THE ROLE OF SIMULATION ENVIRONMENTS IN TEACHING INDUSTRIAL ROBOTICS AND MECHATRONICS
Keywords:
Simulation, Engineering Education, TIA Portal, Robotics Education, Virtual Commissioning, Digital Twin, Industry 4.0, PLC Programming, HMI Design, Mechatronics Learning, Uzbekistan.Abstract
This study investigates the application of simulation environments, particularly Siemens TIA Portal, in teaching industrial robotics and mechatronics in the context of Uzbekistan's engineering education system. Key educational parameters—concept understanding, practical skills development, student engagement, and assessment effectiveness—were optimized within a simulated learning environment at Tashkent State Technical University, allowing adjustments to be made without impacting actual laboratory equipment. Quantitative results indicate that utilizing simulation tools significantly enhances learning outcomes (17.3% improvement in overall performance) and cost-effectiveness (41.2% reduction in equipment requirements). Implementing virtual laboratories reduced hardware dependencies while advanced visualization capabilities improved student comprehension of complex automation concepts. The study underscores the role of simulation environments in facilitating sustainable, effective engineering education through their digital twin capabilities, particularly vital for educational institutions in Uzbekistan facing resource constraints while striving to meet international standards.
Downloads
References
Khakimov, Z., Kodirov, E., & Turdiyev, B. (2022). "Modernization of Engineering Education in Uzbekistan: Challenges and Opportunities." Central Asian Journal of Education, vol. 7, no. 1, pp. 78-96.
Juchheim, J., Müller, D., & Nordmann, R. (2020). "Optimization of Engineering Education through Digital Twin Technologies." Journal of Engineering Education, vol. 109, no. 4, pp. 452-465.
Kraiger, K., Ford, J. K., & Salas, E. (1993). "Application of Cognitive, Skill-Based, and Affective Theories of Learning Outcomes to New Methods of Training Evaluation." Journal of Applied Psychology, vol. 78, no. 2, pp. 311-328.
Xu, X., Chen, K., & Zhang, G. (2021). "Digital Twin and Its Applications in Manufacturing: A Review." IEEE Access, vol. 9, pp. 12929-12942.
Rakhimov, O. (2021). "Automation Education in Uzbekistan: Current State and Development Perspectives." Technical Science and Innovation Journal, vol. 2021, no. 3, pp. 112-124.
Jazdi, N. (2014). "Cyber-Physical Systems in the Context of Industry 4.0." IEEE Conference on Automation, Quality, and Testing, Robotics (AQTR), pp. 1-4.
Kreutzer, J. (2016). "Modeling and Simulation of Complex Manufacturing Systems: An Approach for Sustainable Design." Springer.
Siemens AG. (2020). "Optimizing Automation Education with TIA Portal: A Comprehensive Guide to Simulation and Efficiency Testing." Siemens Industry Journal, vol. 7, no. 3, pp. 45-55.
Tisch, M., Hertle, C., Cachay, J., Abele, E., Metternich, J., & Tenberg, R. (2016). "A Systematic Approach on Developing Action-oriented, Competency-based Learning Factories." Procedia CIRP, vol. 7, pp. 580-585.
Azwan, A., Shukor, M., & Yusof, Y. (2018). "Effective Teaching and Learning in Engineering Education: A Review." International Journal of Engineering Education, vol. 34, no. 3, pp. 167-179.
Abele, E., Metternich, J., & Tisch, M. (2019). "Learning Factories: Concepts, Guidelines, Best-Practice Examples." Springer International Publishing.
Schütze, M., Sachse, P., & Römer, A. (2018). "Visual Learning in Engineering Education: Theory and Practice." International Journal of Engineering Pedagogy, vol. 8, no. 3, pp. 66-79.
