Araştırma Makalesi
BibTex RIS Kaynak Göster

Bir mekanik jiroskopun yalpalama tahmininde uzun kısa süreli bellek ağı yaklaşımı

Yıl 2023, Cilt: 15 Sayı: 3, 179 - 193, 31.12.2023

İlyas Kacar

https://doi.org/10.29137/umagd.1293684

Öz

Mekanik jiroskoplar, ürettikleri jiroskopik tork sayesinde hava ve uzay araçları gibi tamamen asılı duran araçlara yön vermek için kullanılmaktadır. Kara araçlarında da tek veya iki tekerlekli araçların otonom dengesi için kullanılmaktadır. Her ne kadar uzun yıllardır regresyonlar mevcut veriyi modelleme amaçlı kullanılagelen bir yöntem olmuşsa da son yıllarda makine ve derin öğrenme yöntemlerinin sınıflama, modelleme konularında yüksek başarıya sahip oldukları görülmüştür. Bu çalışmada bir derin öğrenme yöntemi olan uzun kısa süreli bellek ağı kullanılarak mekanik bir jiroskopun yalpalama hızı tahmin edilmiştir. Elde edilen modelde RMSE 0.0055 rad/s iken ME değeri -0.0012 rad/s ve R 0.9998 olup model çıktısı ile veri seti arasında yüksek ilişki mevcuttur. Bu hali ile derin öğrenme yöntemlerinin mekanik jiroskop tasarımlarında kullanılması fayda sağlayacaktır.

Anahtar Kelimeler

Jiroskop Makine öğrenimi Uzun kısa süreli hafıza

Teşekkür

Rijit dinamik simülasyonları için kullanılan Ansys®’in eğitsel amaçlı kullanım imkânını sağlayan Karadeniz Teknik Üniversitesi’ne, Dr. Mehmet Seyhan’a teşekkür ederim.

