Technológiai áttekintés, csatlakozó házak feldolgozásához szükséges cella tervezés
Technological overview, design of a machine station for connector processing
Keywords:
industry 4.0, cable assembling, cooperative robots, machine vision, bin-picking, /, kábelkonfekcionálás, kooperatív robotok, gépi-látásAbstract
Increased demand in the field of cable and wire processing encourages continuous quality and innovation, the introduction of modern technologies. Hundreds of thousands of different connectors are in use through various industries actively, they are developing into a huge industry. Automation is difficult due to the many types, sizes, and shapes. However, many innovations and developments in the industry make it possible to build a universal machine that can process connectors in bulk.
Kivonat
A kábel és vezeték feldolgozás területén megnövekedett igények ösztönzik a folyamatos minőségi és gyártási innovációkat, a korszerű technológiák bevezetését. Több százezer különböző csatlakozót használnak különféle iparágak aktívan, ezek fejlesztése egy hatalmas iparág. Automatizációja nehéz a sok típus, méret és forma miatt. Az ipar számos újítása és fejlesztési iránya azonban lehetőséget ad, egy univerzális gép építésére, amely a csatlakozókat ömlesztett formában képes feldolgozni.
References
K. Feldmann, V. Schöppner, és G. Spur, Szerk., Handbuch Fügen, Handhaben, Montieren. München: Hanser, 2014
K. Reif, „Elektrische und elektronische Systeme im Kfz”, in Bosch Autoelektrik und Autoelektronik, K. Reif, Szerk. Wiesbaden: Vieweg+Teubner, 2011
K. Reif, Automobilelektronik: Eine Einführung für Ingenieure, 5., Überarb. Aufl. 2014. Wiesbaden: Springer Vieweg, 2014
Z. Bi és mtsai., „Automated testing of electrical cable harnesses”, in 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), Wuhan, 2018
J. Trommnau, J. Kühnle, J. Siegert, R. Inderka, és T. Bauernhansl, „Overview of the State of the Art in the Production Process of Automotive Wire Harnesses, Current Research and Future Trends”, Procedia CIRP, köt. 81, 2019
Matthew. Bell, „Flexible object manipulation.”, Ph.D., Dartmouth College, 2010
M. Kobayashi, Y. Hirano, és M. Higashi, „Optimization of Assembly Processes of an Automobile Wire Harness”, Computer-Aided Design and Applications, köt. 11, sz. 3, 2014
W. P. J. Pemarathne és T. G. I. Fernando, „Wire and cable routings and harness designing systems with AI, a review”, in 2016 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), Galle, Sri Lanka, 2016
Oba, „Wiring harnesses for Next Generation Automobiles”, 2013
X. Jiang, Y. Nagaoka, K. Ishii, S. Abiko, T. Tsujita, és M. Uchiyama, „Robotized recognition of a wire harness utilizing tracing operation”, Robotics and Computer-Integrated Manufacturing, köt. 34, 2015
M. Rice, H. H. Tay, J. Ng, C. Lim, S. K. Selvaraj, és E. Wu, „Comparing Three Task Guidance Interfaces for Wire Harness Assembly”, in Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, San Jose California USA, 2016
A. Pradhan, „Current Trends in Automotive Wire Harness Design”, 2011
A. Kumar, G. Chaudhary, M. Kalra, és B. K. Jha, „Optimization of cycle time for wire harness assembly – Line balancing and Kaizen approach”, IJIRSET, köt. 03, sz. 08, 2014
N. Öztürk és M. N. Esin, „Investigation of musculoskeletal symptoms and ergonomic risk factors among female sewing machine operators in Turkey”, International Journal of Industrial Ergonomics, köt. 41, sz. 6, 2011
P. Drinkaus és mtsai., „Comparison of ergonomic risk assessment outputs from Rapid Upper Limb Assessment and the Strain Index for tasks in automotive assembly plants”, Work, köt. 21, sz. 2, 2003
