Design and Construction of a Handling Station to Interlace PCBs
Main Article Content
Abstract
This study discusses the design and construction of an automatic handling station to interlace two-part printed circuit boards (PCBs), with special attention to the methodology of constructing its handling nodes. The valuable result of this work is a presentation of the acquired cognitive experience with centric grippers and a new approach to gripping fingers. At the same time, the work describes an overview of the current state of the mechanisms of single-purpose machines. The task is to design a structure of a horizontal manipulator, a stopper for the PCB bed plates, a two-axis manipulator and a gripping head focusing on a sustainable production process. The concept of individual parts' arrangement, interactions, and resulting parameters have been developed following the stated requirements. The design meets all requirements in terms of simplicity of construction, adjustment, reliability of grasping, non-damage of the manipulated part, and sustainability of the whole manufacturing process.
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access). As soon as the paper is accepted, finally submitted and edited, the paper will appear in the "OnlineFirst" page of the journal, thus from this point no other internet-based publication is necessary.
References
Blatnická, M., Sága, M., Blatnický, M., Dižo, J. (2018). Analysis of adaptive gripper effector. Proceedings of the 22nd International Scientific on Conference Transport Means 2018. 522–526.
Blatnický, M., Dižo, J., Blatnická, M. (2018a). Structural design of soldering station chain conveyor working positions. Proceedings of the 22nd Slovak-Polish Scientific Conference on Machine Modelling and Simulations, MMS 2017. 01002. DOI: 10.1051/matecconf/201815701002
Blatnický, M., Dižo, J., Blatnická, M. (2018b). Transport machine design for adaptive gripping of automotive industry products. Proceedings of the 22nd International Scientific on Conference Transport Means 2018, Part III. 1199–1203. URL: https://transportmeans.ktu.edu/wp-content/uploads/sites/307/2018/02/Transport-means-A4-III-2018-09-25-persp.pdf
Blatnický, M., Dižo, J., Barta, D., Rybicka, I. (2020a). Engineering design of a manipulator for mounting an air suspension compressor to a car chassis. Scientific Journal of Silesian University of Technology. Series Transport. 109, 05–16. DOI: 10.20858/sjsutst.2020.109.1
Blatnický, M., Dižo, J., Sága, M., Gerlici, J., Kuba, E. (2020b). Design of a mechanical part of an automated platform for oblique manipulation. Applied Sciences. 10(23), 8467. DOI: 10.3390/app10238467
Blatnický, M., Dižo, J., Gerlici, J., Sága, M., Lack, T., Kuba, E. (2020c). Design of a robotic manipulator for handling products of automotive industry. International Journal of Advanced Robotic Systems. 17(1), 1–11. DOI: 10.1177/1729881420906290
Bosch Rexroth (2024). Rfid System Sales Catalog Available on: https://m.boschrexroth.com/en/xc/products/product-groups/assemblytechnology/topics/rfid-systems/index (Downloaded 12 September 2024 09:49)
Callegari, M., Carbonari, L., Palmieri, G., Palpacelli, M.-C. (2020). Functional design of a manipulator for the automation of laboratory precision tasks. International Journal of Mechanics and Control. 21(2), 29–37.
