Innovation and emerging technologies

These technologies contribute to the concept of Operator Support.

While Virtual Reality (VR) immerses us in a virtual 3D world, Augmented Reality (AR) supplements the physical world around you.

• AR applications for the maintenance of machines or production lines: information is provided to the maintenance technician on a mobile device (tablet or glasses) to help them perform their task.
• AR applications for informing operators while they work: the operator receives information via augmented reality glasses about the parts they need to pick up to assemble a product.
• VR applications for customer experience: using a virtual reality headset, the customer can get an idea of what the product and its installation will look like.
• VR applications for employee training without having to go on site due to safety requirements or risks: the trainee will be trained in a virtual world, using virtual reality goggles, on the product they have to assemble or the procedures they have to follow, as part of safety training. Thus the person does not come into direct contact with the real product or environment and experiences a virtual or digital representation of the product or environment.
• VR and digital twin applications: a digital twin can be used in a virtual reality environment and the digital representation of the product or production facility can be seen through VR goggles

The automation of the production process is not a revolution, it has been present since the first production processes. However, it is constantly evolving and incorporating new technologies. In Industry 4.0, for example, automation has fully integrated digitalisation. Production processes, whether in the assembly industry, the food industry or the chemical industry, are increasingly automated. Humans are freed from these tasks as much as possible and replaced by machines and installations. Not with the aim of no longer having employees, but rather to perform non-value-added tasks and enable employees to focus on value-adding activities or non-automatable tasks. Robots provide the link between the pure automation offered by production machines and the operators’ desires.

The transport of goods is increasingly streamlined in internal logistics process. In logistics environments, robots therefore also play an important role (emergence of AMR or Autonomous Mobile Robots). New technologies such as drones will also be used more and more in this area.

Conclusion: automation will continue to evolve and expand into areas where we do not necessarily expect it. Digitalisation will accelerate and increase its efficiency and flexibility.

Assistant and service drones are playing an increasingly important role in the manufacturing industry. On the one hand, they are used in production and integrated into processes. On the other hand, they and their components are also produced by industry.

By industry…

In Belgium, a number of leading companies play a major role in their manufacture, integration and development. More and more hardware and software companies are dedicated to improving standard drones and their missions. Last but not least, the ever-increasing number of drones in the airspace also needs to be organised, which requires the development of Unmanned Traffic Management (UTM) solutions.

… and for industry

Companies are already using drones, particularly for surveillance and the initial stages of stock management. Drones also allow them, for example, to measure air quality and other indicators using suitable sensors. In the future, the functions of drones and robots will be combined to a greater extent in 'dronebots', which will also use artificial intelligence (AI). These flying robots will be integrated into production environments, offering new applications such as moving objects with robotic arms. While this may still seem a long way off to some, some concrete pilot projects are already emerging.

Drones are also playing a role in Smart Automated Transport and Smart People Transport. Advantages of using drones: Respect for the environment - Accessibility - Quantity and quality of data - Accuracy of data produced

 

Cyber-physical systems (CPS) are objects with embedded software and computing power, like the navigation system in your car or the smart watch on your wrist, which not only tells the time but also analyses your activity.

Industry 4.0 is also called the Industrial IoT. Within a production system, however, the 'objects' are more complex and are referred to as cyber-physical production systems (CPPS). CPPSs are intelligent machines with not only computing power but also integrated sensors and software that enable them to diagnose themselves and take decisions on the basis of the measured state. An intelligent power, in other words, that knows its own status, history, maintenance plan, capacity and the effect of any adjustments and settings.

The objective of industrial communication is to link all segments of a company into a single networked system that encompasses the entire value chain, from management to production. The use of powerful end-to-end data networks increases the availability, flexibility and efficiency of machines and companies. Thanks to highly-developed industrial communication, sensors and field devices can be flexibly integrated into existing systems while offering seamless communication and increased productivity. This is also known as the Industrial IoT (Internet of Things).

More information

AI has become the generic term for applications that perform complex tasks that previously required human intervention. The term is often used interchangeably with related underlying terms, such as machine learning and deep learning. However, there are differences. Machine learning, for example, focuses on building systems that can learn or improve their performance based on the data that feeds them.

Deep learning, on the other hand, is a subgroup of machine learning. It focuses on learning without explicit programming, creating more complex models designed to mimic the way humans learn new information.

The ways in which AI can help improve business processes, products and services are extremely broad.

A digital twin is a digital replica of products, processes and systems. The digital representation reproduces the elements and dynamics with which a machine, an Internet of Things device, etc. operates and evolves throughout its life cycle. Digital twins integrate AI, machine learning and software analysis with data to create living digital simulation models that adapt and update as their physical counterparts change. A digital twin learns and constantly updates itself from multiple sources to represent its state, working conditions or positions in near real time. The digital twin learns:

• By itself using sensor data that informs it of various aspects of its operational conditions;
• From human experts, such as engineers, with relevant and in-depth knowledge of the industrial domain;
• From other similar machines;
• From other fleets of similar machines;
• From any wider systems and environments of which it is a part.

