3B Yazıcının Kontrolünde Optimizasyon Algoritmalarının Performansı

Year 2021, Volume: 33 - ASYU 2020 Özel Sayısı, 10 - 16, 30.12.2021

Aytaç Altan Ahmet Parlak

https://doi.org/10.7240/jeps.894178

Abstract

Üç boyutlu (3B) biyo yazıcılar, rejeneratif tıp ve doku mühendisliği alanlarında özellikle kulak, burun ve yüz-çene protezlerinin biyo baskılarında yoğun şekilde kullanılmaktadır. Modelden modele farklılık gösteren baskı hataları yapay doku ve organların biyo-baskısında sıklıkla görülmektedir. Modelin yüzeyinde meydana gelen hatalar yazdırılacak organın verimli kullanımına engel olmaktadır. Yapay doku ve organ biyo-baskısı sürecinde meydana gelen hataların en aza indirilebilmesi için 3B yazıcının kontrolcü performansının iyileştirilmesi gerekmektedir. Bu çalışmada, yapay doku ve organların biyo-baskısı için İyileştirilmiş Gri Kurt Optimizasyon (İGKO) tabanlı yeni bir uyarlanabilir PID kontrolcü geliştirilmiştir. Yerel minimumlardan kaçınmak için İGKO algoritması tercih edilmiştir. Önerilen algoritmanın yakınsama hızı PID kontrolcünün parametrelerinin hızlı ve doğru şekilde ayarlanabilmesine olanak sağlamaktadır. Geliştirilen İGKO tabanlı uyarlanabilir PID kontrolcünün performansı, performans metriklerinden biri olan zaman ağırlıklı karesel hatanın integrali (Integral of Time multiplied Squared Error-ITSE) yardımıyla ölçülmüştür. 3B yazıcı için önerilen kontrolcünün performansı, klasik PID ve Balina Optimizasyon Algoritması (BOA) tabanlı PID kontrolcülerin performansı ile karşılaştırılmıştır. Elde edilen deneysel sonuçlardan, önerilen İGKO tabanlı uyarlanabilir PID kontrolcünün 3B yazıcının geçici tepkisini önemli ölçüde iyileştirdiği ve yazdırılan burun ve kulak gibi organlardaki yüzey hatalarını en aza indirdiği görülmektedir.

Keywords

3B yazıcı, Yapay doku ve organ, Geliştirilmiş gri kurt optimizasyon, Uyarlamalı kontrol, Burun ve kulak protezi

Thanks

Bu çalışma ASYU2020_Akıllı Sistemlerde Yenilikler ve Uygulamaları Özel sayısı için değerlendirilmek üzere gönderilmiştir.

Performance of Optimization Algorithms in the Control of 3D Printer

Abstract

Three-dimensional (3D) bio printers are used extensively in regenerative medicine and tissue engineering, especially in bioprinting of ear, nose and face-chin prostheses. Printing errors that differ from model to model are frequently seen in the bio-printing of artificial tissue and organ. Errors occurring on the surface of the model prevent the efficient use of the organ to be printed. The controller performance of the 3D printer needs to be improved so that errors in the artificial organ and tissue bio-printing process can be minimized. In this study, a novel adaptive PID controller based on Improved Grey Wolf Optimization (IGWO) has been developed for bio-printing of artificial tissues and organs. The IGWO algorithm has been preferred to avoid local minima. The convergence speed of the proposed algorithm allows the parameters of the PID controller to be adjusted quickly and accurately. The performance of the developed IGWO based adaptive PID controller has been measured with the help of Integral of Time multiplied Squared Error (ITSE), one of the performance metrics. The performance of the proposed controller for the 3D printer has been compared to the classical PID and Whale Optimization Algorithm (WOA) based PID controllers’s performance. From the experimental obtained results, it can be seen that the proposed IGWO based adaptive PID controller significantly improves the 3D printer's transient response and minimizes surface errors in the printed organs such as the nose and ear.