Kaynakça

  • Abbas, H. S., Ali, A., Hashemi, S. M., & Werner, H. (2014). LPV state-feedback control of a control moment gyroscope. Control Engineering Practice, 24, 129-137. doi: 10.1016/j.conengprac.2013.05.008
  • Ahmed, A., Adnaik, I., Bhavsar, D., & Sargar, T. S. (2016). Design and Analysis of Gyro Wheel for Stabilization of a Bicycle. International Journal for Scientific Research & Development, 4(04), 349-351.
  • Amiroh, K., Rahmawati, D., & Wicaksono, A. Y. (2021). Intelligent System for Fall Prediction Based on Accelerometer and Gyroscope of Fatal Injury in Geriatric. Jurnal Nasional Teknik Elektro, 10(3). doi: 10.25077/jnte.v10n3.936.2021
  • Anonimouse. (2023). Precession Wikipedia®. en.wikipedia.org: Wikimedia Foundation, Inc.,.
  • Ansys®. (2023). Academic Research Mechanical Products, 2021 R2, Help System, ANSYS Mechanical User's Guide: ANSYS, Inc.
  • Aranovskiy, S., Ryadchikov, I., Mikhalkov, N., Kazakov, D., Simulin, A., & Sokolov, D. (2021). Scissored pair control moment gyroscope inverted pendulum. Paper presented at the 14TH international symposium intelligent systems.
  • Chen, J., Wang, X., & Xu, X. (2022). GC-LSTM: graph convolution embedded LSTM for dynamic network link prediction. Applied Intelligence, 52(7), 7513-7528. doi: 10.1007/s10489-021-02518-9
  • Chen, X. (2003, 14-17 Dec. 2003). Modeling random gyro drift by time series neural networks and by traditional method. Paper presented at the International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.
  • Chen, X. (2004, 2004//). Modeling Temperature Drift of FOG by Improved BP Algorithm and by Gauss-Newton Algorithm. Paper presented at the Advances in Neural Networks - ISNN 2004, Berlin, Heidelberg.
  • Chen, Z., Yang, C., & Qiao, J. (2022). The optimal design and application of LSTM neural network based on the hybrid coding PSO algorithm. The Journal of Supercomputing, 78(5), 7227-7259. doi: 10.1007/s11227-021-04142-3
  • Dash, S., & Venkatasubramanian, V. (2000). Challenges in the industrial applications of fault diagnostic systems. Computers & Chemical Engineering, 24(2-7), 785-791.
  • Dong, L., Wang, J., Tseng, M.-L., Yang, Z., Ma, B., & Li, L.-L. (2020). Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model. Symmetry, 12(11). doi:10.3390/sym12111750
  • Fan, Y., Ding, H., Li, M., & Li, J. (2018). Modal Analysis of a Thick-Disk Rotor with Interference Fit Using Finite Element Method. Mathematical Problems in Engineering, 2018, 5021245. doi: 10.1155/2018/5021245
  • Farzad, A., Mashayekhi, H., & Hassanpour, H. (2019). A comparative performance analysis of different activation functions in LSTM networks for classification. Neural Computing and Applications, 31, 2507-2521. doi: 10.1007/s00521-017-3210-6
  • He, J. R., & Zhao, M. G. (2015). Control System Design of Self-balanced Bicycles by Control Moment Gyroscope. Paper presented at the Proceedings of the 2015 Chinese Intelligent Automation Conference: Intelligent Technology and Systems.
  • He, Z., Wen, T., Zhang, X., Li, H., Chen, X., & Liu, X. (2022, 25-27 Nov. 2022). Multi-physics Coupling and Thermal Network Analysis of MSCMG. Paper presented at the 2022 China Automation Congress (CAC).
  • Heris, M. K. (2015). Time-series prediction using ANFIS. The Yarpiz Project, Fuzzy Systems.
  • HosseinTabari, Kisi, O., Ezani, A., & Talaee, P. H. (2012). SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. Journal of Hydrology, 444-445, 78-89. doi: 10.1016/j.jhydrol.2012.04.007
  • Huang, J., Li, J., Oh, J., & Kang, H. (2023). LSTM with spatiotemporal attention for IoT-based wireless sensor collected hydrological time-series forecasting. International Journal of Machine Learning and Cybernetics. doi: 10.1007/s13042-023-01836-3
  • Ibrahim, M., Badran, K., & Esmat, A. (2023). Anomaly Detection for Agile Satellite Attitude Control System Using Hybrid Deep-Learning Technique. Aiaa Journal, https://doi.org/10.2514/2511.I011280. doi: 10.2514/1.I011280
  • Jamil, F., & Kim, D. (2019). Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System. Sensors, 19, 3946. doi: 10.3390/s19183946
  • Kacar, İ., Eroğlu, M. A., & Yalçın, M. K. (2021). Design and development of an autonomous bicycle. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 10(1), 364-372. doi: 10.28948/ngumuh.628580
  • Kostyuchenko, T., & Indygasheva, N. (2018). Computer-aided design system for control moment gyroscope. MATEC Web Conf., 158, 01021.
