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DIGITbrain Solution: Actors & Stages

The DIGITbrain Solution promotes a collaborative environment, enabling the manufacturing industry to adopt Digital Twins. By using diverse assets, manufacturers produce a digital counterpart of physical equipment, optimizing analysis, simulation, and performance. We'll delve into its facets using the context of a Bottle Filler (Example is italicized). 

Imagine a beverage assembly line where the "High-speed Bottle Filler" machine plays a crucial role in determining production capacity.

In the DIGITbrain landscape, experts collaborate, ensuring tasks are tackled by the most qualified, making digital twins more accessible across the industry.

1) IT Experts: They lay the technical foundation, crafting and publishing Microservices and Algorithms. For instance, in a beverage production scenario, they might develop a Microservice to monitor a bottle filler's temperature and set alerts for fluctuations.

In our scenario, an IT Expert might create a Microservice to monitor temperature and another Algorithm to alert if there are fluctuations beyond set limits.


2) Manufacturing Domain Experts: These experts weave IT into tangible manufacturing scenarios, constructing Models and MA Pairs. In our beverage context, they might simulate the "High-speed Bottle Filler", predicting potential disruptions. They bridge IT and manufacturing, leveraging IT elements to devise Models and MA Pairs.

Here, a Manufacturing Domain Expert could design a Model that simulates the flow of liquid, predicting potential clogs or spills.


3) Data Analysts: Linking the digital and real, they publish Equipment and its Data.

In the context of our beverage line, a Data Analyst would publish data on fill rates, bottle sizes, and temperature readings, ensuring the digital twin of the "High-speed Bottle Filler" machine accurately reflects its real-world counterpart.


4) Machine Operators: The end-users, they harness the digital twin's insights for real-time physical equipment optimization. Using data, they could fine-tune the "High-speed Bottle Filler" for maximum efficiency.

With the digital twin's data, a Machine Operator could tweak parameters on the "High-speed Bottle Filler", ensuring maximum productivity with minimum waste.


Eight Stages to create a Digital Twin Solution 

1) Create Microservice
IT experts craft Microservices as foundational building blocks, encapsulating standalone functions or tasks suitable for integration within the DIGITbrain Solution.

For the beverage line, an IT Expert might develop a Microservice to monitor the flow rate of liquid into the bottles.


2) Create Algorithm
Post Microservice creation, IT experts design Algorithms that process data, make computations, or assist in decision-making.

Building on the flow rate Microservice, an Algorithm is created to adjust the flow if the filling rate is too slow or too fast, ensuring optimum efficiency.


3) Create Model
Domain experts create Models that simulate specific processes or behaviors within the manufacturing environment.

An expert might design a Model of the "High-speed Bottle Filler" that not only tracks filling but also predicts wear and tear, allowing for predictive maintenance.


4) Create MA Pair
The fusion of a Model and Algorithm results in an MA Pair, combining representational and computational capabilities.

By pairing the Model with the flow adjustment Algorithm, we get a tool that both monitors and optimizes the process.


5) Describe Equipment
Data analysts register and make available specific Equipment (Machine), anchoring the digital twin to real-world machines.

A Data Analyst would detail the filler's specifications.


6) Equipment Data
The essence of the digital twin lies in its data. Data analysts source and publish real-time or historical data related to the Equipment.

In our scenario, a Data Analyst might upload past data of the filler's operational hours, downtimes, and efficiency metrics.


7) Create Process
By combining Data with the right MA Pair, data analysts craft a dynamic representation of the Equipment's behaviour.

Combining the data from the "High-speed Bottle Filler" with the MA Pair allows the creation of a Process that can simulate the filler's response under different conditions, such as changing input pressures or bottle sizes.


8) Execution of Process
The machine operator puts everything into action, using simulations, predictions, or optimizations based on the MA Pair and Equipment Data.

Operators can adapt the filler according to feedback from its digital twin.

Created by liza @ cloudsme 8 months ago (last activity 8 months ago) and viewed 509 times

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