GRECO | Fostering a Next Generation of European Photovoltaic Society through Open Science
01-06-2018
-31-05-2021
01-06-2018
-31-05-2021
01-10-2017
-31-03-2022
01-06-2020
-31-03-2023
01-10-2020
-30-09-2023
01-10-2020
-31-03-2024
01-10-2020
-30-09-2024
01-10-2020
-30-09-2023
COALA's three use cases (textile, white goods, liquid production) will evaluate the COALA solution in their manufacturing processes with significant economic value.
The COALA solution will support the workers when performing process and product quality inspections with quality predictions and prescriptions for mitigation measures. The Augmented Manufacturing Analytics feature is A set of DIA functions interfacing the prescriptive quality analytics service connected to shop floor data sources. It will enable non-data-scientist workers to utilize and customize data analytics during product quality tests.
COALA will contribute in the ongoing discussion about AI ethics and potential standards, monitor the standardization potential for worker education under consideration of AI competencies, and will use and contribute to IT standards. We will take into account available IT-related standards and use them when applicable. This includes normative standards as set by ISO and its national bodies, which are of high importance to industrial companies. We will assess standards proposed by major influential de-facto standardization bodies like W3C, OASIS, and OMG. Standardization topics concern:
COALA solution for on-the-job-training using digital assistant allows the workers to meet the requirements of their work environtment: to be skilled and flexible. To keep up with continuously changing demands and to cope with altering product and process information, production workers need to adopt an approach that welcomes change. They need to be empowered by making customization transparent, enabling rapid adaptations, providing operational flexibility, and augmentation of their skills.
COALA aims to develop a cognitive advisor with dedicated human-assisted AI methods for enabling transfer of tacit knowledge of experts to novice workers. The transfer of tacit knowledge of production workers that grows with their experiences enabling them to cope with challenges of agile manufacturing.
For the change over time we expect a reduction of 15% to 30% by shortening the worker training time.
COALA is expected to impact on the productivity of employees involved in the quality detection
and repair as well as on reduction of the not detected defects delivered.
During the design and development of the COALA didactic concept, societal and environmental well-being is considered through impact analysis and the change of social skills of workers.
COALA solution will support workers that need to use analytics tools and new workers that perform on-the-job training. Complementary to the technology, an education and training concept that focuses on building blue collar worker competencies in human-AI collaboration will be developed. The COALA solution will transform how workers perform their jobs and it allows companies to maintain or increase the quality of their production processes and their products.
COALA solution is developed to consider following factore: factory noise, needed worker capabilities, variety of production machines, expected product and process quality, time-criticality, and worker safety.
01-01-2021
-31-12-2023
01-11-2020
-30-04-2024
01-11-2020
-31-10-2023
01-01-2021
-30-06-2024
01-06-2020
-30-11-2022
01-11-2020
-31-10-2023
01-01-2021
-31-12-2023
01-01-2021
-30-06-2024
01-10-2020
-31-03-2024
01-03-2020
-31-08-2024