  • Kownacki, C. (2011). Optimization approach to adapt Kalman filters for the real-time application of accelerometer and gyroscope signals' filtering. Digital Signal Processing, 21(1), 131-140. doi: 10.1016/j.dsp.2010.09.001
  • Li, Y., Hu, Q., & Shao, X. (2022). Neural network-based fault diagnosis for spacecraft with single-gimbal control moment gyros. Chinese Journal of Aeronautics, 35(7), 261-273. doi: https://doi.org/10.1016/j.cja.2021.11.020
  • Miao, J., Li, X., & Ye, J. (2015, 21-23 Oct. 2015). Predicting research of mechanical gyroscope life based on wavelet support vector. Paper presented at the 2015 First International Conference on Reliability Systems Engineering (ICRSE).
  • Million, E. (2007). The Hadamard product. In R. A. Beezer (Ed.), Linear Algebra (pp. 1-7). Rob Beezer's Home Page: Buzzard.
  • Montoya-Chairez, J., Santibanez, V., & Moreno-Valenzuela, J. (2019). Adaptive control schemes applied to a control moment gyroscope of 2 degrees of freedom. Mechatronics, 57, 73-85. doi: 10.1016/j.mechatronics.2018.11.011
  • Nl, C. (2023). Gyroscope physics. Cleonis, 1(1), 1.
  • Osman, M. O. M., Sankar, S., & Dukkipati, R. V. (1982). Design synthesis of a gyrogrinder using direct search optimization. Mechanism and Machine Theory, 17(1), 33-45. doi: 10.1016/0094-114X(82)90022-2
  • Pan, S., Xu, Z., & Zhao, C. (2019). A novel single-gimbal control moment gyroscope driven by an ultrasonic motor. Advances in Mechanical Engineering, 11(4), 1687814019844382. doi: 10.1177/1687814019844382
  • Shen, L., Zhu, Y., Liu, C., Wang, W., Liu, H., Kamruzzaman, . . . Zheng, X. (2020). Modelling of moving drying process and analysis of drying characteristics for germinated brown rice under continuous microwave drying. Biosystems Engineering, 195, 64-88.
  • Shi, H., Hu, S., & Zhang, J. (2019). LSTM based prediction algorithm and abnormal change detection for temperature in aerospace gyroscope shell. International Journal of Intelligent Computing and Cybernetics, 12(2), 274-291. doi: 10.1108/IJICC-11-2018-0152
  • Song, H., Hu, S.-L., & Zhou, K.-Y. (2017). Review on Development of Fault Diagnosis for Gyroscope. ITM Web Conf., 11, 07001. doi: 10.1051/itmconf/20171107001
  • Sucuoglu, H. S., Bogrekci, I., Gultekin, A., & Demircioglu, P. (2018). Design, Analysis and Development of Mobile Robot with Flip-Flop Motion Ability. IFAC-PapersOnLine, 51(30), 436-440. doi: https://doi.org/10.1016/j.ifacol.2018.11.323
  • Sun, J., Cai, Z., Sun, J., & Jin, D. (2023). Dynamic analysis of a rigid-flexible inflatable space structure coupled with control moment gyroscopes. Nonlinear Dynamics, 111(9), 8061-8081. doi: 10.1007/s11071-023-08254-8
  • Taheri, S., Brodie, G., & Gupta, D. (2021). Optimised ANN and SVR models for online prediction of moisture content and temperature of lentil seeds in a microwave fluidised bed dryer. Computers and Electronics in Agriculture, 182, 106003. doi: 10.1016/j.compag.2021.106003
  • Tobon-Mejia, D. A., Medjaher, K., Zerhouni, N., & Tripot, G. (2012). A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models. IEEE Transactions on Reliability, 61(2), 491-503. doi: 10.1109/TR.2012.2194177
  • Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., & Yin, K. (2003). A review of process fault detection and diagnosis: Part III: Process history based methods. Computers & Chemical Engineering, 27(3), 327-346. doi: 10.1016/S0098-1354(02)00162-X
  • Xiu, T., Yue-dong, L., Xin-xiao, L., & Er-yong, H. (2021). Structural Engineering Analysis for a Control Moment Gyroscope Framework. Journal of Physics: Conference Series, 1939, 012119. doi: 10.1088/1742-6596/1939/1/012119
  • Xudong, Y., Pengfei, Z., Yuanping, X., & Xingwu, L. (2013). Forecasting method for axial ring laser gyroscope drifts in single-axis rotation inertial navigation system. High Power Laser and Particle Beams, 25(04), 847-852. doi: 10.3788/HPLPB20132504.0847
  • Yuan, X., Chen, C., Lei, X., Yuan, Y., & Muhammad Adnan, R. (2018). Monthly runoff forecasting based on LSTM–ALO model. Stochastic Environmental Research and Risk Assessment, 32(8), 2199-2212. doi: 10.1007/s00477-018-1560-y
  • Zhou, Z.-J., & Hu, C.-H. (2008). An effective hybrid approach based on grey and ARMA for forecasting gyro drift. Chaos, Solitons & Fractals, 35(3), 525-529. doi: 10.1016/j.chaos.2006.05.039
  • Zhu, R., Zhang, Y., & Bo, Q. (2000). Identification of temperature drift for FOG using RBF neural networks. 34, 222-225.

