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Building and Cost Analysis of an Industrial Automation System using Industrial Robots and PLC Integration

Year 2021, Issue: 28, 1 - 10, 30.11.2021
https://doi.org/10.31590/ejosat.972290

Abstract

Technology rapidly advances on a daily basis and the resulting changes can provide numerousbenefits for manufacturing methods and machines. Manufacturers who are able to swiftly embrace thesedevelopments can increase their manufacturing output, thereby boosting profitability and gainingcompetitive advantages over their rivals. However, the cost savings which result from new innovationscan vary, depending on the manufacturing model. Consequently, manufacturers need to conduct accurateanalyses for appropriate manufacturing methods in order to ensure that new changes are cost-effective.Nowadays, the use of industrial automation systems is gaining popularity as a method of increasingprofitability for mass production, and these systems utilize control systems, such as industrial robots andprogrammable logic controllers. The use of these elements in the manufacturing process not onlyprovides quality and flexible production methods, which are indispensable considerations, but alsoconserves human effort. The aim of this study was to minimize the cost of a factory-installed industrialautomation system, which produced globe valves with side couplings, through the combined use ofindustrial robots and programmable logic controllers. While calculating returns from the installed system,the differential evolution algorithm was used to predict future unit prices of electricity, and it wasdetermined that the cost of investment would be recovered after a maximum of 2.5 years and that currentyearly production would increase fourfold.

References

  • Yücel, İ.H., Sanayide Robot Teknolojisi. Uygulaması Ve Önemi, DPT Yayınları, Ankara, Aralık, 1991.
  • Sheridan, T.B., Telerobotics, automation, and human supervisory control. 1992: MIT press.
  • Özcan, M., Otomasyon sistemlerinde PLC uygulamaları. 2004: Atlas Yayın dağıtım.
  • Acharya, V., S.K. Sharma, and S. Kumar Gupta, Analyzing the factors in industrial automation using analytic hierarchy process. Computers & Electrical Engineering, 2017.
  • Çengelci, B. and H. Çimen, Endüstriyel robotlar. Makine Teknolojileri Elektronik Dergisi, 2005. 2(2): p. 69-78.
  • Peşkircioğlu, N., Otomasyon ve Entegre Kalite Kontrolu. Verimlilik Dergisi, (15): p. 19-40.
  • De Silva, D., Reactions to Robots, Engineering. 1987, April.
  • Kurtulan, S., PLC ile endüstriyel otomasyon. 2001: Birsen Yayınevi.
  • Rehg, J.A. and G.J. Sartori, Programmable logic controllers. 2009, Upper Saddle River, N.J.: Pearson Prentice Hall.
  • Niola, V., C. Rossi, and S. Savino, Vision system for industrial robots path planning. International journal of Mechanics and control, 2007. 8(1): p. 35-45.
  • CHEN, D.-q. and Y.-c. KUANG, Communication between Mitsubishi PLC and GSK Industrial Robot. Mechanical Engineer, 2013. 4: p. 023.
  • Stückelmaier, P., M. Grotjahn, and C. Fräger. Iterative improvement of path accuracy of industrial robots using external measurements. in Advanced Intelligent Mechatronics (AIM), 2017 IEEE International Conference on. 2017. IEEE.
  • Jeong, H.S., et al. Design of SW architecture for PLC integrated robot. in Ubiquitous Robots and Ambient Intelligence (URAI), 2017 14th International Conference on. 2017. IEEE.
  • Chen, Y. and F. Dong, Robot machining: recent development and future research issues. The International Journal of Advanced Manufacturing Technology, 2013. 66(9-12): p. 1489-1497.
  • Eke, İ., Diferansiyel evrim algoritması destekli yapay sinir ağı ile orta dönem yük tahmini. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 2011. 3(1): p. 28-32.
  • Wang, L., et al., Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm. Energy, 2018. 153: p. 801-815.
  • Standard, I., 8373: 1994. Manipulating industrial robots–Vocabulary.
  • Dişlitaş, S., Endüstriyel Robot Programlama. Baskı, Endüstriyel Robot Programlama Eğitimi ile Mesleki ve Teknik Eğitim Güçlendirilmesi (ERPE-METEG) Projesi, Çorum, 2015.
  • Mirzaoğlu, İ. and M. Sarıtaş, PLC VE SCADA Kullanarak Bir İrmik Üretim Sisteminin Otomasyonu. 2008.
  • Storn, R. and K. Price, Differential evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces [R]. Berkeley: ICSI, 1995.
  • Canyurt, O.E., et al., Energy demand estimation based on two-different genetic algorithm approaches. Energy Sources, 2004. 26(14): p. 1313-1320.
  • Ceylan, H. and H.K. Ozturk, Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion and Management, 2004. 45(15-16): p. 2525-2537.
  • Unler, A., Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025. Energy Policy, 2008. 36(6): p. 1937-1944.
  • Gulcu, S. and H. Kodaz, The estimation of the electricity energy demand using particle swarm optimization algorithm: A case study of Turkey. 8th International Conference on Advances in Information Technology, 2017. 111: p. 64-70.
  • Toksari, M.D., Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey. Energy Policy, 2009. 37(3): p. 1181-1187.
  • Sonmez, M., A.P. Akgungor, and S. Bektas, Estimating transportation energy demand in Turkey using the artificial bee colony algorithm. Energy, 2017. 122: p. 301-310.
  • Beskirli, M., H. Hakli, and H. Kodaz, The energy demand estimation for Turkey using differential evolution algorithm. Sadhana-Academy Proceedings in Engineering Sciences, 2017. 42(10): p. 1705-1715.
  • Kiran, M.S., et al., A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey. Energy Conversion and Management, 2012. 53(1): p. 75-83.
  • Yumurtaci, Z. and E. Asmaz, Electric energy demand of Turkey for the year 2050. Energy Sources, 2004. 26(12): p. 1157-1164.
  • Kavaklioglu, K., et al., Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks. Energy Conversion and Management, 2009. 50(11): p. 2719-2727.
  • Toksari, M.D., A hybrid algorithm of Ant Colony Optimization (ACO) and Iterated Local Search (ILS) for estimating electricity domestic consumption: Case of Turkey. International Journal of Electrical Power & Energy Systems, 2016. 78: p. 776-782.

