industrial Marketing 34 (6), 1223-1235.
Cardanha, B., Da Costa, K, Campos, P., Junior, J. (2018). Comparative analysis after the implantation of a computerized CNC plasma cutting equipment at a shipyard in the city of Manaus–AMV.ITEGAM-JETIA, 4(15): 164-167.
Castillo, L., Alarcón, A., Callejas, M. (2017). Infraestructura física para laboratorios en el área de ingeniería del software. Entramado 13 (2), 260.268.
Chen, M. (2016). Technology, informal workers and cities: insights from Ahmedabad (India), Durban (South Africa) and Lima (Peru). Environment & Urbanization 28 (2), 405-422.
Da Cunha, J., Hernández, T. (2019). El proceso de enseñanza-aprendizaje de la automatización en la carrera de Electromecánica. Conrado, 15(69), 89-95.
Giraldo, E., Giraldo, J., Valderrama, J. (2018). Modelo de Simulación de un Sistema Logístico de Distribución como Plataforma Virtual para el Aprendizaje Basado en Problemas. Información tecnológica, 29(6), 185-198
Guerrero B.B., Jaramillo J.A.C. (2018). Escenarios futuros del sector metalmecánico. Municipio de Tuluá y su zona de influencia. Horizonte 2018-2028. Informador Técnico, 82(2): 181-208.
Hernandez, E., Sorzano, F., Coba, M.,Acevedo, C., Valencia, G. (2018). Experimental Study of the Cutting Processes Effect on the Surface Hardness of ASTM A36 Steel. Contemporary Engineering Sciences, 11 (39), 1921-1928.
Ibarra S.T.C., Beltrán J.A.R., Torres M.G.M. (2016). Análisis del estrés académico en estudiantes de ingeniería como estrategia para el aprendizaje significativo. ANFEI Digital, (5), 1-8.
Kunal, P., Mungla, M. (2020). A Review on Optimization of Plasma Arc Cutting Parameters Using Taguchi Method for EN19. Journal of Science and Technology 5 (3), 172-191.
Kurdna, L.,Merta, M. (2017). The technology of plasma cutting on a cnc machine. International Multidisciplinary Scientific GeoConference : SGEM; Sofia 17 (1.3), 949-956.
Lazarevic, A., Lazarevic, D. (2017) Investigations of material hardness and structural changes in the heat-affected zone during plasma cutting. Weld World 61: 1069–1075.
Liu, Y. (2017). Renovation of a mechanical engineering senior design class to an industry-tied and team-oriented course. European Journal of Engineering Education, 42(6), 800-811.
Masoudi, S., Mirabdolahi, M., Dayyani, M., Jafarian, F., Vafadar, A., & Dorali, M. R. (2019). Development of an intelligent model to optimize heat-affected zone, kerf, and roughness in 309 stainless steel plasma cutting by using experimental results. Materials and Manufacturing Processes, 34(3): 345-356.
Matushkina I., Anakhov, S., Pyckin Y. (2019). Evaluation of the effectiveness of plasma torches design for metal cutting by qualimetric method. In Materials Science Forum, 946: 877-882.
McAuley E., Duncan T., Tammen, V.V. (1989). Psychometric properties of the IntrinsicMotivation Inventory in a competitive sport setting: A confirmatory factor analysis. Research Quarterly for Exercise and Sport, 60: 48-58
Mirabdolahi M., Abootorabi M. (2019). Optimization and Modeling of Plasma Cutting of AISI 309 Stainless Steel by Using Neural Network-Genetic Algorithm Hybrid Model. Modares Mechanical Engineering, 19(10): 2455-2462.
Parthiban, A., Prasath,J., Vivek, P., Pugazhenthi, R. (2018). Experimental investigation of plasma arc cutting for stainless steel sheet. International Journal of Mechanical and Production Engineering Research and Development, 8(1), 907-914
Pittayachaval, P., Aupkaew, Y., Sakhonkhan, S., Sukan, T., & Patchaikhonang, C. (2020). Investigating Plasma-Nozzle Wear Based on Processing Time and Current Ampere. Materials Science Forum, 987, 171–176.
Ramakrishna, S., Raghuram, K., Avinash. (2018) Process modelling and simulation analysis of CNC oxy-fuel cutting process on SA 516 grade 70 carbon steel. Mater Today Proc 5:7818–7827
Ramos, M., Peña, M., Vivas, C., Marquez, I. (2018). Proceso de enseñanza aprendizaje del control de procesos en el programa de Ingeniería Mecánica de la Universidad Libre. Revista ingenio libre 16, 10-15.
Ross, J., Celis, S. (2020). ¿Descanso para hoy o estrés para mañana?: Beneficios y costos de los paros estudiantiles en ingeniería y ciencias, Revista Iberoamericana de Educación en Ingeniería (RIEI) 2 (4), 1-9.
Rúa, R., Blasnilo, E., Jiménez, D., Gutiérrez, A., Andrés, G., & Villamizar, N. I. (2018). Impresión 3D como Herramienta Didáctica para la Enseñanza de Algunos Conceptos de Ingeniería y Diseño. Ingeniería, 23(1), 70-83.
Sharifullin, S., Adigamov. N., Adigamov, N., Solovev, R., Arakcheeva, K. (2016). Surface hardening of cutting elements agricultural machinery vibro arc plasma. Journal of Physics: Conference Series 669 (012049), 1-6.
Suárez O.J., Marquez, A.H., Cardozo, O.A.P. (2019). Estrés académico en estudiantes de Ingeniería que cursan Física: Análisis exploratorio. Revista científica, 76-83.
Tercan, H., Al Khawlib, T., Eppeltb, U., Buscher, C., Meisen, T., Jeschkea, S. (2016). Use of Classification Techniques to Design Laser Cutting Processes Procedia CIRP 52 ( 2016 ) 292 – 297.
Trivedi,K., Kundaliya, D. (2017). Analysis of Process Parameters Used in Plasma Cutting Machine: A Review. Trends in mechanical engineering & technology 17 (2), 18-21.
Węglowski M.S., Pfeifer T. (2014). Influence of cutting technology on properties of the cut edges. Advances in Manufacturing Science and Technology, 38(2): 63-73.
Wu, D., Connor, J., Terpenny, J., gao, R., Kumara, S. (2017). A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests. J. Manuf. Sci. Eng. Jul 2017, 139(7): 071018 (9 pages)
Zambrano, C. (2016). Autoeficacia, Prácticas de Aprendizaje Autorregulado y Docencia para fomentar el Aprendizaje Autorregulado en un Curso de Ingeniería de Software. Formación universitaria, 9(3), 51-60
Zamora, R., Coba, J. (2018). Trends in Modeling and Simulation in the Automotive Industry Concerning the Bond Graph framework. International Journal of Engineering & Technology, 7(4.16), 160-163
Published
2020-07-15
How to Cite
Murillo, L., Bustillos, D., Lara, O., Achote, J., & Rovayo, F. (2020). Development of plasma cutter machine as a teaching support tool in engineering careers. Agroindustria, Sociedad Y Ambiente, 2(15), 85-101. Retrieved from https://revistas.uclave.org/index.php/asa/article/view/2855
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