Long-short-term memory network approach to forecast the precession of a mechanical gyroscope

Yıl 2023, Cilt: 15 Sayı: 3, 179 - 193, 31.12.2023

İlyas Kacar

https://doi.org/10.29137/umagd.1293684

Öz

Mechanical gyroscopes are used to orient the fully suspended vehicles such as air and space vehicles, thanks to the gyroscopic torque they produce. It is also used in land vehicles for the autonomous balance of single or two-wheeled vehicles. Although regressions have been used for modeling existing data for many years, it has been seen that machine and deep learning methods have higher success in classification and modeling in recent years. In this study, the precession of a mechanical gyroscope was forecasted by using a long-short-term memory network, which is a deep learning method. In the model obtained, the RMSE is 0.0055 rad/s, the ME value is -0.0012 rad/s, and the R is 0.9998, and there is a high correlation between the model output and the dataset. So, it is useful to use deep learning methods in mechanical gyroscope designs.

Anahtar Kelimeler

Gyroscope Machine Learning Long short-term memory

Kaynakça

  • Abbas, H. S., Ali, A., Hashemi, S. M., & Werner, H. (2014). LPV state-feedback control of a control moment gyroscope. Control Engineering Practice, 24, 129-137. doi: 10.1016/j.conengprac.2013.05.008
  • Ahmed, A., Adnaik, I., Bhavsar, D., & Sargar, T. S. (2016). Design and Analysis of Gyro Wheel for Stabilization of a Bicycle. International Journal for Scientific Research & Development, 4(04), 349-351.
  • Amiroh, K., Rahmawati, D., & Wicaksono, A. Y. (2021). Intelligent System for Fall Prediction Based on Accelerometer and Gyroscope of Fatal Injury in Geriatric. Jurnal Nasional Teknik Elektro, 10(3). doi: 10.25077/jnte.v10n3.936.2021
  • Anonimouse. (2023). Precession Wikipedia®. en.wikipedia.org: Wikimedia Foundation, Inc.,.
  • Ansys®. (2023). Academic Research Mechanical Products, 2021 R2, Help System, ANSYS Mechanical User's Guide: ANSYS, Inc.
  • Aranovskiy, S., Ryadchikov, I., Mikhalkov, N., Kazakov, D., Simulin, A., & Sokolov, D. (2021). Scissored pair control moment gyroscope inverted pendulum. Paper presented at the 14TH international symposium intelligent systems.
  • Chen, J., Wang, X., & Xu, X. (2022). GC-LSTM: graph convolution embedded LSTM for dynamic network link prediction. Applied Intelligence, 52(7), 7513-7528. doi: 10.1007/s10489-021-02518-9
  • Chen, X. (2003, 14-17 Dec. 2003). Modeling random gyro drift by time series neural networks and by traditional method. Paper presented at the International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.
  • Chen, X. (2004, 2004//). Modeling Temperature Drift of FOG by Improved BP Algorithm and by Gauss-Newton Algorithm. Paper presented at the Advances in Neural Networks - ISNN 2004, Berlin, Heidelberg.
  • Chen, Z., Yang, C., & Qiao, J. (2022). The optimal design and application of LSTM neural network based on the hybrid coding PSO algorithm. The Journal of Supercomputing, 78(5), 7227-7259. doi: 10.1007/s11227-021-04142-3
  • Dash, S., & Venkatasubramanian, V. (2000). Challenges in the industrial applications of fault diagnostic systems. Computers & Chemical Engineering, 24(2-7), 785-791.
  • Dong, L., Wang, J., Tseng, M.-L., Yang, Z., Ma, B., & Li, L.-L. (2020). Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model. Symmetry, 12(11). doi:10.3390/sym12111750
  • Fan, Y., Ding, H., Li, M., & Li, J. (2018). Modal Analysis of a Thick-Disk Rotor with Interference Fit Using Finite Element Method. Mathematical Problems in Engineering, 2018, 5021245. doi: 10.1155/2018/5021245
