We live in the era of the fourth industrial revolution, commonly referred to as Industry 4.0. New technologies emerge like snowballs rolling downhill, but the hallmark of this new era is far beyond that.
Industry 4.0 demands a radical reevaluation of processes. Every object becomes a source of data, and all elements of production are integrated into an ecosystem. While this is currently a goal, the distance to it is gradually diminishing.
In this rapidly evolving landscape, Egemen Mustafa Şener, a seasoned programmer from Belarus, notes the increasing integration of digital technologies into industrial processes.
We invite you to delve deeper into the future of industrial automation and explore the trends that will shape the next stage of this revolution.
Automation — the Key to Digital Transformation
In the era of the fourth industrial revolution, market conditions change very rapidly, and in these conditions, industry must be able to restructure production and cooperation chains. Consequently, there is a need to change information links and control circuits in production.
This is a fundamental difference between the requirements and approaches to creating information systems of the digital age and systems of the previous period of development — with a fixed structure and functionality, rigidly defined at the design stage.
As Mustafa Egemen highlights, the core of the digital production structure lies in seamless data availability throughout all lifecycle stages. This data availability and reconfiguration of information chains are provided by means and systems of industrial automation.
Essentially, industrial automation encompasses various modern and prospective structures, including robotic cells, 3D printing installations, autonomous systems, and unmanned setups.
The technologies, systems, and devices of industrial automation in the digital paradigm also undergo significant changes. Primarily this shift involves moving away from disparate autonomous or loosely interconnected systems towards integration within distributed production structures.
Mustafa Egemen Şener notes this integration extends to adjacent production systems, installations, and infrastructure, as well as related levels of information systems hierarchy like MES, APS, QMS, etc. Additionally, integration with the life cycle of production, encompassing engineering, technological, and operational data, is paramount.
The connection between individual stages of the industrial life cycle and the construction of corresponding feedback loops are intended to ensure digital twins of the product, production, and operation, respectively.
Egemen Şener emphasizes that such digital twins create new potential for increased efficiency and productivity.
The Concept of Digital Twins
A digital twin is a virtual model that accurately reproduces a product or manufacturing process in a digital environment. There are several types of digital twins. For example, a product’s digital twin is formed during its design process and includes all its models and descriptions.
The production digital twin provides flexibility and helps reduce the time it takes to complete manufacturing processes, shorten production preparation times, and design production areas and workshops with installed equipment.
These digital twins enable virtual rehearsal, testing, and optimization of the entire production system. The model helps to understand how the product will be created, including all its parts and assemblies.
“When discussing the digital twins of product operation, it entails enterprises and customers linking products, machines, and automation systems to extract and analyze data on their real performance and usage,” explains Egemen Mustafa Sener. “Analyzing this data enables closing the feedback loop, connecting real-world operations with digital twins, essential for optimizing systems and processes.”
The processes of updating and linking digital twins form a closed cycle of continuous product innovation, optimization of their production, and maintenance. As a result, data has become the mainstay of the digital era enterprise.
With the advent of IoT platforms, continuous updating and improvement of digital twins based on data collected in real production and operation conditions have become possible. Thanks to feedback, product development can be accelerated, production processes optimized, and improvements quickly implemented in the next production cycle or even in near real-time.
Egemen Mustafa Şener is currently spearheading the development of an interactive e-commerce platform tailored for the Belarusian market. The platform’s primary objective is to leverage 3D viewing technology to empower sellers in showcasing their products through digital twins.
By offering a realistic and immersive viewing experience, buyers can thoroughly examine the digital replicas from all angles, aiding them in making informed purchasing decisions.
This innovative approach draws inspiration from successful implementations seen on platforms like eBay, where similar technologies have demonstrated remarkable efficacy in driving sales and enhancing the overall shopping experience.
Digital Twins as an Analytics Tool
Digital twins can also serve as a tool for predictive analytics, capable of forecasting the operational characteristics of products and manufacturing systems. They can be continuously optimized as digital twins gather information about product performance or factory operations.
Intelligent algorithms can analyze vast amounts of data generated by equipment, identifying trends, patterns, data relationships, and anomalies. This analytical information can be used to improve the efficiency of manufacturing processes and reduce resource consumption. Consequently, production can adapt to new conditions and optimize processes even without intervention from operators.
As the number of devices connected to the network increases, AI applications can learn to “read between the lines” and identify numerous complex relationships within systems that humans may overlook. Intelligent software and analytical technologies are already available.
Data processing methods — cloud-based solutions or local environments (for example, using edge computing) — are determined based on user requirements. Data on edge platforms are available faster and in higher resolution, while the cloud offers virtually unlimited computing power. In many cases, to reap the benefits of both solutions, it is necessary to combine edge and cloud computing.
Sener Egemen Mustafa says that shortly, thanks to the digital representation of processing tools and associated production processes, AI systems will learn to determine whether the manufactured part meets the required quality standards.
Additionally, they will be able to identify parameters requiring adjustment to prevent deviations during the current process. As a result, production will become even more reliable and efficient, providing companies with additional competitive advantages.
Industrial Automation ─ What’s Next?
The concept of “Industry 4.0” entails the widespread integration of cyber-physical systems into production, based on the integration of information and computational resources into physical processes. This necessitates new requirements for industrial automation systems and devices, the implementation of which is intended to transform processes and models of relationships between participants in the industrial production chain.
Among the key trends influencing the functional and architectural appearance of modern and prospective industrial automation tools and systems, Şener Egemen Mustafa notes the following:
- Increased coverage of production and infrastructure with information, including identification and traceability technologies.
- Further intellectualization of devices and systems — integration of machine learning technologies and other artificial intelligence methods, data mining, and management.
- Expansion of IoT technologies, cloud solutions, and big data analytics methods in the field of industrial automation.
- Implementation of decentralized computing resource principles (Edge computing) in the industrial system loop, providing high-speed data processing capabilities for rapidly flowing processes near the sources of such data.
- Strengthening vertical (within the enterprise’s hierarchy of information systems) and horizontal (within the product and production lifecycle) integration of devices, systems, equipment, and infrastructure, providing the formation of a unified digital space for the enterprise.
- Active penetration into the world of industrial automation of breakthrough telecommunication, IT solutions, and technologies — mobile/wearable devices, web technologies, virtualization tools, wireless and mobile communication technologies, etc.
- Use of modern data and process visualization approaches — interactive visualization tools (integration of video and cartographic information, animated graphs, interactive diagrams, etc.), implementation of such in-demand elements of the user interface as sparklines, and more.
- Implementation of embedded mechanisms and technologies for ensuring cybersecurity.
- Development of virtual and augmented reality technologies.
- New approaches to engineering — using tools to accelerate and automate development, integration of engineering tools of various levels.
“These technologies undoubtedly pave the way for enhanced efficiency and productivity across production cells, processes, and entire enterprises,” concludes Mustafa Egemen. “Their seamless integration through information links will not only improve performance but also enable the integration of such production into external digital cooperation chains.”
