{"id":42617,"date":"2024-03-26T09:23:23","date_gmt":"2024-03-26T08:23:23","guid":{"rendered":"https:\/\/xitaso.com\/projects\/wissensbasiertes_machinelearning_zur_anomaliebehebung\/"},"modified":"2026-06-02T14:35:02","modified_gmt":"2026-06-02T12:35:02","slug":"adeles","status":"publish","type":"page","link":"https:\/\/xitaso.com\/en\/projects\/adeles\/","title":{"rendered":"ADELeS: Remedying anomalies via neuro-symbolic learning"},"content":{"rendered":"

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The challenge<\/h2>\n

Many everyday products are manufactured using complex production systems that require experienced specialist staff to operate. Due to the shortage of skilled workers and demographic change, it is becoming increasingly difficult to maintain stable and efficient production. In order to still be able to produce competitively, production facilities must also be able to be operated by inexperienced employees. One key to this can be to expand production machines and systems with intelligent assistance systems in order to reduce training times and increase employee satisfaction and overall productivity.[\/vc_column_text][vc_empty_space height=”30″][vc_empty_space height=”30px” el_class=”not-on-mobile”][\/vc_column_inner][vc_column_inner width=”1\/2″][vc_empty_space height=”30″ el_class=”not-on-mobile”][vc_empty_space height=”30″][vc_column_text css=””]<\/p>\n

How we helped<\/h2>\n

In ADELeS (anomaly correction through extracted expert knowledge and learning systems), we are developing an AI-based assistance system to detect and correct quality deviations and errors during production. The quality assurance process combines learning systems with expert knowledge via neuro-symbolic learning. The latter is extracted based\u00a0on experience and data.[\/vc_column_text][vc_empty_space height=”30″][vc_empty_space height=”30″][\/vc_column_inner][\/vc_row_inner][vc_row_inner row_type=”row” type=”grid” text_align=”left” css_animation=””][vc_column_inner][vc_column_text]<\/p>\n

Technologies<\/h2>\n

[\/vc_column_text][vc_empty_space height=”15″][vc_raw_html]JTNDc3BhbiUyMGNsYXNzJTNEJTIydGVjaG5vbG9naWVuJTIyJTNFTWFjaGluZSUyMExlYXJuaW5nJTNDJTJGc3BhbiUzRSUwQSUzQ3NwYW4lMjBjbGFzcyUzRCUyMnRlY2hub2xvZ2llbiUyMiUzRU5ldXJvLVN5bWJvbGljJTIwTGVhcm5pbmclM0MlMkZzcGFuJTNFJTBBJTNDc3BhbiUyMGNsYXNzJTNEJTIydGVjaG5vbG9naWVuJTIyJTNFQUktYmFzZWQlMjBhc3Npc3RhbmNlJTIwc3lzdGVtJTNDJTJGc3BhbiUzRSUwQSUzQ3NwYW4lMjBjbGFzcyUzRCUyMnRlY2hub2xvZ2llbiUyMiUzRUluZHVzdHJ5JTIwNC4wJTNDJTJGc3BhbiUzRSUwQSUzQ3NwYW4lMjBjbGFzcyUzRCUyMnRlY2hub2xvZ2llbiUyMiUzRUtub3dsZWRnZS1JbmZ1c2VkJTIwTGVhcm5pbmclM0MlMkZzcGFuJTNF[\/vc_raw_html][vc_empty_space height=”60″][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”grid” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=””][vc_column][vc_empty_space height=”30″ el_class=”not-on-mobile”][vc_empty_space height=”50″][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”full_width” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” el_class=”two-column-with-heading-on-top” z_index=””][vc_column][vc_row_inner row_type=”row” type=”grid” text_align=”left” css_animation=””][vc_column_inner][vc_column_text]<\/p>\n

The research project in detail<\/h2>\n

[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner row_type=”row” type=”grid” text_align=”left” css_animation=””][vc_column_inner width=”1\/2″][vc_column_text]Anomaly mitigation\u00a0through extracted expert knowledge and learning systems<\/strong><\/p>\n

In research project ADELeS, the University of Augsburg, the Friedrich-Alexander University Erlangen Nuremberg and the companies REHAU Industries SE & Co. KG and XITASO GmbH have joined forces to tackle these challenges in the context of\u00a0an extrusion process for the extrusion of edgebanding, which are used, for example, in furniture production.<\/p>\n

REHAU uses 40-60m long extrusion lines for the production of edgeband, which consist of a large number of individual machines for separate processes (e.g. extruder, cooling, printing units).<\/p>\n

In order to predict the expected product quality on the one hand (predictive quality) and to suggest parameter adjustments to the operators of the extrusion line to mitigate<\/span>\u00a0<\/span>quality anomalies on the other, the research approach provides for the combination of expert knowledge with machine learning methods based on time series data from the extrusion line.<\/p>\n