Endüstriyel Robot ve PLC Entegrasyonuyla Talaşlı İmalat Üretim İşleminin Gerçekleştirilmesi

Year 2021, Issue: 28, 1 - 10, 30.11.2021
https://doi.org/10.31590/ejosat.972290

Abstract

Üreticiler üretim şekillerini değiştirmeden önce doğru analizler yaparak kendilerine en uygun üretim yöntemini seçmeleri gerekmektedir. Bu çalışmada yan rakorlu küresel vana üretilen bir fabrikada Endüstriyel Otomasyon Sistemi kurulmuş ve kurulan bu sistemin maliyeti Endüstriyel Robot ve PLC beraber kullanılarak en aza indirilmesi hedeflenmiştir. Üretim yönteminde Endüstriyel Robotun kullanılması ve mevcut sistemde ki üretimde bir takım değişiklikler yapılmasıyla esnek üretim sağlanmış ve üretimin aksamaması için bazı tedbirler alınmıştır. Kurulan sistem maliyetinin geri dönüşüm süreci hesaplanmasında Diferansiyel Evrim Algoritmasından yararlanılarak gelecekteki elektrik birim fiyatları tahmin edilmiştir. Bu çalışmada, yapılan yatırımın en fazla 2,5 yıl içerisinde geri döneceği ve mevcut yıllık üretim miktarının da yaklaşık 4 kat artacağı tespit edilmiştir.

