CREMA | Cloud-based Rapid Elastic MAnufacturing
01-01-2015
-31-12-2017
01-01-2015
-31-12-2017
01-12-2014
-30-11-2018
11-01-2015
-31-10-2018
11-01-2015
-31-12-2018
09-01-2015
-31-08-2018
10-01-2015
-30-09-2018
01-09-2016
-31-08-2019
01-10-2016
-30-09-2019
01-10-2016
-30-09-2019
01-05-2017
-31-10-2020
01-11-2015
-31-10-2017
01-09-2017
-28-02-2021
UPTIME will reframe predictive maintenance strategy in a systematic and unified way with the aim to fully exploit the advancements in ICT and maintenance management by examining the potential of big data in an e-maintenance infrastructure taking into account the Gartner’s four levels of data analytics maturity and the
proactive computing principles.
UPTIME will enable manufacturing companies to reach Gartner's four levels of data analytics maturity for optimised decision making - each one building on the previous one: Monitor, Diagnose and Control, Manage, Optimize - aims to optimise in-service efficiency and contribute to increased accident mitigation capability by avoiding crucial breakdowns with significant consequences. UPTIME Components UPTIME_DETECT & UPTIME_PREDICT and UPTIME_ANALYZE aim to enhance the methodology framework for data processing and analytics. The key role for the UPTIME_DETECT and UPTIME_PREDICT components are data scientists who are in charge of developing, testing and deploying algorithmic calculations on data streams. In this way, the component is able to to identify the current condition of technical equipment and to give predictions. On the other hand, the UPTIME_ANALYZE is a data analytics engine driven by the need to leverage manufacturers’ legacy data and operational data related to maintenance, and to extract and correlate relevant knowledge.
UPTIME_ANALYZE is a data analytics engine driven by the need to leverage manufacturers’ legacy data and operational data related to maintenance, as well as to extract and correlate relevant knowledge. The data mining and analytics of ANALYZE component practically delivers the intelligence of the ANALYZE component by defining, training, executing and experimenting with different machine learning algorithms.
01-11-2017
-28-02-2021
01-01-2019
-31-07-2022
An AI vision algorithm developed by TNO (WP3) seems to filter bad rated parts compared to installed algorithm. Advantage can be when product print is changing to catch-up development speed in traditional algorithm development.
For this trial, the acquired test data will be analyzed regarding quality classification. In every test a part could pass or fail. Failed parts must be reworked, if possible, and brought back to the process. Sometimes parts are classified as failed even if they are good (false positive). This effect will be analyzed by machine learning algorithms and, if necessary, adopted in classification parameterisation. Additionally, the fact of 100% testing, means every panel is tested automatically, with bottleneck in out of the line test stations will be addressed in setting up failure prediction models for quality forecast. This will be supported by data analysis of pre reflow AOI (automated optical inspection).
With all these data analysis and process optimization activities economical evaluation will be included to support decisions in-process and configuration changes. For the development of these applications, the main steps are data availability/access, data processing, and model development. The developed applications should be deployed on Edge devices.
01-11-2020
-31-10-2023
01-10-2020
-30-09-2023
01-10-2020
-30-09-2023
01-11-2020
-31-10-2023
01-10-2020
-31-03-2024
01-10-2022
-30-09-2026