Cogent Engineering

Integration of Digital Twin and IIoT in Manufacturing

Cogent Engineering is pleased to welcome you to submit your research to the Article Collection "Integration of Digital Twin and IIoT in Manufacturing".

The integration of Digital Twin technology with the Industrial Internet of Things (IIoT) is becoming a central theme in modern manufacturing. A Digital Twin acts as a virtual copy of a machine, process, or production line, allowing engineers to observe behaviour and performance in real time. When this is linked with IIoT sensors and connected devices, manufacturers gain a clearer picture of their operations and can respond quickly to changes on the shop floor. This combination is helping industries shift toward smarter and more flexible production environments where decisions are guided by accurate data rather than assumptions.

This topic is important because it opens the door to more efficient, reliable, and safer manufacturing systems. With real-time data flowing from IIoT devices, a Digital Twin can detect unusual patterns early and help prevent breakdowns, reducing both downtime and maintenance cost. It also supports better use of resources, improved product quality, and more consistent production output. In today’s competitive market, where supply chains are uncertain and customer demands change rapidly, the ability to adapt quickly is essential. Integrating Digital Twin and IIoT gives manufacturers a practical way to strengthen their processes and achieve a higher level of automation without compromising stability.

This Article Collection will focus on research that fits the journal’s scope on digital manufacturing, cyber-physical systems, and smart industry applications. Articles may include work on Digital Twin design, data use, and system integration. Relevant subjects and subtopics include:

• Digital Twin modelling, calibration, and validation
• IIoT-based data collection and communication systems
• Predictive maintenance and condition monitoring
• Intelligent and adaptive process control
• Machine learning and data-driven optimisation within Digital Twin environments
• Cloud, edge, and hybrid system architectures
• System interoperability and standards for digital manufacturing
• Cybersecurity and data management for IIoT-connected systems
• Sustainability and energy efficiency improvements supported by Digital Twins

The Article Collection welcomes original research articles and review papers that show practical relevance or experimental support within real or simulated manufacturing settings.

Meet the Guest Advisors

Tanveer Saleh is a Professor of Engineering at the International Islamic University Malaysia. He has more than 15 years of teaching and research experience (in Singapore, Canada, and Malaysia) and has taught various courses at undergraduate and postgraduate levels. His major research areas include non-conventional micromachining and manufacturing automation.

Muhommad Azizur Rahman is an Associate Professor at the BRAC University. He has more than 19 years of corporate, research, and academic experience in Singapore, Canada, and Bangladesh. His major research areas include advanced manufacturing, procurement, planning, supply chain, logistics, and production management.

The Guest Advisors declare no conflict of interest regarding this work.