Flexibility and Adaptability: Core Skills in Advanced Manufacturing

In nature, flexibility and adaptability are essential to survive. Flora and fauna around the world are full of examples of these life skills. Quick fact about reed: the stem of this plant is made of fiber, strong enough to resist the roughest meteorological conditions. Even when it is swept by a strong wind, the reed bends but never breaks. In the same manner, many animals naturally develop undercoats in the winter to go through the lowest temperatures.

Just like in the wilderness, flexibility and adaptability allow us to keep growing in our social and job environments.

And yet, unlike animals and plants, humans living in society usually need to learn and practice these skills. This is the reason why the DTAM Training Material on Transversal skills emphasizes the importance of becoming more flexible and better at adapting to multiple changes. Thanks to our training material, workers, educators, and VET instructors will have a tool to acquire more knowledge about these skills that everyone naturally possesses but that are not so obvious to apply, especially in the work environment.

Why is being flexible and able to adapt so essential, especially in the 21st century? In its Manifesto for Adult Learning in the 21st Century: The Power and Joy of Learning, the European Association for the Education of Adults (EAEA) outlines “digitalisation, internationalisation, service orientation, [and] flexibilisation” as the biggest mega-trends of today’s labor market. The EAEA adds, “these trends increase in parallel the pace at which employees have to adapt to the constant change and to gain new competencies”. As companies must become more flexible and adapt to constant change, so must their employees.

The pandemic, climate change, economic gaps, or the fast-paced development of cutting-edge technologies are the central global challenges faced by our society. Flexibility is about looking at these challenges as new opportunities, rather than obstacles. Adaptability is about finding new solutions to build the world of tomorrow. Both these skills are not only essential to face adversity, but also to show resilience i.e. the ability to recover after an obstacle and to move forward.

Whether it is about the learners’ daily lives—having to work without less human contacts—or companies’ social impact—building a more sustainable world—flexibility and adaptability are needed individual and social skills to be (re)learned. Therefore, the DTAM team is thrilled to share knowledge with its target groups about these two transversal skills, among many others.

Stay in touch on the DTAM social media pages and our website to be informed about all the rest of the hard and soft skills to be included in our Training Material.

References:

[1] European Association for the Education of Adults, Manifesto for Adult Learning in the 21st Century: The Power and Joy of Learning (2019)

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INCOBOTICS – Ready for Industry 5.0

The use of collaborative robotics is growing and will continue to grow. Future electronics and robotics graduates will have to work alongside big data and artificial intelligence experts to program, monitor, and analyze data coming in and out of cobots and their integrated computer vision (AV) systems. While we’ve been telling you about DTAM project for the past year now, there was another project steadily making progress towards addressing these challenges: the INCOBOTICS – Ready for Industry 5.0. The project was run by three of our existing partners i.e. Politeknika Ikastegia Txorierri (Spain), Apro Formazione (Italy), IDEC (Greece), and another partner from France – CFAI Adamic. With project INCOBOTICS, co-funded by the Erasmus+ program of the EU, the four partners chose to invest in the future of education and lay the groundwork for this to happen via the following results:

  1. A Training Module suitable for:
  • EQF level 5+ educationalists (HE and high level VET);
  • Industrial and technical students (future workers in Industry);
  • Industrial workers who require upskilling in programming for CO-BOTS via courses for the employed or unemployed workers.

The Module comprises of three ready to use, tested and validated units of learning in collaborative robotics as follows:

  • Introduction to Collaborative Robotics in Industry: Industry 5.0
  • Programming COBOTS
  • Artificial Vision Systems

After attending the INCOBOTICS Module, students will be able to understand Industry 5.0, comprehend the COBOTS major brands available on the market, understand the main functionalities of a COBOT, programme a COBOT, understand the capacities and range of Artificial Vision and learn how to programme Artificial Vision.

2. A Best Practices Guide for Teachers and Trainers, which offers pedagogical support and frames the INCOBOTICS Module in the Challenge Based Learning (CBL) Methodology. The Guide includes:

  • Guidelines for implementing the innovative Challenge Based Learning – CBL- methodology upon the INCOBOTICS Module and Units of Learning;
  • ECT accreditation guide for the INCOBOTICS units/module. Principles can be extracted to support accreditation of other units/modules, which teachers design in the course of their work;
  • Four programming challenges created transnationally using different COBOTs.

The Guide is available in four languages: English, Spanish, Italian, French.

In these pictures: Stefano Antona (Apro Formazione) presents the results of the project during the final event held at their headquarters in Alba, Italy.

On Thursday 30 September 2021, the project ended and the developed materials were finally made available for free on the official project training platform here.

