Editor's Choice

Digital twins, science-fiction or reality?

June 2018 Editor's Choice

Industrie 4.0 introduces the somewhat abstract concept of a ‘digital twin’. But is this really new, does it actually exist anywhere in practice, and if so, what steps should be followed to build one?

A process control engineer who is already familiar with CAD, scada, process simulations, manufacturing systems and business (ERP) systems, may find the concept of a digital twin puzzling – after all integration of systems and process automation has been part of the goal of modern manufacturing for some time. So, what is new?

What is a digital twin?

A digital twin is a digital representation of a physical object, plant or process throughout its life-cycle. This digital representation can include original design information, physical attributes and context and usage information, which can in turn be used to model and predict performance.

A digital twin is not a product you can buy. Implementing a digital twin is going to be a journey during which you steadily implement platforms, capabilities, processes and human/machine interfaces. Beware, all roads don’t necessarily lead to Rome, so understanding your business strategy and how these new digital technologies will support this is important as to why a digital twin is needed, and what exactly has to be done.

Design, manufacturing and maintenance data

A digital twin is easiest to understand when considering a physical object, for example a car, or an engine, or an electronic device. The digital twin is a digital representation of this device; the data is initially developed and optimised during design, tracked during manufacturing and then augmented by actual usage data to improve use/maintenance of the object by customers:

• Design data relating to the object is created and optimised virtually using computer aided design and modelling technologies.

• Manufacturing data records the detailed production parameters, for example raw materials, third-party components used in assembly, quality, process conditions and so on.

• Use/maintenance data records how the object is actually used by customers in the field, when/how it is maintained, and so on.

Modern automotive manufacturing already has several of the above elements in place and is a leader in this regard. In other industries however, the digital twin might not be as straightforward.

A digital twin is not restricted to physical objects; it might be implemented for an entire manufacturing system, including physical plant and equipment, human decisions/activities, business processes, customer data, supply chain data, events, environmental information etc. The common thread is the connection, collection, organising, analysis, visualisation and interaction with vast amounts of data.

At the heart of the digital twin is a model that represents the attributes and operation of the system or object. But a digital twin is more than simulation software – a digital twin will usually include artificial intelligence that allows for self-learning. The output of the digital twin will be a rich interactive human machine interface, which uses for example 3D augmented /virtual reality to visualise and simulate performance.

Digital twins support the full product life-cycle in several ways:

• During design, digital twins will improve collaboration and allow product development teams to work virtually across multiple locations. Computer aided design and collaborative tools have existed for some time now. A digital twin builds on this but takes the concept further to support adaptive flexible manufacturing to quickly adapt to environmental conditions and individual customer requirements.

• During manufacturing detailed production information and small variants in the manufactured article will be measured and stored in the digital twin. For example in electronics manufacturing individual components used in assembly are often sourced from competing suppliers and will vary between batches. Tracing each component of the assembled product through design, manufacturing and ultimately during use/maintenance will allow for rich insights into how using different component suppliers affects the product performance in the hands of the customer.

• During use/maintenance, field data will likely be collected and analysed using IoT sensors. True predictive maintenance then becomes possible that will in turn enable more targeted and responsive service to customers.

Implementing a digital twin proof of concept

Implementing a digital twin can be confusing and overwhelming. I suggest that you consider starting small and do a proof of concept (POC). For example:

1. Research the opportunity in terms of your business strategy, do some planning, secure budget and build awareness and support for a POC.

2. Implement remote monitoring capabilities (this will probably need you to improve parts of your systems architecture, implement connectivity and data standards such as OPC-UA and ISO 10303-239, take on new IoT devices and build new capabilities in your IT and manufacturing systems teams).

3. Implement predictive analytics tools that will consume this remote data to self-learn and predict performance (this will likely require new capabilities in data science, modelling, artificial intelligence and visualisation).

4. Connect the result of the above to field service operations (this might require fundamental reorganisation of the established business processes in this area).

5. Close the loop by connecting the data and models back into new product development, design and engineering processes.

As you run this POC and as relevant technologies continue to mature in the market, you might also systematically introduce new human/machine interfaces and data visualisation tools, including 2D/3D visualisation, augmented reality and advanced human machine interfaces (natural language processing and natural user interfaces). Remember, the digital twin is not pure automation, it is intended to augment, not replace, human decision making.

Gavin Halse

Gavin Halse is a chemical process engineer who has been involved in the manufacturing sector since mid-1980. He founded a software business in 1999 which grew to develop specialised applications for mining, energy and process manufacturing in several countries.

For more information contact Gavin Halse, Absolute Perspectives, +27 (0)83 274 7180, gavin@gavinhalse.com, www.absoluteperspectives.com


Share this article:
Share via emailShare via LinkedInPrint this page

Further reading:

Loop Signatures 1: Introduction to the Loop Problem Signatures series
May 2020, Michael Brown Control Engineering , Editor's Choice
Over the years I have had many requests to write a book giving more detailed explanations of some of the problems I have encountered in my work on practical loop optimisation. I am by nature and inclination ...

An example of control and automation using IIoT, edge computing and the cloud
July 2020, Absolute Perspectives , IT in Manufacturing
   The challenges of working in remote locations I am involved in a number of projects investigating the feasibility of coal-bed-methane (CBM) in southern Africa. During one of these projects, I came ...

Loop Signatures 2: The two classes of processes.
July 2020, Michael Brown Control Engineering , Editor's Choice
This article will discuss the two classes of processes called self-regulating and integrating (or ramping) processes. This subject is absolutely vital to regulatory control, but strangely is seldom taught ...

From the editor's desk: The virtual business assistant
May 2020, Technews Publishing (SA Instrumentation & Control) , Editor's Choice
Have you ever wished someone would automate the daily grind of routine tasks and set you free to focus on the more engaging aspects of your job?

From the editor's desk: The virtual business assistant
June 2020, Technews Publishing (SA Instrumentation & Control) , Editor's Choice
Enter robotic process automation (RPA), a disruptive workplace technology that uses software “robots” to mimic many of the repetitive interactions human beings have with their computers. It performs such ...

Case History 172: Interesting controls in a copper extraction plant.
June 2020, Michael Brown Control Engineering , Editor's Choice
In my 30 years devoted to optimising controls in industrial process plants in many countries, I thought that I had seen all the possible process dynamics that one would encounter. Imagine my surprise ...

The emergence of a new future in the energy sector
April 2020 , Editor's Choice
Adaptively complex and persistent challenges in Africa are driving the need for a new future in the energy sector. Lack of access to energy, (more than 600 million people in Africa with no access to energy) ...

Finding the common thread in process industries
March 2020, Absolute Perspectives , IT in Manufacturing
Initially focused on financial management and MRP (materials resource planning), ERP has evolved to embrace the whole value chain, from vendor management, manufacturing, supply chain/logistics, customer relationship management and more.

Case History 171: Instability in a metallurgical plant
March 2020, Michael Brown Control Engineering , Editor's Choice
I have written several articles about the unique problems I have encountered, specifically in the mining processing industry. This article is about some experiences in a mining operation where recently ...

AI in manufacturing – revolutionary opportunity or well-trodden path?
December 2019, Absolute Perspectives , IT in Manufacturing
Artificial Intelligence (AI) has become a catchphrase used by marketers that attributes the characteristics of human intelligence to a computer system.