A4BLUE | Adaptive Automation in Assembly For BLUE collar workers satisfaction in Evolvable context
01-10-2016
-30-09-2019
01-10-2016
-30-09-2019
01-10-2016
-30-09-2019
01-10-2016
-30-09-2019
01-10-2016
-31-03-2020
01-10-2016
-30-09-2019
01-01-2019
-31-07-2022
Cockpit optimiser and Milling Digital Twin with AI tools for accelerating current design and optimisation processes by operators
Solutions to facilitate the analytical thinking of the operator. The solution will help the operator with the correlation of quality and process parameters in order to make a decision upwards in the process.
With the help of skilled production line workers, the data in the AI platform can be annotated and herewith produce the predictive models for ZDM autonomous quality inspection. The platform gives users the ability to monitor the AQ process (Autonomous Quality) and provide feedback for the ZDM.
To acquire quality data, all involved users and managers must understand some basic data science principles. Machine vision in modern times relies on large amount of consistent data. Data acquisition process begins with organized collection of samples, which should become an integral part of every standardized manufacturing process that involves automated quality inspection or ZDM.
There is a need of managing large Data Sets and Big Data, IA solutions for different Manufacturing Processes. Solutions need to support operators in decision-making
Enable operators to work in a more complex environment while reducing the strain of administrative tasks and enabling easy production analytics by capturing information online instead of on paper.
Shopfloor worker (operator – technical support group): From a shopfloor perspective new job profiles, or altered job profiles should be defined, however In essence the job profiles will remain the same, while the operators and Technical Support Groups need to understand & be able to work with these new technologies. This requires some basic knowledge on the (digitalized) systems, for the operators a lot can be captured in SOP’s (Standard Operating Procedures), but the technical support staff should also have some basic knowledge on the workings and the hardware/software side of the systems in order to be able to support the shopfloor where needed.
The ZDM-Autononous Quality Solutions are used as systems that perform tasks in an autonomous/automated way, requiring the intervention of an operator only when an operational tie-breaker is needed. When that is the case, the operator has to analyse the incident and provide for a solution to the AQL System, interacting with it via an HMI interface.
Complete machine parameters correlation is realized, allowing machine operators to take into account all the assets from each workstation of the production line. It enhances its capacity in relation to conventional analytics methods.
The end2end process supported by the overall architecture helps the operator and team leader in their daily activities in order to prevent and anticipate as much as possible quality issues on the product via the analysis of a huge amount of data linked together via the holistic semantic model.
01-10-2020
-30-09-2023
COALA calls for a new experience in human-machine collaboration: it fosters a vision of human-AI symbiosis in which AI augments its users instead of replacing and devaluing them. COALA aims to assist human reasoning, keep humans in decision making without directing them algorithmically.
COALA will develop a Digital Intelligent Assistant (DIA) that can advise blue collar workers, such as machine operators and line managers, in complex, agile, and quality-focused discrete or process manufacturing industry. At this concern, human-centred interaction with the assistant needs to consider aspects such as the instant availability of information, support to problem solving of an adaptive system (e.g. context-awareness, internal information parsing, and impact evaluation).
01-10-2020
-31-03-2024
A4BLUE enables the transference of knowledge among workers and from the organization to the workers as well, including best practives, instructions, tips from workers, etc., which is integrated within the ICT infrastructure for production.