J. R. T. Domingo, Ma. T. S. D. Pano, D. A. G. Ecat, N. A. D. G. Sanchez, és B. P. Custodio, „Risk Assessment on Filipino Construction Workers”, Procedia Manufacturing, köt. 3, 2015
M. Massaccesi, A. Pagnotta, A. Soccetti, M. Masali, C. Masiero, és F. Greco, „Investigation of work-related disorders in truck drivers using RULA method”, Applied Ergonomics, köt. 34, sz. 4, 2003
B. M. Deros, D. D. I. Daruis, és I. M. Basir, „A Study on Ergonomic Awareness among Workers Performing Manual Material Handling Activities”, Procedia - Social and Behavioral Sciences, köt. 195, 2015
S. Sahu és M. Sett, „Ergonomic evaluation of the tasks performed by the female workers in the unorganized sectors of the manual brick manufacturing units in India”, Ergon SA, köt. 22, 2010
P. Mukhopadhyay és S. Srivastava, „Ergonomic Design Issues in Some Craft Sectors of Jaipur”, The Design Journal, köt. 13, sz. 1, 2010
E. H. Østergaard, „THE ROLE OF COBOTS IN INDUSTRY 4.0”
A. Rojko, „Industry 4.0 Concept: Background and Overview”, Int. J. Interact. Mob. Technol., köt. 11, sz. 5, 2017
A. Cherubini, R. Passama, A. Crosnier, A. Lasnier, és P. Fraisse, „Collaborative manufacturing with physical human–robot interaction”, Robotics and Computer-Integrated Manufacturing, köt. 40, 2016
M. Dudek-Burlikowska és D. Szewieczek, „The Poka-Yoke method as an improving quality tool of operations in the process”, Journal of Achievements in Materials and Manufacturing Engineering, köt. 36, sz. 1, 2009.
R. Szabo és A. Gontean, „Industrial robotic automation with Raspberry PI using image processing”, in 2016 International Conference on Applied Electronics (AE), Pilsen, Czech Republic, 2016
E. Kadiyala, S. Meda, R. Basani, és S. Muthulakshmi, „Global industrial process monitoring through IoT using Raspberry pi”, in 2017 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2), Chennai, India, 2017
H. K. és D. D., „Industrial Automation using IoT with Raspberry Pi”, IJCA, köt. 168, sz. 1, 2017
„A Brief History of 3D Printing”, in A New Industrial Future?, 1. kiad., 1 Edition. | New York : Routledge, 2016.: Routledge, 2016
A. Azanha, A. R. T. T. Argoud, J. B. de Camargo Junior, és P. D. Antoniolli, „Agile project management with Scrum: A case study of a Brazilian pharmaceutical company IT project”, IJMPB, köt. 10, sz. 1, 2017
V. Román-Ibáñez, A. Jimeno-Morenilla, F. Pujol-López, és F. Salas-Pérez, „Online simulation as a collision prevention layer in automated shoe sole adhesive spraying”, Int J Adv Manuf Technol, köt. 95, sz. 1–4, 2018
A. A. R. M. A. Ebayyeh és A. Mousavi, „A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry”, IEEE Access, köt. 8, 2020
H. G. Nguyen, M. Meiners, L. Schmidt, és J. Franke, „Deep learning-based automated optical inspection system for crimp connections”, in 2020 10th International Electric Drives Production Conference (EDPC), Ludwigsburg, Germany, 2020
M. Meiners, A. Mayr, és J. Franke, „Process curve analysis with machine learning on the example of screw fastening and press-in processes”, Procedia CIRP, köt. 97, 2021
D. Mourtzis és J. Angelopoulos, „An intelligent framework for modelling and simulation of artificial neural networks (ANNs) based on augmented reality”, Int J Adv Manuf Technol, köt. 111, sz. 5–6, 2020
D. Mourtzis, J. Angelopoulos, és N. Panopoulos, „A Framework for Automatic Generation of Augmented Reality Maintenance & Repair Instructions based on Convolutional Neural Networks”, Procedia CIRP, köt. 93, 2020
G. W. Thum, S. H. Tang, S. A. Ahmad, és M. Alrifaey, „Toward a Highly Accurate Classification of Underwater Cable Images via Deep Convolutional Neural Network”, JMSE, köt. 8, sz. 11, 2020