Discover more about the Incobotics project at www.incobotics.eu/

IoT as part of the DTAM Training Curriculum

In recent years, practical steps into Industry 4.0 have shown a plethora of IoT platforms being utilized throughout the producing industries. The result of this heterogeneity on the platform level is an unmanageable variety in the solution requirements on the edge. Most actors in this field have realized that better interoperability of solutions is mandatory if Industry 4.0 is expected to have a continuing impact.

Within the DTAM project, students will be trained to become the engineers of the future by exploiting the knowledge about IoT. ICT technicians will learn to understand all digital technology needed to install, configure and monitor cyber physical intelligence and tools in AM environments. OT technicians on the other hand will learn the ability to understand and manage digitalization tools and the most advanced AM technologies for secure deployment and maintenance.

In the DTAM IoT lab, devices are available for students so that they can learn in practice. With our European network for sharing all sensor data, students will experience what it is like to work in an (international) AM environment and to connect the equipment. They will learn what it’s like to use different connection types like GSM, LoRa WAN, Ethernet, and Bluetooth. With their knowledge of sensors and data connections, the students will be able to work in the Industry 4.0 environment and further shape the digital transition in advanced manufacturing.

The DTAM partnership is now close to finalising the training curriculum. We can’t wait to share some more good news with you very soon, so don’t lose your patience just yet!

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Digitizing business environments: a case study by AFM

The digitization of workshops is one of the challenges that industrial companies are tackling. The fourth industrial revolution, also called Industry 4.0, is at its peak. Virtually all companies have in their strategy, lines related to the digitization of their value chain and the integration of data in it. Advanced digitization, the industrial internet and smart technologies such as the internet of things (IoT) are shaping production systems that have little to do with the industry of 5 or 10 years ago. The factories of the future are going to be smart, geared towards efficiency, sustainability, flexibility, and competitiveness, in a connected environment where people will be a central element.

However, these developments in Advanced Manufacturing are being limited (in their application) to young machines, manufactured less than 10-15 years ago. Older machines, due to their connectivity and sensorization limitations, are being left out of these developments. In order to find a solution to this problem, AFM CLUSTER has participated in collaboration with the companies Fagor Automation and Savvy Data Systems, in the IIoT4ALL project. This project has been funded by the Spanish Ministry of Industry, Commerce, and Tourism through the General Secretariat of Industry of Small and Medium Enterprises through the aid program to support innovative business groups.

This project has aimed to deploy and validate a solution to access a management system of a plant that integrates devices from previous generations that are not compatible with the new communication standards. In other words, the integration of older machines with more modern ones in a single layer at plant level which brings together all the systems present and allows comprehensive data management.

This layer is of vital importance because many of the older systems, even those with connectivity, do not use the latest communication standards and therefore cannot operate in the same way as new systems in a digitized plant. In addition, the idea of using this layer is to avoid having to modify the machine’s original software, since the aim is to adapt the old and the new communication systems.

Within the activity carried out by companies to digitize their plants, there is a need to include existing machines, that are in full production capacity, in their new production control and planning systems. The companies have to provide this machines with capacities that new technologies allow in terms of programming, simulation and automatic generation of programs that not only increase productivity but also improve the quality of all processes at the plant level. This has already been addressed at the hardware/software level in the machine in previous projects (such as the RETRO-CONNECT project), but a plant level layer was missing that would bring together all the present systems and that would allow comprehensive data management, taking into account both generation devices, the old generation as well as the latest one.

This is an example of a project carried out for the digitization of the workshops. The new scenario will mean an increasing need for adequately trained OT technicians with digital competence.

Projects like DTAM will help to develop digital competencies for the current and future workers. DTAM Training Course will consist of approximately 25 training units on digital and transversal skills relevant for IT and OT technicians in AM environments that contribute to the major areas of Industry 4.0 and the foreseen sections of Big Data, Machine Learning, Sensors, and Cybersecurity.

We will have more updates for you in the coming months.

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IoT Technologies

According to several studies, the Internet of Things (IoT) is being considered as the arrival of the new disruption in the digital realm. It was in 1999 when Kevin Ashton, a professor at MIT (Massachusetts Institute of Technology) at the time, used the expression Internet of Things in a conference with an executive team of Procter & Gamble.

In summary, it can be described as an interconnection of any product with any other around it. Its significance is brutal and, according to a report by McKinsey Global Institute (Fredrik Dahlqvist,  2019), IoT has increased from 13 percent in 2014 to about 25 percent today, making it a key technology for the advance of our industry, territory and society.

In terms of the number of embedded devices, taking into account that every human being is surrounded by at least about 1,000 to 5,000 objects, it is not unreasonable to expect that the Internet of Things could grow to over 43 million in 2023 according to Mckinsey, although there are forecasts, indicating that this deployment rate will be much higher.

