How Digital Twins are Revolutionizing the Manufacturing Industry

Wouldn´t it be great to simulate things varying from toasters to planes, modify processes and prevent problems or even accidents before investing real-world resources into actual implementation? That’s what digital twins are able to offer.

But what are digital twins? They are exact virtual representations of physical objects, processes, or even systems that allow for real-time monitoring, analysis and optimization, before the actual object etc. has taken its final form. More specifically, by collecting data from sensors and other sources, it is possible to create a digital twin that accurately reflects the performance of a physical system.

Digital twins can be useful in a lot of sectors, but their popularity and use in the manufacturing section increase day by day.

One of the greatest things digital twins can offer is the ability to optimize production processes. A digital twin of a production line can simulate and enhance the overall performance of the system, identifying possible bottlenecks and inefficiencies beforehand to reduce maintenance issues and optimize the production process. In addition, digital twins can help improve the manufacturing process by identifying quality issues and ensuring that the final product meets the required quality standards. For instance, thanks to these practices, Boeing was able to achieve a 40% improvement rate in first-time quality of parts.

The use of digital twins in advanced manufacturing is not limited to the production line or product design. They can also be used to monitor and optimize the performance of machines and robots.  Practically, we can simulate all kinds of robots, from robot vacuums to humanoids that could potentially ruin the world, and observe its movements, actions, and interactions with other objects in a virtual environment. This can help identify potential issues with the robot’s design or programming and optimize its performance before it is deployed in the real world. By doing so, the downtime could be reduced while the manufacturing process could overall become more efficient. Even when they are launched, by collecting data, it is possible to create a digital twin that accurately reflects the performance of the physical robot. Like that, the robot’s movements can be monitored, possible issues could be identified, and performance could be optimized in real-time. And who knows, you could potentially save the world.

However, it´s not all daisies and roses. Some big problems may occur. One of them is data privacy and cybersecurity. Digital twins rely on the collection of data from various sources, including sensors, machines, and people. It is important to ensure that the data collected is secure and that privacy concerns are addressed. In this case, companies should be obliged to have policies and procedures in place to protect sensitive data and ensure that it is not shared or used inappropriately. Furthermore, digital twins are only as good as the data that is used to create them. Errors in the data can lead to incorrect conclusions and suboptimal decisions.

Last but not least, the subject could raise ethical concerns, especially when monitoring individuals or making decisions that affect them. Furthermore, the potential for bias and discrimination is raised. If the data used to create a digital twin is biased or incomplete, it could lead to discriminatory practices that impact certain groups of people. It is therefore important to ensure that the data used is accurate, validated, and reliable. In addition, questions about accountability and responsibility can be raised. If decisions are made based on the analysis of digital twin data, who is responsible for those decisions? Should there be transparency around how digital twin data is used and who is making decisions based on that data?

In conclusion, in today’s fast-paced manufacturing world, digital twins have emerged as a game-changing technology, offering businesses the ability to optimize processes, reduce downtime, and improve product quality. However, as with any powerful technology, there are also risks and ethical concerns to consider. By carefully balancing the benefits and potential downsides, businesses can harness the power of digital twins to transform their operations and stay ahead of the competition.

Featured image credit: Freepik.

Da Vinci College Initiates Second Pilot Round of “IoT and Sensors” Module in DTAM Project

Some while ago we told you about how our partners from Apro Formazione and Txorierri are running a combined exercise as part of the DTAM pilot training activities. Today, we would like to the same and let you know how the piloting activities are going on the other end of Europe i.e. the Netherlands.

Da Vinci College, a key partner in the international DTAM project (Digital Transformation in Advanced Manufacturing), is committed to providing students with a cutting-edge education. The European DTAM project focuses on various subjects such as cybersecurity, IoT and sensors, Big data, Machine learning, and Transversal skills, empowering students with the knowledge necessary to excel in the rapidly evolving field of advanced manufacturing.