The assistance systems that presents these suggestions is<\/span>\u00a0triggered by automatic detection of quality anomalies.[\/vc_column_text][vc_empty_space height=”30″ el_class=”only-on-mobile”][\/vc_column_inner][vc_column_inner width=”1\/2″][vc_single_image image=”45812″ img_size=”full” css=”” qode_css_animation=””][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”grid” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=””][vc_column][vc_empty_space height=”50″][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”full_width” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” el_class=”two-column-with-heading-on-top” z_index=””][vc_column][vc_row_inner row_type=”row” type=”grid” text_align=”left” css_animation=””][vc_column_inner width=”1\/2″][vc_column_text]Expert knowledge is captured, formalized and quantified through experience- and data-based knowledge extraction. The result is used in both explainable learning classifier systems and neuro-symbolic approaches.[\/vc_column_text][vc_empty_space height=”20px” el_class=”not-on-mobile”][vc_single_image image=”42258″ img_size=”200×200″ alignment=”center” qode_css_animation=””][vc_empty_space height=”30px” el_class=”not-on-mobile”][\/vc_column_inner][vc_column_inner width=”1\/2″][vc_column_text]The focus of XITASO is on the further development of the innovative methodology for data-based knowledge extraction developed in the predecessor project AIPE, the representation of this procedural knowledge in knowledge graphs, their representation in vectors, and the neuro-symbolic combination of expert<\/span>\u00a0<\/span>knowledge and neural networks.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”grid” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=””][vc_column][vc_empty_space height=”30″ el_class=”not-on-mobile”][vc_empty_space height=”50″][vc_column_text css=””]<\/p>\n

Publications on the research project<\/h2>\n

[\/vc_column_text][vc_empty_space height=”35″][\/vc_column][vc_column][vc_row_inner row_type=”row” type=”full_width” text_align=”left” css_animation=””][vc_column_inner width=”1\/6″][vc_empty_space height=”50″][\/vc_column_inner][vc_column_inner width=”2\/3″][vc_raw_html css=””]JTVCZG9jX2xpYnJhcnklMjBzb3J0X2J5JTNEJTIyZGF0ZSUyMiUyMGRvY190YWclM0QlMjJBREVMZVMlMjIlMjBkb2NfY2F0ZWdvcnklM0QlMjJwdWJsaWNhdGlvbiUyMiUyMGNvbHVtbnMlM0QlMjJ0aXRsZSUyQ2RvY190YWdzJTJDZGF0ZSUyMiUyMGRhdGVfZm9ybWF0JTNEJTIyWSUyMiU1RA==[\/vc_raw_html][\/vc_column_inner][vc_column_inner width=”1\/6″][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”grid” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=””][vc_column][vc_empty_space height=”30″ el_class=”not-on-mobile”][vc_empty_space height=”50″][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”full_width” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=”” el_class=”row-image-mobile-full-width”][vc_column][vc_empty_space height=”40″][vc_row_inner row_type=”row” type=”grid” text_align=”left” css_animation=””][vc_column_inner][vc_column_text]<\/p>\n

Project participants and funding<\/h2>\n

[\/vc_column_text][vc_empty_space height=”30″][\/vc_column_inner][\/vc_row_inner][vc_row_inner row_type=”row” type=”grid” text_align=”left” css_animation=””][vc_column_inner][vc_column_text]<\/p>\n

Project partners<\/h3>\n

[\/vc_column_text][vc_empty_space height=”15″][\/vc_column_inner][\/vc_row_inner][vc_row_inner row_type=”row” type=”grid” text_align=”left” css_animation=””][vc_column_inner width=”1\/3″][vc_empty_space height=”10″ el_class=”not-on-mobile”][vc_single_image image=”42956″ img_size=”medium” onclick=”custom_link” img_link_target=”_blank” css=”” qode_css_animation=”” link=”https:\/\/www.uni-augsburg.de\/en\/fakultaet\/fai\/informatik\/prof\/oc\/research\/ongoing-research\/”][vc_empty_space height=”30″][\/vc_column_inner][vc_column_inner width=”1\/3″][vc_empty_space height=”10″ el_class=”not-on-mobile”][vc_single_image image=”42959″ img_size=”medium” onclick=”custom_link” img_link_target=”_blank” css=”” qode_css_animation=”” link=”https:\/\/wi2.fau.de\/research\/nlp\/adeles\/”][vc_empty_space height=”30″][\/vc_column_inner][vc_column_inner width=”1\/3″][vc_empty_space height=”10″ el_class=”not-on-mobile”][vc_single_image image=”42842″ img_size=”medium” onclick=”custom_link” img_link_target=”_blank” css=”” qode_css_animation=”” link=”https:\/\/www.rehau.com\/uk-en\/”][vc_empty_space height=”30″][\/vc_column_inner][\/vc_row_inner][vc_empty_space height=”10″ el_class=”not-on-mobile”][vc_empty_space height=”40″][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”full_width” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=”” el_class=”row-image-mobile-full-width”][vc_column][vc_row_inner row_type=”row” type=”grid” text_align=”left” css_animation=””][vc_column_inner width=”1\/3″][vc_column_text]<\/p>\n