In terms of market impact, the IoT market is expected to reach an economic impact of between $4 trillion and $11 trillion by 2025. In terms of features, functionalities, capabilities, and cost of the Internet of Things, it is relevant to say that IoT platforms are the superbase for interconnecting devices and generating an ecosystem of their own (Ashton, K. 2011).

Companies active in any kind of sector are deploying IoT devices in order to capture data and create new value revenue streams offering new products and services to end-users. This technology will help to digitalize our world creating new business models for all kinds of businesses, from the big corporations to the industrial and traditional SMEs.

Here indicated some of the relevant platforms of IoT existing currently:

In this sense, “A key milestone of the DTAM project is the training curriculum that will focus on five key areas, one of which will be the Internet of Things”. In addition, to facilitate the learning and use of this technology in the framework of the DTAM project, a virtual IoT laboratory is being developed, and for this purpose, it is necessary to design the global map of the integration of devices-systems-services in the value chain. Have a look at the image below, provided by our partners at GAIA, illustrating this integration process:

IoT integrated vision. Global map of the integration. Source: GAIA

Interested in learning more about IoT and Big data? We will have some more exciting news for you in the coming months, so stay tuned!

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Segment your network and protect the core of your business

It’s not a secret that as the number of connected devices in factories grow, the companies are exposed to greater risks. And if this were not enough, there devices usually have different operating sustems, some already outdated and without support. What can you do to prevent a cyber attack from stopping production?

A containment plan is very important for companies that want to mitigate the risks of an attack on their machines. In order to do this, segmenting and organisizing the network is vital. To accomplish this task our partners at Saranet follow regulations such as IEC62443. It is also crucial to have a backup restoration service that speeds up the recovery of the factory in the case of a hypothetical cyber attack.

Here are some of the benefits of having a containment plan:

  • Enables dynamic segmentation
  • Prevents an IP threat from spreading through the comapny’s networks
  • Avoids uncontrolled access
  • Enables data traffic analysis
  • Improves the management of the network

The organization of the connected devices in the factory must include the separation of these in “islands” that do not communicate with each other, but with a central device. It is also key to install transition equipment between the IT and OT networks in the DMZ area, as well as installing firewalls.

How can you mitigate the risks of machine access?

Although it is still common to access the devices locally, usually using a display integrated in the machines itself, it is increasingly common to do so remotely. In both cases it’s very important to track these connections because they access the core of the business. In the first case, it is also important to to have a cybersecurity-aware staff. People can be the weakest link the chain!

When a company accepts a remote action request, the uses accesses the corporate network via the perimeter firewall, so the right to access is guaranteed through a protected communication.

Allowing a direct connection between the corporate network and the industrial netowrk or between them and the internet, is against any good practice.

The following actions should be added to the network segmentation:

  • Strong authentication strategies
  • Secure password policy
  • Proper user namangement and permissions

The DTAM project will create a modern training curriculum addressing advanced manufacturing topics like Cybersecurity, Big data and Transversal skills. Stay tuned to learn more!

Learn more about Saranet by visiting their official website at www.sarenet.es.

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The role of digitalization in business development during the COVID-19 pandemic

According to a study conducted by Euler Hermes earlier this year, countries with higher levels of digitalization have proven to be more resilient to the economic shock caused by COVID-19. Not surprisingly, the United States, Germany and Denmark are in the top three of the Enabling Digitalization Index (EDI) for 2020.

Euler Hermes’ digitalization index (EDI) measures the ability and flexibility of countries to stimulate the growth of digital companies and the ability of traditional businesses to recognize digitalization. The study involves 115 countries and the results are based on five indicators: regulatory requirements, knowledge, connectivity, infrastructure and size.

For 2020, the United States is the best in the knowledge ecosystem, competitive market size and favorable regulatory requirements. Germany is a leader in terms of knowledge and commercial infrastructure, but at the same time the quality of connectivity has fallen, following what has been happening around the world. It is interesting what is happening with China – the country is experiencing growth in all indicators, but faces problems in improving the skills of the population in the digital environment.

The main conclusion of the study is that digitalization can alleviate the shock caused by COVID-19. From an economic point of view, countries whose environment is more favorable for business digitalization (good connectivity, market size, regulations, logistics and knowledge) have managed to successfully respond to the crisis and have increased digitalization in many sectors.

One of the key outcomes of the DTAM project is the creation of the “DTAM Training Course” consisting of training units on digital and transversal skills relevant for IT and OT technicians in AM environments that will enhance the ability of industries to deal with the digitalization challenges caused by the COVID-19 crisis.

The role of Internet of Things in Advanced Manufacturing

Internet of Things (IoT) paves the way for savvy manufacturers to redefine the manufacturing process and how products are designed. Advanced manufacturing allows companies to deliver more value to customers resulting in a much stronger partnership. IoT together with Artificial Intelligence and Big Data are among the key enabling technologies of Advanced Manufacturing.