The digital twin also incorporates historical data on the use of the machine in the past and factors this into its digital model. In various industries, twins are used to optimise the operation and maintenance of hardware assets, systems and manufacturing processes. They are a formative technology for the Industrial Internet of Things, where physical objects can live and interact virtually with other machines and humans.

Simulation is the imitation of the functioning of a real-world process or system. Simulation first requires the development of a model, which represents the essential characteristics, functions and behaviours of the selected physical or abstract system or process. The model represents the system itself, while the simulation reproduces the functioning of the system over time. Simulation is used in many contexts, such as technology simulation for performance optimisation, safety engineering, testing, training, education and video games. Often, computer experiments are used to study simulation models. Simulation can be used to show the real end effects of alternative approaches and conditions. It is also useful when the real system cannot be implemented because it is not accessible or would be dangerous or unacceptable to run, or because it is in the design phase but not yet built, or simply does not exist. Key elements of simulation include the acquisition of information from valid sources, the appropriate selection of essential characteristics and behaviours, the use of approximations and simplifying assumptions within the simulation, and the fidelity and validity of the simulation results.

This refers to all measures taken to protect information assets/information contained in assets, concerning people, processes and technologies, against digital crime. An asset is everything that is valuable to a company, such as the network, infrastructure (servers/workstation/laptop), applications, data. A policy of classification (confidentiality, integrity, availability) and categorisation of assets is essential in order to allow for appropriate security measures. Cybersecurity also means the degree of protection in terms of access, integrity and availability of data (CIA).

Various measures have been developed to limit cyber threats:

• Hardware and software measures
• Physical measures: in the industrial world, for example, physical and logical access to the production line should be well secured, for example by disabling access to USB ports, as they constitute a vulnerable input.
• User measures: as the human factor is the weakest link in cybersecurity, an awareness programme must be conducted. Every employee and supplier has a role to play.
• Measures at process level

It is also important that consumers and organisations know how vulnerable they are and what they can do about it. A security audit will help determine the level of maturity and resilience. Critical processes should also be identified and a business impact analysis (BIA) report maintained. These are just a few of the hundreds of controls that need to be in place for good cyber security.

With the rise of technologies such as the Internet of Things and artificial intelligence, the amount of data available to companies has literally exploded in recent years, and the computing power required to process it has also increased.

High-Performance Computing (HPC) makes it possible to process and analyse large amounts of data and perform complex calculations at high speed by combining the power of several thousand processors. This makes it possible to solve extremely complex and demanding problems and to carry out digital simulations.

HPC is used for simulation and modelling in a large number of areas such as the automotive and aerospace sector for product design and manufacturing, the development of autonomous driving models, in the meteorological field but also for the analysis of seismic waves and their effects on structures, in the field of precision medicine, in the gaming and film industry, in the banking sector for the assessment of financial risks, in the detection of fraud, etc.

Blockchain is a distributed, transparent and secure information storage and transmission system. A transmission technology that operates without a central control body, which brings a dimension of trust to interactions between individuals. This technology can have a significant impact on existing business models (eliminating intermediaries and the risk of fraud) and has the potential to transform many economic and social sectors.

As factories around the world become more interconnected, blockchain technology is increasingly being considered. The factory of the future spans an interconnection of machines, parts, products and value chain stakeholders, including machine suppliers and logistics companies. More than ever, manufacturers face the challenge of securely sharing data within the company and outside the factory walls.

Blockchain technology can integrate with other technologies (AI, IoT, 5G, etc.) to improve transparency and trust at all stages of the manufacturing value chain, from raw material sourcing to inventory management and finished product delivery.

The key issues that it could help to address are

• tracking the supply chain for greater transparency
• sourcing of materials and detection of counterfeits
• engineering design of complex and long-life products
• identity management
• asset tracking
• quality assurance
• regulatory compliance.

Blockchain can help streamline operations, gain visibility into supply and track assets with unprecedented accuracy. Blockchain has the potential to revolutionise the way manufacturers design, develop, manufacture and scale their products.

Additive manufacturing (AM) technologies are technologies for making physical objects from computer-aided design (CAD) data. Parts are created by successively adding layers of material in liquid, sheet, molten thermoplastic or powder form. Parts are created by layering material in liquid or sheet form, molten thermoplastic material or powder, layer by layer, to create a 3D object. The range of materials increases almost daily, with polymers and metals having the largest supply. The base material is supplied in powder, liquid or solid form to the AM machine to eventually produce (or 3D print) an object in solid form. Additive manufacturing is one of the pillars of Industry 4.0 as it allows for increased process automation (less human intervention), more flexible production, reduced environmental impact of the parts produced, etc.

Some key factors in the use of these technologies:

• Time and cost savings in the design phase
• Reduced consumption of raw materials
• Weight reduction
• Increased functionality
• Complex parts without increasing cost
• Customisation
• Reduced time to market
• Elimination of investment in manufacturing tools
• Optimisation of the supply chain (production on demand)