Document Type : Reseach Article
Authors
- Waleed Hammed 1
- Ameer H. Al-Rubaye 2
- Bashar S. Bashar 3
- Merzah Kareem Imran 4
- Mustafa Ghanim Rzooki 5
- Ali Mohammed Hashesh 6
1 Medical technical college, Al-Farahidi University, Baghdad, Iraq
2 Department of Petroleum Engineering, Al-Kitab University, Altun Kupri, Iraq
3 Al-Nisour University College, Baghdad, Iraq
4 Building and Construction Engineering Technology Department, AL-Mustaqbal University College, Hillah 51001, Iraq
5 Medical Device Engineering, Ashur University College, Baghdad, Iraq
6 Al-Hadi University College, Baghdad,10011, Iraq
Abstract
Although theoretical models of manufacturing processes are useful for understanding physical events, it can be challenging to apply them in real-world industrial settings. When huge data are accessible, artificial intelligence approaches in the context of Industry 4.0 can offer effective answers to real production challenges. Deep learning is increasingly being used in the realm of artificial intelligence to address a variety of issues relating to information and communication technology, but it is still limited or perhaps nonexistent in the industrial sector. In this study, wire electrical discharge machining—a sophisticated machining technique primarily used for computer hardware components—is applied to effectively forecast unforeseen occurrences. By identifying hidden patterns in process signals, anomalies, such as changes in the thickness of a machined item, may be efficiently anticipated before they occur. In this study, a model for anomaly detection in the sequence of thickness change in the machined component based on transformers is suggested. Our method is able to achieve 94.32 % and 94.16 % accuracy in Z 135 and Z 15 datasets, respectively. Also, it forecasts the abnormalities inside the sequence 1.1 seconds in advance, according to our tests on a dataset that has been introduced.
Keywords
- [1] Wang, Jun, José A. Sánchez, Borja Izquierdo, and Izaro Ayesta. "Experimental and numerical study of crater volume in wire electrical discharge machining." Materials, Vol. 13, No. 3, p. 577, 2020.
- [2] Abu Qudeiri, Jaber E., Ahmad Saleh, Aiman Ziout, Abdel-Hamid I. Mourad, Mustufa Haider Abidi, and Ahmed Elkaseer. "Advanced electric discharge machining of stainless steels: Assessment of the state of the art, gaps and future prospect." Materials, Vol. 12, No. 6, p. 907, 2019.
- [3] Barrios, Sonia, David Buldain, María Paz Comech, Ian Gilbert, and Iñaki Orue. "Partial discharge classification using deep learning methods—Survey of recent progress." Energies, Vol. 12, No. 13, p. 2485, 2019.
- [4] El-Bahloul, Sara Ahmed. "Optimization of wire electrical discharge machining using statistical methods coupled with artificial intelligence techniques and soft computing." SN Applied Sciences, Vol. 2, No. 1, pp. 1-8, 2020.
- [5] Khan, Sarmad Ali, Mudassar Rehman, Muhammad Umar Farooq, Muhammad Asad Ali, Rakhshanda Naveed, Catalin I. Pruncu, and Waheed Ahmad. "A detailed machinability assessment of DC53 steel for die and mold industry through wire electric discharge machining." Metals, Vol. 11, No. 5, p. 816, 2021.
- [6] Umar Farooq, Muhammad, Mohammad Pervez Mughal, Naveed Ahmed, Nadeem Ahmad Mufti, Abdulrahman M. Al-Ahmari, and Yong He. "On the investigation of surface integrity of Ti6Al4V ELI using Si-mixed electric discharge machining." Materials, Vol. 13, No. 7, p. 1549, 2020.
- [7] Fujiyoshi, Hironobu, Tsubasa Hirakawa, and Takayoshi Yamashita. "Deep learning-based image recognition for autonomous driving." IATSS research, Vol. 43, No. 4, 244-252, 2019.
