Shashank Gupta
- Student Department of Automation and Robotics, Guru Gobind Singh Indraprastha University New Delhi India
Abstract
The design and development of an automatic blower system to improve productivity in manufacturing processes are presented in this research article. The suggested system employs advanced control algorithms and sensors to improve the blower’s function while minimizing energy usage. The goal of the research is to automate the blower system’s operation by incorporating intelligent features, which will decrease the need for human intervention and increase overall efficiency. Experimental results reveal considerable gains in energy efficiency and performance compared to typical manual control approaches. The discoveries of this research contribute to the advancement of automated systems in manufacturing environments, boosting ecologically friendly and advantageous functioning. Scientific advancements have accelerated the technique of machinery advancement to a new level, enabling fully automated manufacturing equipment to enter the market and eventually phase out manual labor. Automation attackers are one of several linked sectors that have grown over the past few years because of the development of industrial automation. The creation of attackers as a new piece of machinery to operate automated production lines can significantly raise the level of automation of manufacturing businesses and increase worker productivity.
Keywords: Automatic blower system, efficiency, industrial applications, control algorithms, energy consumption
[This article belongs to International Journal of Advanced Robotics and Automation Technology(ijarat)]
References
- Kong, Y. Zhou, Y. Liu, and L. Xue, “Using the data miningmethod to assess the innovation gap: a case of industrialrobotics in a catching-up country,” Technological Forecastingand Social Change, vol. 119, pp. 80–97, 2017.
- A. Lubnina, M. V. Shinkevich, E. N. Yalunina,I. V. Gaidamashko, A. F. Savderova, and M. A. Komissarova,“Innovative strategy for improving the efficiency of industrialenterprises management,” Revista Espacios, vol. 39, no. 9, 2018.
- E. Breaz, O. Bologa, and S. G. Racz, “Selecting industrialrobots for milling applications using AHP,” Procedia Com-puter Science, vol. 122, pp. 346–353, 2017.
- Diao, J. Tang, P. C. Loh, S. Yin, L. Wang, and Z. Liu, “Anefficient DSP–FPGA-based implementation of hybrid PWM forelectric rail traction induction motor control,” IEEE Transactionson Power Electronics, vol. 33, no. 4, pp. 3276–3288, 2018.
- Wu, D. Zhang, J. Liu, and X. Han, “A moment approach topositioning accuracy reliability analysis for industrial robots,”IEEE Transactions on Reliability, vol. 69, no. 2, pp. 699–714,2020.
- Burghardt, D. Szybicki, P. Gierlak, K. Kurc, P. Pietru´s, andR. Cygan, “Programming of industrial robots using virtualreality and digital twins,” Applied Sciences, vol. 10, no. 2,p. 486, 2020.
- D. Evjemo, T. Gjerstad, E. I. Grøtli, and G. Sziebig, “Trendsin smart manufacturing: role of humans and industrial robotsin smart factories,” Current Robotics Reports, vol. 1, no. 2,pp. 35–41, 2020.
- V. Wang and L. Wang, “A literature survey of the robotictechnologies during the COVID-19 pandemic,” Journal ofManufacturing Systems, vol. 60, pp. 823–836, 2021
- Li, R. Batra, D. Brown et al., “Particle robotics based onstatistical mechanics of loosely coupled components,” Nature,vol. 567, no. 7748, pp. 361–365, 2019.
- Saleh and M. Khan, “Modular robotic platform for au-tonomous machining,” International Journal of AdvancedManufacturing Technology, vol. 105, no. 5-6, pp. 2557–2567,2019.
| Volume | 01 |
| Issue | 01 |
| Received | July 2, 2023 |
| Accepted | July 17, 2023 |
| Published | December 12, 2023 |