Electronics Engineering and Industrial Automation

Background

Industrial automation, the cornerstone of Industry 4.0, represents the shift from manual and mechanized production to integrated, intelligent, and self-regulating cyber-physical systems. Its evolution spans mechanization, electrification, digitalization, and now cognitive computing, where machines communicate, analyze data, and make autonomous decisions. The research area is fundamentally interdisciplinary, intersecting electrical, mechanical, and computer engineering with control theory and data science. Current research focuses on enhancing system autonomy, interoperability, and intelligence. Key sub-domains include the Industrial Internet of Things (IIoT) for real-time data exchange, collaborative robotics (cobots) for human-machine teamwork, artificial intelligence for predictive maintenance and adaptive control, and digital twins for simulation and optimization. Challenges being addressed involve cyber security, system resilience, human-robot interaction, and the ethical integration of AI. This research drives transformative improvements in productivity, safety, and sustainability across global manufacturing, energy, and logistics sectors, positioning automation as essential to future industrial competitiveness.

The symposium, which serves as a specialized session of the 4th International Conference on Mechatronics and Smart Systems (CONF-MSS 2026), will focus on electronics and automation.

Goal/Rationale

A central problem in contemporary industrial automation is the scalability and resilience of heterogeneous cyber-physical systems. While smart factories deploy interconnected devices, robots, and AI-driven analytics, these systems often operate in isolated "automation silos" with proprietary protocols, hindering seamless integration, real-time interoperability, and agile adaptation to dynamic production demands. This fragmentation limits predictive maintenance, increases downtime, and obstructs the realization of fully autonomous, reconfigurable manufacturing lines. To address this, a convergence of open-architecture platforms, edge intelligence, and digital twin technology is required. Recent advances in OPC UA (Open Platform Communications Unified Architecture) as a vendor-neutral data-exchange standard enable semantic interoperability across machines and software layers. Simultaneously, embedding AI at the edge—using lightweight machine learning models on local devices—facilitates real-time decision-making and reduces cloud dependency. Furthermore, synchronized digital twins, which mirror physical systems in a virtual environment, allow for simulation-based optimization, predictive analytics, and safe testing of process adjustments before deployment. Integrating these advances into a unified framework can foster resilient, self-optimizing production systems. Future efforts should prioritize standardization, cyber security by design, and human-centric AI interfaces to ensure scalable, secure, and sustainable automation ecosystems.

Scope

This research delineates its scope within the domain of advanced industrial cyber-physical systems, focusing on the conceptualization, integration, and systemic optimization of intelligent, interconnected architectures for autonomous process control. The investigation spans the complete technological stack, encompassing field-level sensor-actuator networks, control-level programmable logic, supervisory-level data acquisition and human-machine interfaces, and their integration with enterprise-level planning systems. Furthermore, it critically engages with the socio-technical dimensions of Industry, examining the broader implications of technological adoption. Contributors are specifically encouraged to submit research addressing:

• Investigating barriers and developing protocols for vendor-agnostic data interoperability, with emphasis on semantic modeling and open communication standards.
• Exploring the deployment of embedded artificial intelligence and lightweight machine learning models for decentralized, real-time process optimization and prognostic health management.
• Examining safety protocols, ethical design principles, and performance metrics for effective human-robot co-working in dynamic industrial environments.
• Proposing novel defensive frameworks and risk mitigation strategies to secure interconnected industrial control systems against sophisticated threats.
• Developing methodologies and metrics for enhancing the energy efficiency, material circularity, and overall environmental sustainability of automated production systems.