- [8] Pastor-Pellicer, Joan, María José Castro-Bleda, Salvador Espana-Boquera, and Francisco Zamora-Martinez. "Handwriting recognition by using deep learning to extract meaningful features." Ai Communications, Vol. 32, No. 2, pp. 101-112, 2019.
- [9] Qiu, Xipeng, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. "Pre-trained models for natural language processing: A survey." Science China Technological Sciences, Vol. 63, No. 10, pp. 1872-1897, 2020.
- Kinghorn, Philip, Li Zhang, and Ling Shao. "A hierarchical and regional deep learning architecture for image description generation." Pattern Recognition Letters, Vol. 119, pp. 77-85, 2019.
- Sharghi, Elnaz, Vahid Nourani, Hessam Najafi, and Amir Molajou. "Emotional ANN (EANN) and wavelet-ANN (WANN) approaches for Markovian and seasonal based modeling of rainfall-runoff process." Water resources management, Vol. 32, No. 10, pp. 3441-3456, 2018.
- Nourani, Vahid, Zahra Razzaghzadeh, Aida Hosseini Baghanam, and Amir Molajou. "ANN-based statistical downscaling of climatic parameters using decision tree predictor screening method." Theoretical and Applied Climatology, Vol. 137, No. 3, pp. 1729-1746, 2019.
- Nourani V, Molajou A, Tajbakhsh AD, Najafi H. “A wavelet based data mining technique for suspended sediment load modeling. Water Resources Management” , Vol. 31, pp. 1769-84, 2019.
- Nassif, Ali Bou, Ismail Shahin, Imtinan Attili, Mohammad Azzeh, and Khaled Shaalan. "Speech recognition using deep neural networks: A systematic review." IEEE access, Vol. 7, pp. 19143-19165, 2019.
- Stahlberg, Felix. "Neural machine translation: A review." Journal of Artificial Intelligence Research, Vol. 69, pp. 343-418, 2020.
- Ackerson, Joseph M., Rushit Dave, and Naeem Seliya. "Applications of recurrent neural network for biometric authentication & anomaly detection." Information, Vol. 12, No. 7, p. 272, 2021.
- McClelland, James L., and David E. Rumelhart. "Distributed memory and the representation of general and specific information." In Connectionist psychology: A text with readings, Vol. 43, pp. 75-106, 2020.
- Wang, Yaoli, Lipo Wang, Fangjun Yang, Wenxia Di, and Qing Chang. "Advantages of direct input-to-output connections in neural networks: The Elman network for stock index forecasting." Information Sciences, Vol. 547, pp. 1066-1079, 2021.
- Smagulova, Kamilya, and Alex Pappachen James. "A survey on LSTM memristive neural network architectures and applications." The European Physical Journal Special Topics 228, No. 10, pp. 2313-2324, 2019.
- Kiranyaz, Serkan, Onur Avci, Osama Abdeljaber, Turker Ince, Moncef Gabbouj, and Daniel J. Inman. "1D convolutional neural networks and applications: A survey." Mechanical systems and signal processing, Vol. 151, p. 107398, 2021.
- Sarıgül, Mehmet, Buse Melis Ozyildirim, and Mutlu Avci. "Differential convolutional neural network." Neural Networks, Vol. 116, pp. 279-287, 2019.
- Yin, Jiateng, and Wentian Zhao. "Fault diagnosis network design for vehicle on-board equipments of high-speed railway: A deep learning approach." Engineering Applications of Artificial Intelligence, Vol. 56 , 250-259, 2016.
- Canizo, Mikel, Isaac Triguero, Angel Conde, and Enrique Onieva. "Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study." Neurocomputing, Vol. 363, pp. 246-260, 2019.
- Wang, Peng, Robert X. Gao, and Ruqiang Yan. "A deep learning-based approach to material removal rate prediction in polishing." Cirp Annals, Vol. 66, No. 1, pp. 429-432, 2017.