References

  • Yücel, İ.H., Sanayide Robot Teknolojisi. Uygulaması Ve Önemi, DPT Yayınları, Ankara, Aralık, 1991.
  • Sheridan, T.B., Telerobotics, automation, and human supervisory control. 1992: MIT press.
  • Özcan, M., Otomasyon sistemlerinde PLC uygulamaları. 2004: Atlas Yayın dağıtım.
  • Acharya, V., S.K. Sharma, and S. Kumar Gupta, Analyzing the factors in industrial automation using analytic hierarchy process. Computers & Electrical Engineering, 2017.
  • Çengelci, B. and H. Çimen, Endüstriyel robotlar. Makine Teknolojileri Elektronik Dergisi, 2005. 2(2): p. 69-78.
  • Peşkircioğlu, N., Otomasyon ve Entegre Kalite Kontrolu. Verimlilik Dergisi, (15): p. 19-40.
  • De Silva, D., Reactions to Robots, Engineering. 1987, April.
  • Kurtulan, S., PLC ile endüstriyel otomasyon. 2001: Birsen Yayınevi.
  • Rehg, J.A. and G.J. Sartori, Programmable logic controllers. 2009, Upper Saddle River, N.J.: Pearson Prentice Hall.
  • Niola, V., C. Rossi, and S. Savino, Vision system for industrial robots path planning. International journal of Mechanics and control, 2007. 8(1): p. 35-45.
  • CHEN, D.-q. and Y.-c. KUANG, Communication between Mitsubishi PLC and GSK Industrial Robot. Mechanical Engineer, 2013. 4: p. 023.
  • Stückelmaier, P., M. Grotjahn, and C. Fräger. Iterative improvement of path accuracy of industrial robots using external measurements. in Advanced Intelligent Mechatronics (AIM), 2017 IEEE International Conference on. 2017. IEEE.
  • Jeong, H.S., et al. Design of SW architecture for PLC integrated robot. in Ubiquitous Robots and Ambient Intelligence (URAI), 2017 14th International Conference on. 2017. IEEE.
  • Chen, Y. and F. Dong, Robot machining: recent development and future research issues. The International Journal of Advanced Manufacturing Technology, 2013. 66(9-12): p. 1489-1497.
  • Eke, İ., Diferansiyel evrim algoritması destekli yapay sinir ağı ile orta dönem yük tahmini. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 2011. 3(1): p. 28-32.
  • Wang, L., et al., Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm. Energy, 2018. 153: p. 801-815.
  • Standard, I., 8373: 1994. Manipulating industrial robots–Vocabulary.
  • Dişlitaş, S., Endüstriyel Robot Programlama. Baskı, Endüstriyel Robot Programlama Eğitimi ile Mesleki ve Teknik Eğitim Güçlendirilmesi (ERPE-METEG) Projesi, Çorum, 2015.
  • Mirzaoğlu, İ. and M. Sarıtaş, PLC VE SCADA Kullanarak Bir İrmik Üretim Sisteminin Otomasyonu. 2008.
  • Storn, R. and K. Price, Differential evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces [R]. Berkeley: ICSI, 1995.
  • Canyurt, O.E., et al., Energy demand estimation based on two-different genetic algorithm approaches. Energy Sources, 2004. 26(14): p. 1313-1320.
  • Ceylan, H. and H.K. Ozturk, Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion and Management, 2004. 45(15-16): p. 2525-2537.
  • Unler, A., Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025. Energy Policy, 2008. 36(6): p. 1937-1944.
  • Gulcu, S. and H. Kodaz, The estimation of the electricity energy demand using particle swarm optimization algorithm: A case study of Turkey. 8th International Conference on Advances in Information Technology, 2017. 111: p. 64-70.
  • Toksari, M.D., Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey. Energy Policy, 2009. 37(3): p. 1181-1187.
  • Sonmez, M., A.P. Akgungor, and S. Bektas, Estimating transportation energy demand in Turkey using the artificial bee colony algorithm. Energy, 2017. 122: p. 301-310.
  • Beskirli, M., H. Hakli, and H. Kodaz, The energy demand estimation for Turkey using differential evolution algorithm. Sadhana-Academy Proceedings in Engineering Sciences, 2017. 42(10): p. 1705-1715.
  • Kiran, M.S., et al., A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey. Energy Conversion and Management, 2012. 53(1): p. 75-83.
  • Yumurtaci, Z. and E. Asmaz, Electric energy demand of Turkey for the year 2050. Energy Sources, 2004. 26(12): p. 1157-1164.
  • Kavaklioglu, K., et al., Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks. Energy Conversion and Management, 2009. 50(11): p. 2719-2727.
  • Toksari, M.D., A hybrid algorithm of Ant Colony Optimization (ACO) and Iterated Local Search (ILS) for estimating electricity domestic consumption: Case of Turkey. International Journal of Electrical Power & Energy Systems, 2016. 78: p. 776-782.
There are 31 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Enes Efe 0000-0002-6136-6140

Muciz Özcan 0000-0001-5277-6650

Hüseyin Haklı 0000-0001-5019-071X

Publication Date November 30, 2021
Published in Issue Year 2021 Issue: 28

Cite

APA Efe, E., Özcan, M., & Haklı, H. (2021). Building and Cost Analysis of an Industrial Automation System using Industrial Robots and PLC Integration. Avrupa Bilim Ve Teknoloji Dergisi(28), 1-10. https://doi.org/10.31590/ejosat.972290