  • Farzad, A., Mashayekhi, H., & Hassanpour, H. (2019). A comparative performance analysis of different activation functions in LSTM networks for classification. Neural Computing and Applications, 31, 2507-2521. doi: 10.1007/s00521-017-3210-6
  • He, J. R., & Zhao, M. G. (2015). Control System Design of Self-balanced Bicycles by Control Moment Gyroscope. Paper presented at the Proceedings of the 2015 Chinese Intelligent Automation Conference: Intelligent Technology and Systems.
  • He, Z., Wen, T., Zhang, X., Li, H., Chen, X., & Liu, X. (2022, 25-27 Nov. 2022). Multi-physics Coupling and Thermal Network Analysis of MSCMG. Paper presented at the 2022 China Automation Congress (CAC).
  • Heris, M. K. (2015). Time-series prediction using ANFIS. The Yarpiz Project, Fuzzy Systems.
  • HosseinTabari, Kisi, O., Ezani, A., & Talaee, P. H. (2012). SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. Journal of Hydrology, 444-445, 78-89. doi: 10.1016/j.jhydrol.2012.04.007
  • Huang, J., Li, J., Oh, J., & Kang, H. (2023). LSTM with spatiotemporal attention for IoT-based wireless sensor collected hydrological time-series forecasting. International Journal of Machine Learning and Cybernetics. doi: 10.1007/s13042-023-01836-3
  • Ibrahim, M., Badran, K., & Esmat, A. (2023). Anomaly Detection for Agile Satellite Attitude Control System Using Hybrid Deep-Learning Technique. Aiaa Journal, https://doi.org/10.2514/2511.I011280. doi: 10.2514/1.I011280
  • Jamil, F., & Kim, D. (2019). Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System. Sensors, 19, 3946. doi: 10.3390/s19183946
  • Kacar, İ., Eroğlu, M. A., & Yalçın, M. K. (2021). Design and development of an autonomous bicycle. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 10(1), 364-372. doi: 10.28948/ngumuh.628580
  • Kostyuchenko, T., & Indygasheva, N. (2018). Computer-aided design system for control moment gyroscope. MATEC Web Conf., 158, 01021.
  • Kownacki, C. (2011). Optimization approach to adapt Kalman filters for the real-time application of accelerometer and gyroscope signals' filtering. Digital Signal Processing, 21(1), 131-140. doi: 10.1016/j.dsp.2010.09.001
  • Li, Y., Hu, Q., & Shao, X. (2022). Neural network-based fault diagnosis for spacecraft with single-gimbal control moment gyros. Chinese Journal of Aeronautics, 35(7), 261-273. doi: https://doi.org/10.1016/j.cja.2021.11.020
  • Miao, J., Li, X., & Ye, J. (2015, 21-23 Oct. 2015). Predicting research of mechanical gyroscope life based on wavelet support vector. Paper presented at the 2015 First International Conference on Reliability Systems Engineering (ICRSE).
  • Million, E. (2007). The Hadamard product. In R. A. Beezer (Ed.), Linear Algebra (pp. 1-7). Rob Beezer's Home Page: Buzzard.
  • Montoya-Chairez, J., Santibanez, V., & Moreno-Valenzuela, J. (2019). Adaptive control schemes applied to a control moment gyroscope of 2 degrees of freedom. Mechatronics, 57, 73-85. doi: 10.1016/j.mechatronics.2018.11.011
  • Nl, C. (2023). Gyroscope physics. Cleonis, 1(1), 1.
  • Osman, M. O. M., Sankar, S., & Dukkipati, R. V. (1982). Design synthesis of a gyrogrinder using direct search optimization. Mechanism and Machine Theory, 17(1), 33-45. doi: 10.1016/0094-114X(82)90022-2
  • Pan, S., Xu, Z., & Zhao, C. (2019). A novel single-gimbal control moment gyroscope driven by an ultrasonic motor. Advances in Mechanical Engineering, 11(4), 1687814019844382. doi: 10.1177/1687814019844382