On Monday, May 15th, a group of 55 vocational (EQF level 4) students embarked on the second pilot round of the “IoT and Sensors” module. These students follow a 3-year curriculum for “software developer”, consisting of 2 years at school and 2 internships at a software development company.

The “IoT and Sensors” module plays a vital role in the DTAM project, exploring the principles of the Internet of Things (IoT) and the significance of sensors in transforming manufacturing processes.

Under the guidance of their instructors, Da Vinci College students will engage in a comprehensive module comprising lessons and exercises designed to deepen their understanding of the Internet of Things. The module encompasses various topics, including sensor technologies, data collection and analysis, connectivity, and the application of IoT principles in industrial contexts.

The pilot will end with a challenge, where students are asked to solve a real-world problem by creating a prototype device. In our case, the challenge consists of building a temperature/air humidity sensor device to monitor climate conditions in a building and to warn when dangerous levels are reached.

Through their participation in the pilot round, students will not only acquire in-depth knowledge but also provide valuable feedback for further refinement of the module. This collaborative approach ensures continuous improvement and an enhanced learning experience for future students.

Da Vinci College takes great pride in its partnership with the DTAM project. By enabling students to explore the latest advancements in digital transformation, the college prepares them for the challenges of advanced manufacturing.

Da Vinci College extends its appreciation to the teachers, project partners, and students involved in this project!

ThINKER LAB project – Tinkering Laboratories for inclusive and active learning

Creating a professional training program for technicians with the necessary skills to facilitate the migration to Industry 4.0, as envisaged by the DTAM project, it is required a deep knowledge of the subjects and technologies involved in the automation process of the industrial sector.

In this sense, there are projects that help deepen the use of advanced – and usually high-cost – technologies even within the walls of a classroom. One such opportunity is provided by the ThINKER LAB project, coordinated by Apro Formazione and realised thanks to a partnership of four European partners: Txorierri (Spain), SIC Ljubljana (Slovenia), Salpaus (Finland), Goteborgs Tekniska College (Sweden).

ThINKER LAB project, funded by the Erasmus+ programme, aims to provide a different approach to the study of STEM subjects, particularly for students with learning difficulties. Specifically, the project allows students to gain real-world experience of machines and automation systems, harnessing teachers’ skills and ideas in recreating industrial environments and using low-cost or shared hardware and solutions. The idea is to revolutionise the concept of industrial simulators in vocational schools by proposing a new approach with the following features:

  • Low-cost components such as actuators and sensors used in open-source systems, e. g. Arduino.
  • Easy construction using 3D printers, recycled materials and common tools that every school uses in its laboratory
  • Easy connection with student projects
  • Transparency of the interface: students do not need to know what the simulator looks like, they have to use the simulator like a real industrial system

Among the expected results of the project is the creation of an open online platform where teachers can find ideas and solutions to create their own simulators, downloading ready-to-use projects prepared by the project partners. But in addition to consulting the guidelines and projects, the platform is also intended to be a space where to upload new projects, share them with the community, give feedback, discuss and propose ideas, ask for suggestions or propose different solutions for common issues. At the end of the project, scheduled for November 2023, the platform will be open to welcome everyone’s contribution, so that it will be maintained and grow over time as a true reference point for teaching STEM subjects in VET schools.

Currently, a large collection of best practices is available on the site, from which teachers from all over Europe can extract teaching materials to use in the classroom. For example, a chemistry project in which a fluid analyser is created based on the use of red cabbage; a loudspeaker project, bicycle shelter projects, photovoltaic phone chargers and projects to survey trees in parks. All projects are freely downloadable and have minimal implementation costs that everyone can afford.

ThINKER LAB is an important project for reaching DTAM’s objectives because many of the skills that can be developed using the materials on the platform are closely related to the DTAM training course. Thanks to the tools on the platform, one can learn to use programmable boards such as Arduino, various types of sensors, such as the ultrasonic sensor that allows one to create an exercise to calculate the volume and weight of a product in a container, but also the use of:

  • stepper motors, used to make a clock;
  • optical sensors for object recognition;
  • temperature sensors, used in an exercise to control the overheating of a carbon fibre.

