SCOTT | Secure COnnected Trustable Things
01-05-2017
-31-10-2020
01-05-2017
-31-10-2020
01-01-2019
-31-07-2022
One the one hand, some pilot owners expect that no particular skills will be requested after the QU4LITY project development implementations. For example:
New job profiles and associated skills are: Digital Business Processes Analyst, Expert in Machine Learning Algorithms, DevOps Development knowledge, Data scientist (programming and statistical knowledge), Artificial Inteligence knowledge, Cybersecurity expert, Ontology architects and modellers in MBSE, Digitalized systems Shopfloor worker, Digital and connectivity engineering, New systems integration Manufacturing Engineer, Cloud -Data Formats - Data analytics Engineer, Product, manufacturing and quality global knowledge.
Re- and upskilling needs were identified in the following areas: AI and Data analytics; Agile development, Multi disciplinary project management (IT, mechanical, electrical engineering); Design Thinking; Standardization; Data Analysis and Data Space technology for Manufacturing; IT Skills : Docker environment and languages like phyton of json; Data Analytics : basic skills , BI softwares
Programming languages such as C#, C ++, HTML, Java, Microsoft .NET and SQL Server ; data tools for data cleaning and preprocessing, data parsing, data feature engineering; machine to machine (M2M) data and protocols; Machine Learning Skilling for all languages/ ML Systems; Data analysis skills
The following knowledge delivery mechanism where identified as relevant: AR/VR, gamification, on-the-job training, vocational training, MooCs (Massive Open Online Courses)
01-01-2019
-30-06-2023
01-01-2019
-31-12-2022
01-11-2018
-31-10-2022
01-10-2018
-31-03-2023
01-05-2015
-31-07-2018
01-06-2017
-31-05-2020
01-11-2015
-31-10-2017
01-09-2017
-28-02-2021
UPTIME will develop a versatile and interoperable unified predictive maintenance platform for industrial & manufacturing assets from sensor data collection to optimal maintenance action implementation. Through advanced prognostic algorithms, it predicts upcoming failures or losses in productivity. Then, decision algorithms recommend the best action to be performed at the best time to optimize total maintenance and production costs and improve OEE.
UPTIME innovation is built upon the predictive maintenance concept and the technological pillars (i.e. Industry 4.0, IoT and Big Data, Proactive Computing) in order to result in a unified information system for predictive maintenance. UPTIME open, modular and end-to-end architecture aims to enable the predictive maintenance implementation in manufacturing firms with the aim to maximize the expected utility and to exploit the full potential of predictive maintenance management, sensor-generated big data processing, e-maintenance, proactive computing and industrial data analytics. UPTIME solution can be applied in the context of the production process of any manufacturing company regardless of their processes, products and physical models used.
Key components of UPTIME Platform include:
One of the main learnings is that Data quality needs to be ensured from the beginning of the process. This implies spending some more time, effort and money to carefully select the sensor type, data format, tags, and correlating information. This turns to be particular true when dealing with human-generated data. It means that if the activity of input of data from operators is felt as not useful, time consuming, boring and out of scope, this will inevitably bring bad data.
Quantity of data is another important aspect as well. A stable and controlled process has less variation. Thus, machine learning requires large sets of data to yield accurate results. Also this aspect of data collection needs to be designed for example some months, even years in advance, before the real need emerges.
This experience turns out into some simple, even counterintuitive guidelines:
1. Anticipate the installation of sensors and data gathering. The best way is doing it during the first installation of the equipment or at its first revamp activity. Don’t underestimate the amount of data you will need, in order to improve a good machine learning. This of course needs also to provide economic justification, since the investment in new sensors and data storing will find payback after some years.
2. Gather more data than needed.
A common practice advice is to design a data gathering campaign starting from the current need. This could lead though to missing the right data history when a future need emerges. In an ideal state of infinite capacity, the data gathering activities should be able to capture all the ontological description of the system under design. Of course, this could not be feasible in all real-life situations, but a good strategy could be populating the machine with as much sensors as possible.
3. Start initiatives to preserve and improve the current datasets, even if not immediately needed. For example, start migrating Excel files distributed across different PCs into common shared databases, taking care of making a good data cleaning and normalization (for example, converting local languages descriptions in data and metadata to English).