- Khan, Salman, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah. "Transformers in vision: A survey." ACM Computing Surveys , Vol. 363, pp. 246-260, 2021.
- Dabre, Raj, Chenhui Chu, and Anoop Kunchukuttan. "A survey of multilingual neural machine translation." ACM Computing Surveys (CSUR), Vol. 53, No. 5, pp. 1-38, 2020.
- Zaheer, Manzil, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham et al. "Big bird: Transformers for longer sequences." Advances in Neural Information Processing Systems, Vol. 33, pp. 17283-17297, 2020.
- Pan, Xuran, Chunjiang Ge, Rui Lu, Shiji Song, Guanfu Chen, Zeyi Huang, and Gao Huang. "On the integration of self-attention and convolution." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vol. 32, pp. 815-825. 2022.
- Floridi, Luciano, and Massimo Chiriatti. "GPT-3: Its nature, scope, limits, and consequences." Minds and Machines, Vol. 30, No. 4, 681-694, 2020.
- Acheampong, Francisca Adoma, Henry Nunoo-Mensah, and Wenyu Chen. "Transformer models for text-based emotion detection: a review of BERT-based approaches." Artificial Intelligence Review, Vol. 54, No. 8, pp. 5789-5829, 2021.
- Bhatt, Dulari, Chirag Patel, Hardik Talsania, Jigar Patel, Rasmika Vaghela, Sharnil Pandya, Kirit Modi, and Hemant Ghayvat. "CNN variants for computer vision: History, architecture, application, challenges and future scope." Electronics, Vol. 10, No. 20, p. 2470, 2021.
- Currey, Anna, and Kenneth Heafield. "Incorporating source syntax into transformer-based neural machine translation." In Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers), Vol.12, pp. 24-33. 2019.
- Grechishnikova, Daria. "Transformer neural network for protein-specific de novo drug generation as a machine translation problem." Scientific reports, Vol. 11, No. 1, pp. 1-13, 2021.
- Basodi, Sunitha, Chunyan Ji, Haiping Zhang, and Yi Pan. "Gradient amplification: An efficient way to train deep neural networks." Big Data Mining and Analytics, Vol. 3, No. 3, pp. 196-207, 2020.
- Jha, Dipendra, Vishu Gupta, Logan Ward, Zijiang Yang, Christopher Wolverton, Ian Foster, Wei-keng Liao, Alok Choudhary, and Ankit Agrawal. "Enabling deeper learning on big data for materials informatics applications." Scientific reports, Vol. 11, No. 1, pp. 1-12, 2021.
- Gumaei, Abdu, Mohammad Mehedi Hassan, Abdulhameed Alelaiwi, and Hussain Alsalman. "A hybrid deep learning model for human activity recognition using multimodal body sensing data." IEEE Access, Vol. 7, pp. 99152-99160, 2019.
- Liu, Ze, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. "Swin transformer: Hierarchical vision transformer using shifted windows." In Proceedings of the IEEE/CVF on Computer Vision, Vol.65, pp. 10012-10022. 2021.
- Liu, Zhenhua, Yunhe Wang, Kai Han, Wei Zhang, Siwei Ma, and Wen Gao. "Post-training quantization for vision transformer." Advances in Neural Information Processing Systems, Vol. 34, pp. 28092-28103, 2021.
- Deepak, S., and P. M. Ameer. "Automated categorization of brain tumor from mri using cnn features and svm." Journal of Ambient Intelligence and Humanized Computing, Vol. 12, No. 8, 8357-8369, 2021.
- Choi, Youngkwon, Gayathri Naidu, Long D. Nghiem, Sangho Lee, and Saravanamuthu Vigneswaran. "Membrane distillation crystallization for brine mining and zero liquid discharge: opportunities, challenges, and recent progress." Environmental Science: Water Research & Technology, Vol. 5, No. 7, 1202-1221, 2019.
)