  • Shen, L., Zhu, Y., Liu, C., Wang, W., Liu, H., Kamruzzaman, . . . Zheng, X. (2020). Modelling of moving drying process and analysis of drying characteristics for germinated brown rice under continuous microwave drying. Biosystems Engineering, 195, 64-88.
  • Shi, H., Hu, S., & Zhang, J. (2019). LSTM based prediction algorithm and abnormal change detection for temperature in aerospace gyroscope shell. International Journal of Intelligent Computing and Cybernetics, 12(2), 274-291. doi: 10.1108/IJICC-11-2018-0152
  • Song, H., Hu, S.-L., & Zhou, K.-Y. (2017). Review on Development of Fault Diagnosis for Gyroscope. ITM Web Conf., 11, 07001. doi: 10.1051/itmconf/20171107001
  • Sucuoglu, H. S., Bogrekci, I., Gultekin, A., & Demircioglu, P. (2018). Design, Analysis and Development of Mobile Robot with Flip-Flop Motion Ability. IFAC-PapersOnLine, 51(30), 436-440. doi: https://doi.org/10.1016/j.ifacol.2018.11.323
  • Sun, J., Cai, Z., Sun, J., & Jin, D. (2023). Dynamic analysis of a rigid-flexible inflatable space structure coupled with control moment gyroscopes. Nonlinear Dynamics, 111(9), 8061-8081. doi: 10.1007/s11071-023-08254-8
  • Taheri, S., Brodie, G., & Gupta, D. (2021). Optimised ANN and SVR models for online prediction of moisture content and temperature of lentil seeds in a microwave fluidised bed dryer. Computers and Electronics in Agriculture, 182, 106003. doi: 10.1016/j.compag.2021.106003
  • Tobon-Mejia, D. A., Medjaher, K., Zerhouni, N., & Tripot, G. (2012). A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models. IEEE Transactions on Reliability, 61(2), 491-503. doi: 10.1109/TR.2012.2194177
  • Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., & Yin, K. (2003). A review of process fault detection and diagnosis: Part III: Process history based methods. Computers & Chemical Engineering, 27(3), 327-346. doi: 10.1016/S0098-1354(02)00162-X
  • Xiu, T., Yue-dong, L., Xin-xiao, L., & Er-yong, H. (2021). Structural Engineering Analysis for a Control Moment Gyroscope Framework. Journal of Physics: Conference Series, 1939, 012119. doi: 10.1088/1742-6596/1939/1/012119
  • Xudong, Y., Pengfei, Z., Yuanping, X., & Xingwu, L. (2013). Forecasting method for axial ring laser gyroscope drifts in single-axis rotation inertial navigation system. High Power Laser and Particle Beams, 25(04), 847-852. doi: 10.3788/HPLPB20132504.0847
  • Yuan, X., Chen, C., Lei, X., Yuan, Y., & Muhammad Adnan, R. (2018). Monthly runoff forecasting based on LSTM–ALO model. Stochastic Environmental Research and Risk Assessment, 32(8), 2199-2212. doi: 10.1007/s00477-018-1560-y
  • Zhou, Z.-J., & Hu, C.-H. (2008). An effective hybrid approach based on grey and ARMA for forecasting gyro drift. Chaos, Solitons & Fractals, 35(3), 525-529. doi: 10.1016/j.chaos.2006.05.039
  • Zhu, R., Zhang, Y., & Bo, Q. (2000). Identification of temperature drift for FOG using RBF neural networks. 34, 222-225.
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Makine Mühendisliği
Bölüm Makaleler
Yazarlar

İlyas Kacar 0000-0002-5887-8807

Yayımlanma Tarihi 31 Aralık 2023
Gönderilme Tarihi 7 Mayıs 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 15 Sayı: 3

Kaynak Göster

APA Kacar, İ. (2023). Bir mekanik jiroskopun yalpalama tahmininde uzun kısa süreli bellek ağı yaklaşımı. International Journal of Engineering Research and Development, 15(3), 179-193. https://doi.org/10.29137/umagd.1293684
Tüm hakları saklıdır. Kırıkkale Üniversitesi, Mühendislik Fakültesi.