Finally, the third important learning is that Data Scientists and Process Experts still don’t talk the same language and it takes significant time and effort from mediators to help them communicate properly. This is also an aspect that needs to be taken into account and carefully planned. Companies need definitely to close the “skills” gaps and there are different strategies applicable:
train Process Experts on data science;
train Data Scientists on subject matter;
develop a new role of Mediators, which stays in between and shares a minimum common ground to enable clear communication in extreme cases.
Quantity and quality of data: the available data in the FFT use case mainly consists of legacy data from specific measurement campaigns. The campaigns were mainly targeted to obtain insights about the effect of operational loads on the health of the asset, which is therefore quite suitable to establish the range and type of physical parameters to be monitored by the UPTIME system. UPTIME_SENSE is capable of acquiring data of mobile assets in transit using different modes of transport. While this would have been achievable from a technical point of view, the possibility to perform field trials was limited by the operational requirements of the end-user. Therefore, only one field trial in one transport mode (road transport) was performed, which yielded insufficient data to develop useful state detection capability. Due to the limited availability of the jig, a laboratory demonstrator was designed to enable partially representative testing of UPTIME_SENSE under lab conditions, to allow improvement of data quantity and diversity and to establish a causal relationship between acquired data and observed failures to make maintenance recommendations.
Installation of sensor infrastructure: during the initial design to incorporate the new sensors into the existing infrastructure, it is necessary to take into consideration the extreme physical conditions present inside the milling station, which require special actions to avoid sensors being damaged or falling off. A flexible approach is adopted, which involves the combination of internal and external sensors to allow the sensor network prone to less failure. Quantity and quality of data: it is necessary to have a big amount of collected data for the training of algorithms. Moreover, the integration of real-time analytics and batch data analytics is expected to provide a better insight into the ways the milling and support rollers work and behave under various circumstances.
Quantity and quality of data need to be ensured from the beginning of the process. It is important to gather more data than needed and to have a high-quality dataset. Machine learning requires large sets of data to yield accurate results. Data collection needs however to be designed before the real need emerges. Moreover, it is important having a common ground to share information and knowledge between data scientists and process experts since in many cases they still don’t talk the same language and it takes significant time and effort from mediators to help them communicate properly.
01-10-2017
-31-03-2021
01-10-2017
-30-09-2020
01-09-2017
-30-11-2022
01-11-2017
-30-04-2021
01-05-2018
-31-10-2021
01-06-2016
-30-09-2019
01-07-2015
-31-07-2018
01-06-2018
-31-05-2021
01-06-2018
-31-05-2021
01-01-2018
-01-01-2021
01-10-2018
-31-03-2022
ROSSINI develops and demonstrates technologies enabling a significant advancement in HRC. They are:
These technologies will be then integrated into the ROSSINI Platform architecture.
Expected achievements: 15% increase in OECD Job Quality Index through work environment and safety improvement; 20% reduction in production reconfiguration time and cost; reduction of heavy works impacts and costs: increase in the overall job satisfaction and job attractiveness; increased value-chain integration and stakeholder satisfaction
HRC applications pose several challenges to the manufacturing industry which sees an increased need for automation and scalability, notably in SMEs. Moreover, at the moment, HRC applications imply also huge investments in terms of effort, time and intellectual capital to integrate robots and sensors into the manufacturing workflow which can’t be afforded by most of the European SMEs, notably if the production combines low volume with high mix. Trough ROSSINI project, implementation of real and cost effective HRC contributes to redesign workplaces combining automation and lean manufacturing concept, with a drastic reduction of conversion and reconfiguration costs.
The development of the Rossini Modular KIT allowed to bring significant advance in terms of tecnology and awareness on collaborative robotics for all Europe. In particular, the set of efficient and modular tools developed within Rossini enable and ease different specific activities: from the hazarda assessment evaluation untill the multiple detection of humans on a monitored area where robots are working. Nevertheless, important activities still need quite a few effort in terms of knowledge, development and collaboration: from the necessity to refine (or define new well-establised interfaces) untill the identification of solutions able to bring the human-robot iteraction even closer (and more trustworthy), also in terms of standadization (that still present several gaps on this topic).