Z. Dai, J. Yi, Y. Zhang, B. Zhou, és L. He, „Fast and accurate cable detection using CNN”, Appl Intell, köt. 50, sz. 12, 2020
X. Ye, G. Wu, F. Fan, X. Peng, és K. Wang, „Overhead ground wire detection by fusion global and local features and supervised learning method for a cable inspection robot”, SR, köt. 38, sz. 3, 2018
X. Li, C. Gao, Y. Guo, F. He, és Y. Shao, „Cable surface damage detection in cable-stayed bridges using optical techniques and image mosaicking”, Optics & Laser Technology, köt. 110, 2019
L. Zheng, X. Liu, Z. An, S. Li, és R. Zhang, „A smart assistance system for cable assembly by combining wearable augmented reality with portable visual inspection”, Virtual Reality & Intelligent Hardware, köt. 2, sz. 1, 2020
D. De Gregorio, G. Palli, és L. Di Stefano, Let’s take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation. 2018
P. Chang és T. Padir, „Model-Based Manipulation of Linear Flexible Objects with Visual Curvature Feedback”, 2020
H. G. Nguyen és J. Franke, „Deep learning-based optical inspection of rigid and deformable linear objects in wiring harnesses”, Procedia CIRP, köt. 104, 2021
F. Spenrath és A. Pott, „Using Neural Networks for Heuristic Grasp Planning in Random Bin Picking”, in 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), Munich, Germany, 2018
J.-K. Oh, S. Lee, és C.-H. Lee, „Stereo vision based automation for a bin-picking solution”, Int. J. Control Autom. Syst., köt. 10, sz. 2, 2012
M. Moosmann és mtsai., „Increasing the Robustness of Random Bin Picking by Avoiding Grasps of Entangled Workpieces”, Procedia CIRP, köt. 93, 2020
M. Moosmann és mtsai., Using Deep Neural Networks to Separate Entangled Workpieces in Random Bin Picking. 2020
C. Martinez, R. Boca, B. Zhang, H. Chen, és S. Nidamarthi, „Automated bin picking system for randomly located industrial parts”, in 2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), Woburn, MA, USA, 2015
V. Vonasek, A. Vick, és M. Saska, „Motion planning with motion primitives for industrial bin picking”, in 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Limassol, Cyprus, 2017
T. F. Iversen és L.-P. Ellekilde, „Benchmarking motion planning algorithms for bin-picking applications”, IR, köt. 44, sz. 2, 2017
F. Spenrath, A. Spiller, és A. Verl, „Gripping Point Determination and Collision Prevention in a Bin- Picking application”, in ROBOTIK 2012; 7th German Conference on Robotics, 2012
R. Matsumura, Y. Domae, W. Wan, és K. Harada, „Learning Based Robotic Bin-picking for Potentially Tangled Objects”, in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019
G. Leão, C. M. Costa, A. Sousa, és G. Veiga, „Detecting and Solving Tube Entanglement in Bin Picking Operations”, Applied Sciences, köt. 10, sz. 7, 2020
K. Arulkumaran, M. P. Deisenroth, M. Brundage, és A. A. Bharath, „Deep Reinforcement Learning: A Brief Survey”, IEEE Signal Process. Mag., köt. 34, sz. 6, 2017
N. Heess, G. Wayne, D. Silver, T. Lillicrap, Y. Tassa, és T. Erez, „Learning Continuous Control Policies by Stochastic Value Gradients”, arXiv:1510.09142 [cs], 2015
V. Mnih, N. Heess, A. Graves, és K. Kavukcuoglu, „Recurrent Models of Visual Attention”, arXiv:1406.6247 [cs, stat], 2014
S. Levine, C. Finn, T. Darrell, és P. Abbeel, „End-to-End Training of Deep Visuomotor Policies”, arXiv:1504.00702 [cs], 2016
V. Mnih és mtsai., „Playing Atari with Deep Reinforcement Learning”, arXiv:1312.5602 [cs], 2013
I. Lenz, H. Lee, és A. Saxena, „Deep Learning for Detecting Robotic Grasps”, arXiv:1301.3592 [cs], 2014
M. Hausknecht és P. Stone, „Deep Recurrent Q-Learning for Partially Observable MDPs”, arXiv:1507.06527 [cs], 2017