In addition, the IoT laboratory at Apro Formazione (Alba, Italy) is fully online and available to be used remotely by other schools that do not have it. Soon the other labs of the partnership will also be online.

In April and May 2023, five national and international Hackathons were held as part of the project activities. The student groups challenged each other on the development of a theme and were judged on technical correctness, amount of recycled materials used, number of external materials developed and compliance with the allocated budget.

The winners were the students of the Slovenian partner SIC Ljubljana, who presented a project on a package sorting system: using simple materials and specific sensors, they created a system capable of sorting packages with certain physical characteristics.

Find out more about the ThINKER LAB project, its activities and teaching tools by visiting

CECIMO “Transformation of Manufacturing: Embracing Digital and Green Skills” Report

The European Association of the Machine Tool Industries and related Manufacturing Technologies or CECIMO, is an umbrella organisation that serves the common interests and values of the European Machine Tool Industries and related Manufacturing Technologies at EU and global level. Our very own DTAM Project member AFM CLUSTER belongs to CECIMO with other 14 national associations in Europe.

CECIMO in collaboration with its’ member associations has recently published the “Transformation of Manufacturing: Embracing Digital and Green Skills” report, which gives information about:

  1. The current state of play
  2. Dual Shortage: Skills and Labour
  3. Emerging Skills
  4. Focus areas

This report confirms that the shift towards sustainable practices and digital solutions is becoming increasingly important in all sectors and advanced manufacturing is no exception. The digital and green transitions are two inevitably interlinked megatrends. It also confirms that machine tool manufacturers are going through a period of transformations and are facing two main challenges, the shortage of required technical skills across the production line and the immediate labour shortage.

The report provides detailed information about both challenges, as well as details about the emerging skills, specifically regarding digital skills, green skills and other future skills.

In line with project DTAM’s mission, the report states that “education is a key driver in building the new generation of skilled employees in manufacturing” and continues by listing 4 key actions to be undertaken by educational institutions i.e.:

  1. Develop tailored courses and modernised training facilities to offer the right skill sets that can adapt to the new dynamic of manufacturing.
  2. Provide work-based learning options, including apprenticeships to mitigate skill mismatches in times of rapid technological change.
  3. Offer sector-specific courses and specialised technical training to meet the needs for digital skills such as Mobile Learning in Smart Factories (MLS) platform as a learning tool in education and training for young professionals in mechanical engineering or technical high schools.
  4. Establish greater coordination and collaboration between workers, educational institutions, and industries to encourage the uptake of mechanical engineering in university courses through innovative teaching methods such as gamification.

In addition, the report provides an analysis of the gradual development of new roles that will complement the traditional roles in the Machine Tool industry, in light of the digital and green transformation

Access the full report via the following link:

Artificial Intelligence in Advanced Manufacturing

Machine Learning (ML) is a subset of AI, which is concerned with the design and development of algorithms that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed to do so. In other words, ML is the process of training a model using data, so that it can make predictions or decisions about new data that it has not seen before.

Nowadays, manufacturing is entering a period of substantial innovation and change driven by the increased integration of sensors and the Internet-of-things (IoT), increased data availability, and advances in robotics and automaton. Its operations across industries are faced with the challenge of meeting throughput, quality, and cost objectives while ensuring a safe working environment. This has become increasingly difficult due to growing product and process complexity, higher variability in customer demand and preferences, and relentless competitive pressures [1]. Artificial Intelligence (AI) is a great ally in meeting these needs.

In recent years, Artificial Intelligence has shown significant progress in automating activities that are associated with human thinking, such as planning, decision making, and problem solving. An over sixfold increase in the number of publications from 2015 to 2020 an estimated 15.7 trillion-dollar worth of economy in 2030 [2] are only a few indications of the vast existing and future impact of AI in both academia and industry.