Keywords

3D printer, Artificial tissue and organ, Improved grey wolf optimization, Adaptive control, Nose and ear prosthesis

References

• Nuseir, A., Hatamleh, M. M. D., Alnazzawi, A., Al‐Rabab'ah, M., Kamel, B., & Jaradat, E., (2019). Direct 3D printing of flexible nasal prosthesis: optimized digital workflow from scan to fit. Journal of Prosthodontics, 28(1), 10-14.
• Zhong, N., & Zhao, X., (2017). 3D printing for clinical application in otorhinolaryngology. European Archives of Oto-Rhino-Laryngology, 274(12), 4079-4089.
• Di Gesù, R., Acharya, A. P., Jacobs, I., & Gottardi, R., (2020). 3D printing for tissue engineering in otolaryngology. Connective Tissue Research, 61(2), 117-136.
• Farook, T. H., Jamayet, N. B., Abdullah, J. Y., Rajion, Z. A., & Alam, M. K., (2020). A systematic review of the computerized tools and digital techniques applied to fabricate nasal, auricular, orbital and ocular prostheses for facial defect rehabilitation. Journal of Stomatology, Oral and Maxillofacial Surgery, 121(3), 268-277.
• Bauermeister, A. J., Zuriarrain, A., & Newman, M. I., (2016). Three-dimensional printing in plastic and reconstructive surgery: a systematic review. Annals of Plastic Surgery, 77(5), 569-576.
• Nemli, S.K., Aydin, C., Yilmaz, H., Bal, B.T., & Arici, Y.K., (2013). Quality of life of patients with implant-retained maxillofacial prostheses: a prospective and retrospective study. The Journal of Prosthetic Dentistry, 109(1), 44-52.
• Irish, J., Sandhu, N., Simpson, C., Wood, R., Gilbert, R., Gullane, P., Brown, D., Goldstein, D., Devins, G., & Barker, E., (2009). Quality of life in patients with maxillectomy prostheses. Head & Neck: Journal for the Sciences and Specialties of the Head and Neck, 31(6), 813-821.
• Levine, E., Degutis, L., Pruzinsky, T., Shin, J., & Persing, J. A., (2005). Quality of life and facial trauma: psychological and body image effects. Annals of Plastic Surgery, 54(5), 502-510.
• Horlock, N., Vögelin, E., Bradbury, E. T., Grobbelaar, A. O., & Gault, D. T., (2005). Psychosocial outcome of patients after ear reconstruction: a retrospective study of 62 patients. Annals of Plastic Surgery, 54(5), 517-524.
• Hatamleh, M. M., Haylock, C., Watson, J., & Watts, D. C., (2010). Maxillofacial prosthetic rehabilitation in the UK: a survey of maxillofacial prosthetists’ and technologists’ attitudes and opinions. International Journal of Oral and Maxillofacial Surgery, 39(12), 1186-1192.
• Watson, J., & Hatamleh, M. M., (2014). Complete integration of technology for improved reproduction of auricular prostheses. The Journal of Prosthetic Dentistry, 111(5), 430-436.
• Hatamleh, M. M., & Watson, J., (2013). Construction of an implant‐retained auricular prosthesis with the aid of contemporary digital technologies: a clinical report. Journal of Prosthodontics: Implant, Esthetic and Reconstructive Dentistry, 22(2), 132-136.
• Altan, A., & Hacıoğlu, R., (2018). The algorithm development and implementation for 3D printers based on adaptive PID controller. Journal of Polytechnic, 21(3), 559-564.
• Mirjalili, S., Mirjalili, S. M., & Lewis, A., (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46-61.
• Wang, X., Zhao, H., Han, T., Zhou, H., & Li, C., (2019). A grey wolf optimizer using Gaussian estimation of distribution and its application in the multi-UAV multi-target urban tracking problem. Applied Soft Computing, 78, 240-260.
• Altan, A., & Parlak, A. (2020). Adaptive Control of a 3D Printer using Whale Optimization Algorithm for Bio-Printing of Artificial Tissues and Organs. IEEE Innovations in Intelligent Systems and Applications Conference, October, 1-5.
• Nadimi-Shahraki, M. H., Taghian, S., & Mirjalili, S., (2021). An improved grey wolf optimizer for solving engineering problems. Expert Systems with Applications, 166, 113917.
• Tan, P. N., Steinbach, M., & Kumar, V., (2005). Introduction to Data Mining. Pearson Addison Wesley.