The adoption of the IoT technology in the advanced manufacturing environment, also called Industrial IoT, has many benefits: cost reduction, shorter time-to-market, mass customization and improved safety. Below are some indicative Industrial IoT applications:

  • Predictive maintenance. By connecting IoT-driven devices that are equipped with different types of sensors, to other devices, cloud or legacy systems, technicians and engineers can obtain essential maintenance data allowing to estimate the current condition of machineries, determine warning signs, transmit alerts and activate corresponding repair processes.
  • Remote Production Control. Thanks to the IoT technology, data collected from various field devices like switches, valves, and other indication elements is transmitted to the industrial automation system that ensures an overall control of machinery amid production process.
  • Asset tracking. The use of IoT technology in combination with native web and mobile apps makes it possible to obtain real-time asset information and make reasonable decisions.
  • Logistics management. IoT can reveal supply chain inefficiencies by eliminating blind spots from logistics processes.

Maximizing the impact of Industrial IoT requires innovation through strategic IT/OT partnerships. For instance, just recently, Siemens, IBM, and Red Hat announced a collaboration that would allow Industrial IoT users to use a hybrid cloud solution to maximize their efforts.

The DTAM consortium brings together education and training providers, advanced manufacturing industry representatives, as well as other key actors of the advanced manufacturing ecosystem to co-design, co-develop and test a novel curriculum and IoT labs for the upskilling/reskilling of advanced manufacturing technicians.

We are almost finalizing the first version of the DTAM curriculum and we are going through the first steps towards defining the specifications of the IoT labs which will be used during the pilots to promote problem-solving and experimental learning.

Stay tuned to learn more!

Sources:

[1] https://www.byteant.com/blog/5-best-use-cases-of-iot-in-manufacturing/

[2] https://www.forbes.com/sites/danielnewman/2021/04/19/how-tech-partnerships-are-driving-the-expansion-of-the-industrial-iot/?sh=3557b0c41c8e

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What is Machine Learning & how is it used in Advanced Manufacturing?

Artificial Intelligence (AI) provides several applications for systems to work in an automated environment and Machine Learning (ML) is one of those applications. ML provides the ability for a computer system to learn, adapt and improve from previous accessed data and procedures, without the need to be specially programmed. Firstly, the learning begins with the data collection and observation while using the data to find patterns and improve the decisions of the system in the future. The goal of the system is to learn automatically and readapt its procedures, without the need of human intervention or supervision. Note must be made that the difference in context for the ML and classical algorithms can be easily understood regarding a simple text. Classical algorithms interpret it as a sequence of keywords, while the ML algorithms use semantic analysis and mimic the human ability to understand the content of the text. The most uses ML algorithms are supervised ML algorithms, unsupervised ML algorithm, semi – supervised ML algorithms and reinforcement ML algorithms.

Furthermore, the techniques used in ML can discover valuable patterns in data. This characteristic is very valuable in the manufacturing sector and leads to Advanced Manufacturing (AM) procedures. Because the manufacturing world is ever evolving, there are no universally applicable methods and is important to have a clear understanding of the requirements of each task. ML applications are useful in the manufacturing because:

  1. deal with different data (numerical, nominal, text, and image),
  2. handle noise, outliers, fuzzy data,
  3. real time processing,
  4. deal with large data sets and data of high dimensions,
  5. produce easily understood results,
  6. simple to implement.

Finally, there are several advantages in the ML application for the AM, such are:

  1. the techniques are able to handle NP complete problems for optimization of intelligent manufacturing
  2. ML handles high – dimensional, multivariable data and extracts relationships within large datasets and evolving manufacturing environment
  3. ML increases the understanding of the manufacturing domain
  4. Improves the lifecycle of the manufacturing process
  5. Discovers unknown relationships between the manufacturing entities (Pham & Afify, 2015)

Future workers in advanced manufacturing need to be trained not only in cutting edge technology, but also in creativity, team leading, problem solving, self-learning, adaptability and flexibility in order to be employable and provide services of high quality. DTAM’s training curriculum aims to fulfil this emergent need and provide and integrated programme with the appropriate balance of skills for advanced manufacturing workforce of today and tomorrow.

Thus, one of five Innovative Training Modules of DTAM will be dedicated to Machine Learning as a modular digital training pathway for IT and OT technicians in AM.

Stay tuned to take full advantage of the DTAM training course we are working on.

#DTAMproject #upskillingyourfuture #Industry40 #AdvancedManufacturing #MachineLearning

References:

[1] Pham, D., & Afify, A. (2015). Machine-learning techniques and their applications in manufacturing . Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering, 219(5): 395-412.

[2] Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016). Machine learning in manufacturing: advantages, challenges and applications. Production & Manufacturing Research, 4:1, 23-45.