A concept for a production flow control system toolset for discrete manufacturing of mechanical products
Article Sidebar
Open full text
Issue Vol. 21 No. 1 (2025)
-
A multi-modal transformer-based model for generative visual dialog system
Ghada ELSHAMY, Marco ALFONSE, Islam HEGAZY, Mostafa AREF1-17
-
Spatial identification of manipulable objects for a bionic hand prosthesis
Yurii LOBUR, Kostiantyn VONSEVYCH, Natalia BEZUGLA18-30
-
Numerical modelling and comparison of SIF in pipelines exposed to internal pressure with longitudinal crack using XFEM method
Aya BARKAOUI, Mohammed EL MOUSSAID, Hassane MOUSTABCHIR31-43
-
Machine learning evidence towards eradication of malaria burden: A scoping review
Idara JAMES, Veronica OSUBOR44-69
-
Harnessing multi-source data for AI-driven oncology insights: Productivity, trend, and sentiment analysis
Wissal EL HABTI, Abdellah AZMANI70-82
-
The evolution and impact of artificial intelligence in market analysis: A quantitative bibliometric exploration of the past thirty-five (35) years
Donalson WILSON, Abdellah AZMANI83-96
-
Structural equation modeling (SEM) in Jamovi: An example of analyzing the impact of factors on the innovation activity of enterprises
Assel SADENOVA, Oxana DENISSOVA, Marina KOZLOVA, Saule RAKHIMOVA, Arkadiusz GOLA, Saltanat SUIEUBAYEVA97-110
-
Enhancing intrusion detection systems: Innovative deep learning approaches using CNN, RNN, DBN and autoencoders for robust network security
Yakub HOSSAIN, Zannatul FERDOUS, Tanzillah WAHID, Md. Torikur RAHMAN, Uttam Kumar DEY, Mohammad Amanul ISLAM111-125
-
A systematic literature review of diabetes prediction using metaheuristic algorithm-based feature selection: Algorithms and challenges method
Sirmayanti, Pulung Hendro PRASTYO, Mahyati, Farhan RAHMAN126-142
-
A concept for a production flow control system toolset for discrete manufacturing of mechanical products
Jarosław CHROBOT143-153
-
Optimizing customer relationship management through AI for service effectiveness: Systematic literature review
Aji HARTANTO, VERONICA, Danang PRIHANDOKO153-163
-
LANA-YOLO: Road defect detection algorithm optimized for embedded solutions
Paweł TOMIŁO164-181
Archives
-
Vol. 21 No. 3
2025-10-05 12
-
Vol. 21 No. 2
2025-06-27 12
-
Vol. 21 No. 1
2025-03-31 12
-
Vol. 20 No. 4
2025-01-31 12
-
Vol. 20 No. 3
2024-09-30 12
-
Vol. 20 No. 2
2024-08-14 12
-
Vol. 20 No. 1
2024-03-30 12
-
Vol. 19 No. 4
2023-12-31 10
-
Vol. 19 No. 3
2023-09-30 10
-
Vol. 19 No. 2
2023-06-30 10
-
Vol. 19 No. 1
2023-03-31 10
-
Vol. 18 No. 4
2022-12-30 8
-
Vol. 18 No. 3
2022-09-30 8
-
Vol. 18 No. 2
2022-06-30 8
-
Vol. 18 No. 1
2022-03-30 7
-
Vol. 17 No. 4
2021-12-30 8
-
Vol. 17 No. 3
2021-09-30 8
-
Vol. 17 No. 2
2021-06-30 8
-
Vol. 17 No. 1
2021-03-30 8
Main Article Content
DOI
Authors
Abstract
Requirements for product traceability in certain industrial sectors make Production Flow Control Systems (PFC) a desirable component in the operation of production enterprises. Such a system serves as a valuable tool for companies by preventing defective products from being sent to customers, enabling the automatic blocking of defective parts once the cause of the defect is identified. This article discusses a proposed PFC system toolset that meets fundamental industrial requirements in the field of discrete manufacturing of mechanical products. The system integrates key elements such as rework, disassembly, single non-controlled stations, as well as various essential and optional software applications and modules.
Keywords:
References
Agnusdei, G. P., Coluccia, B., Elia, V., & Miglietta P. P. (2022). IoT technologies for wine supply chain traceability: Potential application in the Southern Apulia Region (Italy). Procedia Computer Science, 200, 1125-1134. https://doi.org/10.1016/j.procs.2022.01.312 DOI: https://doi.org/10.1016/j.procs.2022.01.312
Argilovski, A., Jovanoski, B., Minovski, R., & Peneva, G. (2023). Product traceability in manufacturing - A review of the concepts for enhanced digital transformation. XXI International Scientific Conference „Management and Engineering '23" (ISCME).