AI offers unique capabilities for problem-solving, especially in identifying and classifying multivariate, nonlinear patterns in operational and performance data that are hidden to plant engineers. There is a huge amount of continuously generated data from machines, ambient sensors, controllers, and labour records, which can be categorized into environmental data, process data, production operation data, and measurement or check data. These data are the reflection of the physical machine characteristics [3]. AI techniques have advanced the state of monitoring, diagnosis, and prognosis, which are of critical value to modern manufacturing. This allows for an abundance of applications, such as predictive maintenance or quality control.

Some of the applications of AI are listed below:

  • Quality control: AI can be used to inspect and detect defects in real-time, improving the quality control process and reducing defects.
  • Predictive maintenance: By analysing sensor data, AI can predict when maintenance is required, reducing downtime, and increasing productivity.
  • Process optimization: AI can analyse data from various sensors to optimize manufacturing processes, reducing waste, and increasing efficiency.
  • Supply chain management: AI can be used to monitor and optimize supply chains, reducing costs and increasing delivery efficiency.
  • Autonomous robotics: AI-powered robots can perform repetitive and dangerous tasks, increasing productivity and reducing the risk of accidents.
  • Product design: AI can assist in designing products by generating and evaluating various design options based on customer requirements and market trends.

It is important to recognize the significance of getting companies ready for the shift towards digitalization and adapting to the challenges that come with technological disruptions. This highlights the need for educating and providing training for the younger generation in these areas, which is where the DTAM project comes in. The main objective of the DTAM project is to prepare students in thIe field of digital transformation and equip them with the necessary skills to face the challenges in this area.

Our training course on Digital Transformation Technologies is on its way to being pilot tested. Stay in the loop as we are going to be sharing more exciting news very soon.


[1] Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., and Zhang, J. (August 13, 2020). “Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook.” ASME. J. Manuf. Sci. Eng. November 2020; 142(11):110804.

[2] PWC, 2021. Pwc’s Global Artificial Intelligence Study: Sizing the Prize. 

[3] Mojtaba Mozaffar, Shuheng Liao, Xiaoyu Xie, Sourav Saha, Chanwook Park, Jian Cao, Wing Kam Liu, Zhengtao Gan, “Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives, Journal of Materials Processing Technology”, Volume 302, 2022.

[4] Featured image: Freepik/vectorjuice

Robots inspired by human behavior

When we are talking about smart manufacturing, robots cannot be missed. Industrial robots have considerably increased productivity and relieve humans of heavy tasks since they were first incorporated into manufacturing lines. As the use of robots raises many ethical issues regarding the employability of humans, it is important to mention the words of the professor Robert Radwin: “We envision a workplace where workers won’t be replaced by robots, but rather where robots will assist workers in their jobs. That’s our goal” [1].

Robot learning combines several machine learning techniques with robotics-related content i.e., in robot learning and behavior. Robot learning places more focus on producing actions as the output than traditional machine learning does on monitoring the environment as the input. For instance, whereas reinforcement learning offers formalizations for machine behavior, deep learning assists the robot in handling unstructured settings. Animal behavior is similar to this type of scenario.


Figure 1: Human behaviour and machine behaviour [2]

Humans use their sense organs, such as their eyes, nose and hands, which are connected to the brain through the neurological system, to perceive their surroundings. For instance, light-sensitive cells on the retina detect environmental light before sending bio-electricity signals to the cerebral cortex. To create motion, however, the cerebral cortex also communicates with the relevant muscles. Close-loop formation is created by this pipeline, same as in automatic control. For instance, a human will use his eyes to detect the glass of milk and watch his hand as it to reach it, take it to his lips, drink from it, and then put the glass back. Similarly, behaviors are shared by robotic devices in the closed-loop, as seen in the Figure 1. Cameras are used by the robot to examine its surroundings, while computers are used to analyze perceptual data. The robotic manipulator is then driven by algorithms sending commands to the robot controller [2].

