The benefits digital twin has hailed upon industries since its origin are numerous. From reducing manufacturing costs, to designing prototypes without wasting resources or energy, the digital twin has been an important industry 4.0 technology. Over the years, however, the expectations of a digital twin have increased. It is no longer enough for industrial players to manipulate a digital representation of a physical object without influencing the physical object. Hence, this is why a different but related concept called the cognitive digital twin emerged.
A cognitive digital twin is a more advanced version of a digital twin that allows agile and robust opportunities to manipulate physical objects and have more control of big data. This technology was made possible by advancing Machine Learning (ML), Artificial Intelligence (AI) and cognitive science. As the term implies, the cognitive digital twin can hold certain cognitive functions, including having an excellent memory, as demonstrated in the speed at which data is encoded and retrieved, providing smart data representation and can carry out expert reasoning. This is increasingly being implemented in smart machine optimisation. This article will explain how cognitive digital twins can help optimise smart engine optimisation.
What Feature of Cognitive Digital Twin Help in Smart Machine Optimisation?
The core feature of cognitive digital twins is infusing optimisation techniques. This is encapsulated within the unique features of the cognitive digital twin. In an article titled Cognitive Digital Twin for Manufacturing Systems, the authors detail six ways cognitive digital twin differs from the traditional type. They include:
Perception
It is common knowledge that ML techniques are less effective in forming or learning representations of large and high dimensional data volumes. Perception, therefore, helps power cognitive digital twins with the right capacity. Understanding that cognitive science greatly influenced it, perception is a feature that belongs to the literature of cognitive psychology. According to a book by Huffman, Dowdell, and Sanderson, perception in psychology refers to the selection, organisation and interpretation of sensory data into a “usable mental representation of the world”. In the context of a cognitive digital twin, this refers to forming useful representations of data related to the physical twin and its physical environment for further processing. This is meant to help increase visibility in smart machine optimisation.
Attention
Humans pay attention for various reasons. The more attention that is put into something, the better the outcome. Attention, in simple terms, can be perceptual, whereby a person can interpret something through their senses. It can also be nonperceptual or controlled. A cognitive digital twin can hold high levels of attention, thereby focusing on a specific task or goal by its intent. Alternatively, various environmental signals or circumstances can also influence the cognitive digital twins to act independently. This means that the cognitive digital twin will be able to monitor or select a task to focus on by itself, thereby gaining more autonomy in the manufacturing process. As a result, it can simplify and improve the digital twins’ capacity to make smart decisions.
Memory
Memory is a multifaceted process. It does not simply refer to remembering certain aspects. It instead reflects various abilities to reflect the type of memory that resembles humans. For instance, it holds working memory, whereby the cognitive digital twins can hold information while working on it. It also can recollect and remember past episodes of the physical twin’s life, a process called episodic memory. Additionally, cognitive digital twins have semantic memory which allows it to make sense of the knowledge of the environment and its interaction with the physical twin. Cognitive digital twins can encode information through these different forms of memory by self-learning, perceiving and comparing it to past knowledge, storing information over time, and accessing it when needed.
Reasoning
One aspect unique to humans is their reasoning. Humans can take into account a wide range of factors to make meaningful decisions. Integrating this capability into a cognitive digital twin makes reasoning a significant reason one can easily enable smart machine optimisation. It draws a viable conclusion that is consistent with the starting point, working together with the information or conclusions drawn from the other capabilities of cognitive digital twins, including perception, attention and memory. The different ways of reaching a decision are classified as a deduction, induction and probabilistic reasoning. Ensuring reasoning is intact in smart machine optimisation is important because it helps establish transparency in a factory. This is an important aspect in ensuring that decisions are made intelligently.
Problem-solving
All the features of a cognitive digital twin work together. Hence, problem-solving is an aspect that infuses the capabilities mentioned above to reach a feasible solution to the problem at hand. It is an essential aspect of making decisions and enhancing autonomy to help in smart machine optimisation. Here, different courses of action will be considered and strictly evaluated to present the most appropriate solution. Newell and Simon explain in their article that humans approach problem-solving by first thinking about the initial stage of the problem. Next, they consider the objective of solving the problem and, in its intervening stages, look at the pros and cons of the different strategies formed in the process. It is this complex task that the cognitive digital twin represents.
Learning
Learning in a cognitive digital twin refers to the process of transforming the experience of the physical twin into reusable knowledge. This paves the way for new experiences to be formed. In other words, this represents the human’s ability to adapt, gain new information and relearn. This is an essential feature of making intelligent decisions and enacting smart engine optimisation. As a result, cognitive digital twins are very independent, not requiring the interruption of humans to make changes to the system manually. This way, cognitive digital twins respond to the physical system of the digital replica with much greater accuracy than a traditional digital twin model by only referring to the most relevant information. In this sense, a cognitive digital twin consistently codifies and maintains newly produced knowledge.
Cerexio: Striving towards Digital Twin Excellence
Cerexio is a reputed digital vendor offering a range of smart engine optimisation solutions. The Cerexio Digital Twin System is powered by the latest battle-tested simulation technology enhancing its ability to optimise all processes in your factory. It can elevate the interoperability of all assets and software connected to your company, provide a detailed visualised dashboard, offer smarter forms of decision-making, can facilitate neo and retro-filling industries and applications, present innovative business model engineering capabilities, and create real-time and intelligent data-driven decisions and more. It has enormous potential to develop further its solution to represent a cognitive digital twin model that can enhance autonomy, transparency and seamless optimisation in your advanced manufacturing processors. As a technology vendor that houses a range of industry 4.0 technologies, Cerexio is known for holding digital twin excellence.
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Cognitive Digital Twins to Become the Key Enabler of the Visions of Industry 4.0
Digital twins, for a long time, was the key enabler of visions in industry 4.0 technology. However, with the advancement of cognitive science, AI and ML capabilities, the development of cognitive digital twins is expected to become the ultimate key enabler of visions in advanced technologies. It will be a leading solution for enterprises to creatively and intelligently exploit implicit knowledge from manufacturing systems and help them to make intelligent decisions and enhance smart machine optimisation efforts. As cognitive digital twins hold features of perception, memory, attention, reasoning, problem-solving and learning, the success of your business operations is guaranteed.
This article is prepared by Cerexio, a leading technology vendor that offers specialised solutions in the Advanced Manufacturing Technology Sector. The company is headquartered in Singapore and has offices even in Australia. Cerexio consists of a team of experts that have years of experience and holds detailed knowledge on a range of subject matters centric to the latest technologies offered in manufacturing and warehouse operations, as well as in predictive maintenance, digital twin, PLC & instrumentation setup, enterprise integrator, data analytics and total investment system.