Cheshmberah, M., & Beheshtikia, S. (2020). Supply chain management maturity: An all-encompassing literature review on models, dimensions and approaches. Logforum, 16(1), 8. http://dx.doi.org/10.17270/J.LOG.2020.377 DOI: https://doi.org/10.17270/J.LOG.2020.377
Chrobot, J. (2023). Requirements for flow control systems for discrete production of mechanical products. In A. Burduk, A. Batako, J. Machado, R. Wyczółkowski, K. Antosz, & A. Gola (Eds.), Advances in Production (Vol. 790, pp. 255–267). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-45021-1_19 DOI: https://doi.org/10.1007/978-3-031-45021-1_19
Conti, M. (2022). EVO-NFC: Extra virgin olive oil traceability using NFC suitable for small-medium farms. IEEE Access, 10, 20345–20356. https://doi.org/10.1109/ACCESS.2022.3151795 DOI: https://doi.org/10.1109/ACCESS.2022.3151795
Costa, F., do Sameiro Carvalho, M., Fernandes, J. M., Alves, A. C., & Silva, P. (2017) Improving visibility using RFID – the case of a company in the automotive sector. Procedia Manufacturing, 13, 1261-1268. https://doi.org/10.1016/j.promfg.2017.09.048 DOI: https://doi.org/10.1016/j.promfg.2017.09.048
Kuhn, M., Funk, F., & Franke, J. (2021). Blockchain architecture for automotive traceability. Procedia CIRP, 97, 390-395. https://doi.org/10.1016/j.procir.2020.05.256 DOI: https://doi.org/10.1016/j.procir.2020.05.256
Kumar, G. (2024). Drug traceability - divine or challenge for pharma sector. International Journal of Scientific Research, Computer Science, Engineering and Information Technology, 10(1), 154-159. https://doi.org/10.32628/CSEIT2410126 DOI: https://doi.org/10.32628/CSEIT2410126
Leal, F., Chis, A. E., Caton, S., González–Vélez, H., García–Gómez, J. M., Durá, M., Sánchez–García, A., Sáez, C., Karageorgos, A., Gerogiannis, V. C., Xenakis, A., Lallas, E., Ntounas, T., Vasileiou, E., Mountzouris, G., Otti, B., Pucci, P., Papini, R., Cerrai, D., & Mier, M. (2021). Smart pharmaceutical manufacturing: Ensuring end-to-end traceability and data integrity in medicine production. Big Data Research, 24, 100172. https://doi.org/10.1016/j.bdr.2020.100172 DOI: https://doi.org/10.1016/j.bdr.2020.100172
Mitsiaki, A., Dimitriou, N., Margetis, G., Konstantinos, V., & Tzovaras, D. (2023). Enhancing defect traceability and data integrity Industry 4.0 using blockchain. 10th ECCOMAS Thematic Conference on Smart Structures and Materials (pp. 1173-1184). http://dx.doi.org/10.7712/150123.9866.443273 DOI: https://doi.org/10.7712/150123.9866.443273
Sarkar, S. (2022) Digital traceability of pharmaceutical drugs in supply chain. International Journal of Advance Research in Computer Science and Management, 10(2), 39-44.
Sarkar, S. (2024). The future of digital drug traceability in the global supply chain. World Journal of Clinical Medicine Research, 4(1), 1–6. http://dx.doi.org/10.31586/wjcmr.2024.896 DOI: https://doi.org/10.31586/wjcmr.2024.896
Schuitemaker, R., & Xu, X. (2020). Product traceability in manufacturing: A technical review. Procedia CIRP, 93, 700-705. https://doi.org/10.1016/j.procir.2020.04.078 DOI: https://doi.org/10.1016/j.procir.2020.04.078
Steghöfer, J.-P., Koopmann, B., Becker, J. S., Törnlund, M., Ibrahim, Y., & Mohamad, M. (2021). Design decisions in the construction of traceability information models for safe automotive systems. IEEE 29th International Requirements Engineering Conference (RE) (pp. 185-196). IEEE. http://dx.doi.org/10.1109/RE51729.2021.00024 DOI: https://doi.org/10.1109/RE51729.2021.00024
Xiao, X. (2021). Improved traceability process for frozen tilapia waste elimination in cold chain. Cleaner Engineering and Technology, 4, 100148. https://doi.org/10.1016/j.clet.2021.100148 DOI: https://doi.org/10.1016/j.clet.2021.100148
Zheng, M., Zhang, S., Zhang, Y., & Hu, B. (2020). Construct food safety traceability system for people’s health under the internet of things and big data. IEEE Access, 9, 70571–7058. http://dx.doi.org/10.1109/ACCESS.2021.3078536 DOI: https://doi.org/10.1109/ACCESS.2021.3078536
Article Details
Abstract views: 205
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
