Development of a Neural Network Structure for Identifying Begin-end Points in the Assembly Process
More details
Hide details
Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala Willowa 2, 43-300 Bielsko-Biała, Poland, Poland
Submission date: 2023-01-10
Final revision date: 2023-04-15
Acceptance date: 2023-04-16
Online publication date: 2023-04-19
Publication date: 2023-06-12
Corresponding author
Izabela Kutschenreiter-Praszkiewicz   

Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala Willowa 2, 43-300 Bielsko-Biała, Poland, Poland
Journal of Machine Engineering 2023;23(2):100-109
The paper presents an approach to video-based assembly analysis using machine learning. A neural network is one of the machine learning methods that is widely studied in many engineering fields. The purpose of this paper is to develop a deep neural network structure for identifying begin-end points for a selected component assembly process. A neural network structure that effectively identifies begin-end points is proposed and an example from industry is presented. The proposed approach can prove useful in the assembly process analysis.
PUTNIK G., SHAH V., PUTNIK Z., FERREIRA L., 2020, Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – It Does not Mean Total Automation, Human is Still in the Centre: Part I – Manufacturing Singularity and an Intelligent Machine Architecture, Journal of Machine Engineering, 20/4, 161–184,
PUTNIK G., SHAH V., PUTNIK Z., FERREIRA L., 2021, Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – It Does not Mean Total Automation, Human is Still in the Centre: Part II – In-CPS and a View from Community on Industry 4.0 Impact on Society, Journal of Machine Engineering, 21/1, 133–153,
UHLMANN E., POLTE M., BLUMBERG J., KRAFT Z., 2021, Hyperparameter Optimization of Artificial Neural Networks to Improve the Positional Accuracy of Industrial Robots, Journal of Machine Engineering, 21/2, 47–59,
LÜ Q., LIU T., ZHANG R. et al., 2022, Generation Approach of Human-Robot Cooperative Assembly Strategy Based on Transfer Learning, J. Shanghai Jiaotong Univ. (Sci.), 27, 602–613.
WANG P., LIU H., WANG L., GAO R., 2018, Deep Learning-Based Human Motion Recognition for Predictive Context-Aware Human-Robot Collaboration, CIRP Annals – Manufacturing Technology, 67, 17–20.
SINHA S., FRANCIOSA P., CEGLAREK D., 2020, Object Shape Error Response Using Bayesian 3D Convolutional Neural Networks for Assembly Systems with Compliant Parts, IEEE 18th International Conference on Industrial Informatics (INDIN), Warwick, United Kingdom, 104–109,
SINHA S., FRANCIOSA P., CEGLAREK D., 2021, Building a Scalable and Interpretable Bayesian Deep Learning Framework for Quality Control of Free Form Surfaces, IEEE Access, 9, 50188–50208,
COPOT C., BURLACU A., LAZAR C., 2009, Image Moments Based Visual Control Algorithm for Servoing Systems, IEEE 5th International Conference on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania, 157–160,
CHAUMETTE F.,2004, Image Moments: a General and Useful set of Features for Visual Servoing, in IEEE Transactions on Robotics, 20/4, 713–723,
WANG F., CUI B., LIU Y., REN B., 2022, Deep Reinforcement Learning for Peg‑in‑hole Assembly Task Via Information Utilization Method, Journal of Intelligent & Robotic Systems, 106, 16.
GROOVER M., 2016, Work Systems, the Methods,Measurement and Management of Work, Pearson.
KUTSCHENREITER-PRASZKIEWICZ I., 2015, Time Study, Wydawnictwo Naukowe Akademii Techniczno-Humanistycznej, (in Polish).
GAVALI P., BANU S., 2019, Chapter 6 – Deep Convolutional Neural Network for Image Classification on CUDA Platform, Editor(s): Arun Kumar Sangaiah, Deep Learning and Parallel Computing Environment for Bioengineering Systems, Academic Press, 99–122.
BURLACU A., COPOT C.,·LAZAR C., 2014, Predictive Control Architecture for Real-Time Image Moments Based Servoing of Robot Manipulators, J. Intell. Manuf., 25, 1125–1134.
MARCHAND E., CHAUMETTE F., 2005, Feature Tracking for Visual Servoing Purposes, Robotics and Autonomous Systems, 52/1, 53–70.
CHAUMETTE, F.,1998, Potential Problems of Stability and Convergence in Image-Based and Position-Based Visual Servoing, The Confluence of Cision and Control, 66–78.
SANGEETHA D., DEEPA P.,2017, A Low-Cost and High-Performance Architecture for Robust Human Detection Using Histogram of Edge Oriented Gradients, Microprocessors and Microsystems, 53, 106–119.
MADHAVAN S., JONES M., 2017, Deep Learning Architectures. The Rise of Artificial Intelligence,
BOESCH G., 2023, Image Recognition: The Basics and Use Cases,
LECUN Y, BENGIO Y, HINTON G., 2015, Deep Learning, Nature, 521(7553), 436–444, nature14539.
CIRESAN D., MEIER U., SCHMIDHUBER J., 2012, Multi-Column Deep Neural Networks for Image Classification, IEEE Conference on Computer Vision and Pattern Recognition, 3642–3649.
KRIZHEVSKY A., SUTSKEVER I., HINTON G., 2012, ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems, 25, 1097–1105.
Long Short-Term Memory (LSTM)
ALVES G., 2018, Discovering SOM, An Unsupervised Neural Network, https://Medium.Com/Neuronio/-D....
ZAMORA-HERNANDEZ M., CASTRO-VARGAS J., AZORIN-LOPEZ J., GARCIA-RODRIGUEZ J., 2021, Deep Learning-Based Visual Control Assistant for Assembly in Industry 4.0, Computers in Industry 131,103485.
KRÜGER J., LEHR J., SCHLÜTER M., BISCHOFF N., 2019, Deep Learning for Part Identification Based on Inherent Features, CIRP Annals – Manufacturing Technology, 68, 9–12.
SRIVASTAVA N., HINTON G., KRIZHEVSKY A., SUTSKEVER I., SALAKHUTDINOV R., 2014, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Journal of Machine Learning Research, 15, 1929–1958.
BISHOP C., 2006, Pattern Recognition and Machine Learning, Springer.
Journals System - logo
Scroll to top