Document Type
Article
Publication Date
12-17-2025
Department
Department of Applied Computing
Abstract
This study investigates the implementation of Industry 4.0 paradigms in the context of smart paint manufacturing, focusing on process control and recipe management through the integration of Ignition SCADA, a Siemens programmable logic controller (PLC), and a MySQL Workbench database. The developed architecture employs Ignition as an interoperable communication interface that facilitates bidirectional data exchange between the PLC and the database, thereby establishing a cyber-physical system for automated monitoring and control. The digital recipe management module formalizes paint formulations into parameterized datasets specifying paint and solvent ingredient ratios, process variables, and operational constraints, which are executed autonomously by the control system to ensure uniformity, traceability, and adaptive process optimization. The simulation framework replicates real-time production dynamics, addressing variability in raw-material supply and process conditions through data-driven feedback mechanisms. The results demonstrate the feasibility of achieving enhanced process efficiency, product quality, and scalability via the convergence of SCADA, PLC, and database technologies within an Industry 4.0 environment. This work contributes to the advancement of smart manufacturing methodologies and provides a replicable model for digital transformation in process industries.
Publication Title
Advances in Mechanical Engineering
Recommended Citation
Michael, S.,
Atieh, A.,
Nkwocha, E.,
&
Rawashdeh, N.
(2025).
Cyber-physical framework for smart paint manufacturing: Hybrid integration of PLC and recipe management simulation.
Advances in Mechanical Engineering,
17(12).
http://doi.org/10.1177/16878132251406501
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/2211
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Version
Publisher's PDF
Publisher's Statement
© The Author(s) 2025. Publisher’s version of record: https://doi.org/10.1177/16878132251406501