{"id":5559,"date":"2023-09-26T07:55:14","date_gmt":"2023-09-26T07:55:14","guid":{"rendered":"https:\/\/www.coala-h2020.eu\/?p=5559"},"modified":"2023-10-05T13:59:47","modified_gmt":"2023-10-05T13:59:47","slug":"coala-newsletter-5-sept-2023","status":"publish","type":"post","link":"https:\/\/www.coala-h2020.eu\/index.php\/2023\/09\/26\/coala-newsletter-5-sept-2023\/","title":{"rendered":"COALA Newsletter #5 – Sept 2023"},"content":{"rendered":"\n

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Welcome to COALA Newsletter September 2023<\/h2>\n\n\n\n
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Dear reader,

The fifth edition of the COALA newsletter presents two main enablers of the COALA solution: the prescriptive quality analytics component and the COALA augmented manufacturing analytics assistant. Both enablers have been integrated and tested in the white goods use case.

A short introduction of these enablers will be presented with an explanatory video to give you a quick overview of their potential implementation in an industrial use case.  Our end-users, who lead the white goods use case, share their feedback on the recent final evaluation of the COALA augmented manufacturing analytics assistant.

Related to activities involving the COALA community, our partners have been actively participating in and organising different events, from webinar series, podcasts, industrial workshops, showcase events and presentations at joint European projects events to providing contributions to the AI4EU community. Some new scientific publications have been presented at different conferences. You can find our latest open-access publications and community activities on our website.
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We hope that you find our newsletter interesting and useful. <\/p><\/div><\/div>\n<\/div><\/div>\n\n\n\n

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Karl Hribernik<\/strong><\/figcaption><\/figure>\n\n\n\n
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Stefan Wellsandt<\/strong><\/figcaption><\/figure>\n\n\n\n


Project Coordinator<\/strong>
BIBA – Bremer Institut f\u00fcr Produktion und Logistik GmbH<\/a><\/p><\/div><\/div>\n<\/div><\/div>\n<\/div><\/div><\/section>\n\n\n\n

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SOLUTION<\/h2>\n\n\n\n

Augmented Prescriptive Quality Analytics for Factory Workers<\/h2>\n\n\n\n
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Prescriptive Analytics for Quality Optimisation<\/h4>\n<\/div><\/div>\n\n\n\n
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The PREVENTION (Prescriptive Analytics for Quality Optimization) component addresses the prescriptive quality analytics needs of COALA. PREVENTION aims to build predictive quality models and prescribe mitigating actions to optimize quality-related manufacturing performance indicators. To achieve its aims, PREVENTION utilizes three types of analytics: descriptive, predictive, and prescriptive.

The component aims to cover the whole lifecycle of data analytics to derive insights on quality while enabling the development of several independent analytics processes with different scopes (descriptive, predictive, prescriptive) from multiple data sources and algorithms. Main features of COALA Prescriptive Quality Analytics Service:

– Exploratory Analytics, showing statistics for the quality of the production as a whole and its components, i.e. products and defects.
– Risk Assessment, including evaluation of the criticality of the defect concerning the products and parts affected.
– Predictive Quality, predictions about product defects and defects in the upcoming days.
– Quality Recommendations, providing recommendations for actions in quality control

The following demo video will show how the COALA PREVENTION component is implemented in the White Goods Production use case. If you have any questions related to this component, please feel free to contact us.<\/p><\/div><\/div>\n<\/div><\/div>\n\n\n\n

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Alexandros Bousdekis<\/strong>
Senior Research Scientist<\/strong>
Institute of Communication and Computer Systems (ICCS<\/a>)
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COALA Augmented Manufacturing Analytics Assistant<\/h4>\n<\/div><\/div>\n\n\n\n
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COALA Augmented Manufacturing Analytics (AMA) aims to support factory workers in performing time-consuming and information-intensive tasks and to enhance their operational efficiency.

The COALA AMA Assistant has been implemented and tested in the White Goods Production use case. The assistant enables operators to conduct quality control tests on products, ensuring the integrity of all product components. COALA incorporates a user-specific checklist tailored to the operator’s level of experience. Furthermore, COALA provides assistance in recording defect incidents during the quality control process.

Lab supervisors utilize COALA to access a range of analytical views, including prescriptive, descriptive, and predictive analytics pertaining to defect incidents. This empowers supervisors to make informed decisions and take preemptive actions.

Some key functions of the COALA Digital Intelligent Assistant are demonstrated in the video below. If you have any questions about the AMA assistant, please do not hesitate to contact us.<\/p><\/div><\/div>\n<\/div><\/div>\n\n\n\n

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Mina Foosherian<\/strong>
Research Scientist<\/strong>
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INTERVIEW<\/h2>\n\n\n\n

Human-AI collaboration in quality control of White Goods<\/h4>\n\n\n\n
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