In particular, the following topic have been identified as important issues to tackle in future activities and research:
ROSSINI helps European factories to attract skilled workforce in factories because of the attention paid to job quality and employee satisfaction
01-10-2018
-31-03-2022
In order for CoLLaboratE to successfully realize its vision, several prerequisites were set in the form of major Scientific and Technological Objectives throughout the project duration. These are summarized in the following points:
Objective 1: To equip the robotic agents with basic collaboration skills easily adaptable to specific tasks
Objective 2: To develop a framework that enables non-experts teaching human-robot collaborative tasks from demonstration
Objective 3: The development of technologies that will enable autonomous assembly policy learning and policy improvement
Objective 4: To develop advanced safety strategies allowing effective human robot cooperation with no barriers and ergonomic performance monitoring
Objective 5: To develop techniques for controlling the production line while making optimal use of the resources by generating efficient production plans, employing reconfigurable hardware design, and utilising AGV’s with increased autonomy
Objective 6: To investigate the impact of Human-Robot Collaboration to the workers’ job satisfaction, as well as test easily applicable interventions in order to increase trust, satisfaction and performance
Objective 7: To validate CoLLaboratE system’s ability to facilitate genuine collaboration between robots and humans
The CoLLaboratE project will have profound impact on strengthening the competitiveness and growth of companies in the manufacturing sector:
- CoLLaboratE developed a co-production cell for manufacturing production lines, capable to perform assembly operations through human-robot collaboration. This cell is the result of inter-disciplinary technological advances that were realized during the project, in a series of highly significant areas related to robotics and artificial intelligence. The proposed system has been demonstrated and evaluated at TRL6, being ready for commercial take-up, allowing this assembled knowledge to be in turn, rapidly integrated in real production lines of industries and SMEs.
- CoLLaboratE developed technologies for autonomous and collaborative assembly learning and teaching methods by non-experts so that no explicit robot programming is required. As the products of industries, such as LCD TV’s rapidly evolve, flexibility so as to easily adapt in a new assembly task regarding a new product, is a major quality sought for modern assembly lines. Robots that need several months to be programmed and start working on the task are a rather unrealistic solution. Given the (a) time-consuming programming process typically required for industrial robots and (b) difficulties posed to robots from uncertainties in small parts assembly, cheap labour hands of low cost countries (LCCs) have so far been typically utilized instead of robotic solutions, through LCC assembly outsourcing strategies.
- CoLLaboratE service portfolio included a set of innovative fast and flexible manufacturing techniques, combining the benefits of the reconfigurable hardware design and modern ICT technologies (e.g. AΙ, learning toolkit, digitization of assembling processes)
- CoLLaboratE introduced novel AGVs on shop floors with enhanced capabilities, that apart from motion planning and obstacle detection, they are also capable of detecting the intentions of human users in the factory in order to provide flexibility and facilitate the production process, along with optimal use of resources.
- CoLLaboratE reduced delivery times and costs, whereas robot assembly techniques will also allow a much greater degree of customization and product variability. As it is highlighted in the euRobotics AISBL Strategic Research Agenda, the use of robotics in production is a key factor in making manufacturing within Europe economically viable; locating manufacturing in Europe through robotic solutions that will suppress LCC outsourcing is a major goal for the near future. Through flexible assembly lines, the manufacturing companies will be offered with great leverage over their innovation capacity and integration of new knowledge into their products.
- CoLLaboratE paved the way for a new era in industrial assembly lines, where robots will present genuine collaboration with the human workers and will allow manufacturing industries to establish in-house robotic-based assembly lines, capable to rapidly adapt in continuously evolving products. Through its advances, SMEs holding robotic-based assembly lines, will benefit by acting as subcontractors for large industries, since they will be a viable alternative to LCC outsourcing.
It becomes clear that the CoLLaboratE project has profound potential to strengthen the competitiveness and growth of companies and bring back production to Europe, by implementing novel artificial intelligence technologies and integrating robots with collaborative skills in the production, meeting a specific, highly important need of European, as well as worldwide manufacturing industries toward their future growth and sustainability.
The target users for the CoLLaboratE system are manufacturing industries in need of flexible and affordable automation systems to boost their global competitiveness. Successful completion of CoLLaboratE will allow SMEs and large manufacturing companies in Europe to easily program assembly tasks and flexibly adapt to changes in the production pipeline. Such ease of use and rapid integration time of robotic assembly systems is expected to pave the way for step change in the adoption of not only collaborative robots, but a complete collaborative environment provided by the CoLLaboratE solution.
The main innovation will be represented by the introduction in production of MPFQ model fused with AQ control loops: Functional Integration and Correlation between Material, Quality, Process and Appliance Functions.