Lastly, some of the benefits of operating robots in industry include, reducing programming time and automation costs, making it easier for workers in the industry. Stephen Hawking, a theoretical physicist, believed that humans would be replaced by robots, and robotic technology has advanced more quickly than expected [3]. Thus, this robot-human matter needs more research and development so that worker’s jobs are not threatened.


[1] L. Liu et al., “Human Robot Collaboration for Enhancing Work Activities,” Hum. Factors, 2022, doi: 10.1177/00187208221077722.

[2] Z. Liu, Q. Liu, W. Xu, L. Wang, and Z. Zhou, “Robot learning towards smart robotic manufacturing: A review,” Robot. Comput. Integr. Manuf., vol. 77, p. 102360, Oct. 2022, doi: 10.1016/J.RCIM.2022.102360.

[3] How robots are stealing human jobs and threatening our future.” (accessed Jan. 04, 2023).

Featured image: Freepik/macrovector

Are you taking advantage of the Digital transformation?

Nowadays, the exponential growth of technological advances is causing disruptions and continuous changes at a global level. The impact these changes are having on industry is also affecting on the strategies of managers, those responsible for R&D&I departments, etc.

The digital transformation we are undergoing is having an impact on the processes, strategy and culture of companies. In this regard, some conclusions drawn from a survey conducted by KPMG in 2017 stated that:

  • Companies do not know how to implement and deploy digital transformation in their own processes.
  • Digitalisation is seen as a phenomenon that generates tactical benefits, such as cost reduction, etc., but they are not aware of its actual benefits.
  • There is a lack of knowledge about what kind of technology a company can use to cope with digital transformation.
  • Organisations are showing increased interest in cognitive automation (also known under the phenomenon of Machine Learning / Artificial Intelligence).
  • The main barriers to these changes are often related to a lack of strategic vision, uncertainty about where to start the process towards digitalisation, lack of technology profiles or digital skills among the workforce.

In the absence of organisational capacity and/or culture to understand the impact these technologies are having on businesses, it becomes even more important to understand some of the wide range of existing disruptive technologies out there. For instance, the Internet of Things and Data Analytics are already being exploited to bridge the gap needed to achieve productivity goals and improve customer experience. The use of Artificial Intelligence and robotic automation software to manage transactions is making it possible to predict customer needs.

In this sense, we understand the relevance of preparing companies for the digital transformation and how to face the challenges posed by the technological disruption we are experiencing. Therefore, it is necessary to educate and train new generations of students in these areas, hence the importance of the DTAM project, which aims to do exactly that. 

Our training course on Digital Transformation Technologies is on its way to being pilot tested. Stay in the loop as we are going to be sharing more exciting news very soon.

Featured image credit: Freepik/vectorjuice

Collaboration between Industry and the Academic world in the Cybersecurity Field

Within the digitalization strategy of Politeknika Txorierri, Jokin Goioaga, the Electricity and Electronics department coordinator, accompanied the cluster formed by Cybasque (group Gaia) and Basque Cybersecurity Center in a trip to Cardiff (Wales), alongside a group of professors and ICT managers of several Hetel centers.

The aim of the trip was to get to know the cybersecurity ecosystem formed by the education centers, institutions and specialized companies. During the visit they met with the regional government representatives, the University of Cardiff representatives and the managers of the companies Thales and Purecyber. Moreover, he had the opportunity to attend the launching of a partnership event between Airbus and the University of Cardiff about excellence in socio-technical cybersecurity.

Currently, Wales is considered one of the most important innovation poles in cybersecurity. In this aspect, it is worth mentioning the collaboration between the public and private sectors, and the proximity between the companies of the sector and the academic institutions, as this assures outstanding professionals, according to the necessities of the industry, that help develop this sector in Wales.