Chen, Q., Qi, Y., Chen, G., Liu, J., Wang, Yu., Gao, Z. (2021). Design of positioning and mapping system for space station mobile robot based on ROS. Proceeding - 2021 China Automation Congress, CAC 2021. 3008–3012. DOI: 10.1109/CAC53003.2021.9727739
Ciubucciu, G., Solea, R., Filipescu, A., Filipescu, A. (2017). Visual servoing and obstacle avoidance method based control autonomous robotic systems servicing a mechatronics manufacturing line. Proceedings of the 2017 IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2017. 874–879. DOI: 10.1109/IDAACS.2017.8095212
D'Souza, F., Costa, J., Pires, J. N. (2020). Development of a solution for adding a collaborative robot to an industrial AGV. Industrial Robot. 47(5), 723–735. DOI: 10.1108/IR-01-2020-0004
Euchner.cz (2024a). Clampless Metal Case Available on: http://www.euchner.cz/data/pdf/Download/Katalogy/safety/Euchner_elmech_bezp_spinace_kovove_pouzdro.pdf (Downloaded: 8 August 2024 08:58)
Euchner.cz (2024b). Security Clips Available on: http://www.euchner.cz/produkty/bezpecnost/bezpecnostnispinace/ (Downloaded: 9 September 2024 15:40)
Festo (2024), Electrical Automation Available on: http://www.elektroprumysl.cz/automatizace/vsechno-elektricky (Downloaded: 10 September 2024 08:48)
Glucina, M., Andelic, N, Lorencin, I., Car, Z. (2023). Detection and Classification of Printed Circuit Boards Using YOLO Algorithm. Electronics. 12(3), 667. DOI: 10.3390/electronics12030667
Han, S., Li, M., Luo, O., Duan, M., Ma, X. (2022). Design alternative for automobile door trim strip production line. Proceedings of the 37th International Conference of the Polymer Processing Society, PPS 2022. 193724. DOI: 10.1063/5.0168903
Homza, G., Barkallah, M., Louati, J., Haddar, M. (2024). Preliminary Design for the Vibration Analysis of a PCB Model: An Analytical Approach. Proceedings of the 4th International Conference on Advanced Materials Mechanics and Manufacturing (A3M2023). 320–326. DOI: 10.1007/978-3-031-57324-8_34
Kahn, R.U., Shah, F., Kahn, A.A., Tahir, H. (2024). Advancing PCB Quality Control: Harnessing YOLOv8 Deep Learning for Real-Time Fault Detection. Computers, Materials and Continua. 81(1), 345–367. DOI: 10.32604/cmc.2024.054439
Linear drives (2024). Linear Actuators Available on: https://www.festo.com/cat/en-gb_gb/data/doc_SK/PDF/SK/DGC-K_SK.PDF (Downloaded: 1 August 2024 12:03)
Naito, K., Shirai, A., Kaneko, S., Capi, G. (2021). Recycling of printed circuit boards by robot manipulator: A Deep Learning Approach. Proceedings of the IEEE International Symposium on Robotic and Sensors Environments, ROSE 2021. DOI: 10.1109/ROSE52750.2021.9611773
Peng, J., Wang, D., Zhai, J., Teng, Y., Kimmig, A., Tao, X., Ovtcharova, J. (2025). Meta-learning enhanced adaptive robot control strategy for automated PCB assembly. Journal of Manufacturing Systems. 78, 46–57. DOI: 10.1016/j.jmsy.2024.11.009
Szalai, S., Herold, B., Kurhan, D., Németh, A., Sysyn, M., Fischer, S. (2023a). Optimization of 3D Printed Rapid Prototype Deep Drawing Tools for Automotive and Railway Sheet Material Testing. Infrastructure. 8(3), 43. DOI: 10.3390/infrastructures8030043
Szalai, S., Szívós, B. F., Kurhan, D., Németh, A., Sysyn, M., Fischer, S. (2023b). Optimization of Surface Preparation and Painting Processes for Railway and Automotive Steel Sheets. Infrastructure. 8(2), 28. DOI: 10.3390/infrastructures8020028
Venkatesan, V., Cappelleri, D. J. (2017). Development of an automated flexible micro-soldering station. Proceedings of the ASME Design Engineering Technical Conference. 131761. DOI: 10.1115/DETC2017-68107
Zhang, A., Kandubai, R. V. P. P., Hammarberg, S. (2022). A Functional Retro-Fitting Robotic Smart Lock Manipulator. International Journal of Mechanical Engineering and Robotics Research. 11(3), 123–128. DOI: 10.18178/ijmerr.11.3.123-128
Zhang, J. T., Shi, X. Y., Qu, D., Xu, H. D., Chang, Z. F. (2024). PCB Defect Recognition by Image Analysis using Deep Convolutional Neural Network. Journal of Electronic Testing-Theory and Applications. 40, 657–667. DOI: 10.1007/s10836-024-06145-3