Project sponsor<\/h3>\n

[\/vc_column_text][vc_empty_space height=”15″][vc_column_text]Bavarian Ministry of Economic Affairs, Regional Development and Energy[\/vc_column_text][vc_empty_space height=”15″ el_class=”not-on-mobile”][vc_single_image image=”40722″ img_size=”full” qode_css_animation=””][vc_empty_space height=”15″ el_class=”only-on-mobile”][\/vc_column_inner][vc_column_inner width=”1\/3″][vc_column_text]<\/p>\n

Funding program<\/h3>\n

[\/vc_column_text][vc_empty_space height=”15″][vc_column_text]Bayerisches Verbundf\u00f6rderprogramm (BayVFP)
\n\u2013 Digitisation funding line \u2013
\nInformation and communication technology funding programme[\/vc_column_text][vc_empty_space height=”15″ el_class=”only-on-mobile”][\/vc_column_inner][vc_column_inner width=”1\/3″][vc_column_text]<\/p>\n

Project management<\/h3>\n

[\/vc_column_text][vc_empty_space height=”15″][vc_column_text]VDI\/VDE Innovation + Technik GmbH
\nKI-Produktionsnetzwerk Augsburg[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_empty_space height=”60″][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”grid” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=””][vc_column][vc_empty_space height=”40″ el_class=”not-on-mobile”][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”grid” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=”” css=”.vc_custom_1720431560707{background-color: #e8e0e0 !important;}”][vc_column][vc_empty_space height=”40″][vc_column_text]<\/p>\n

Interested in helping with our research projects?<\/h2>\n

[\/vc_column_text][vc_empty_space height=”15″][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”grid” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=”” css=”.vc_custom_1720431573373{background-color: #e8e0e0 !important;}”][vc_column width=”1\/6″][\/vc_column][vc_column width=”2\/3″][vc_column_text]With research and innovation projects, we explore the potential of tomorrow\u2019s technologies. For this purpose, we are always looking for committed colleagues who would like to continue and shape the XITASO path with us.[\/vc_column_text][vc_empty_space height=”15″][vc_column_text]<\/p>\n

Show all job offers<\/a><\/p>\n

[\/vc_column_text][vc_empty_space height=”40″][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”grid” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=””][vc_column][vc_empty_space height=”30″ el_class=”not-on-mobile”][vc_empty_space height=”50″ el_class=”not-on-mobile”][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”grid” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=””][vc_column][vc_column_text]<\/p>\n

Further projects<\/h2>\n

[\/vc_column_text][vc_empty_space height=”40″][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”full_width” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=”” el_class=”three-column-references”][vc_column][vc_row_inner row_type=”row” type=”grid” text_align=”left” css_animation=””][vc_column_inner width=”1\/3″][vc_raw_html css=””]JTNDYSUyMGhyZWYlM0QlMjIlMkZlbiUyRnByb2plY3RzJTJGa2lzcHAtaW50ZWxsaWdlbnQtc2NoZWR1bGluZy1vZi1udXJzaW5nLXN0YWZmJTJGJTIyJTNFJTBB[\/vc_raw_html][vc_empty_space height=”250″ image_repeat=”no-repeat” background_image=”38818″][vc_column_text css=””]<\/p>\n

Research Project KISPP<\/div>\n

Intelligent scheduling of nursing staff<\/h3>\n

[\/vc_column_text][vc_raw_html]JTNDJTJGYSUzRQ==[\/vc_raw_html][\/vc_column_inner][vc_column_inner width=”1\/3″][vc_raw_html]JTNDYSUyMGhyZWYlM0QlMjIlMkZlbiUyRnByb2plY3RzJTJGdmFsaXNlbnMtZnVzaW5nLXNlbnNvci1kYXRhLWZvci1hdXRvbm9tb3VzLWRyaXZpbmclMkYlMjIlM0U=[\/vc_raw_html][vc_empty_space height=”250″ image_repeat=”no-repeat” background_image=”40759″][vc_column_text css=””]<\/p>\n

Research project VALISENS<\/div>\n

Fusing sensor data for autonomous driving<\/h3>\n

[\/vc_column_text][vc_raw_html]JTNDJTJGYSUzRQ==[\/vc_raw_html][\/vc_column_inner][vc_column_inner width=”1\/3″][vc_raw_html]JTNDYSUyMGhyZWYlM0QlMjIlMkZlbiUyRnByb2plY3RzJTJGYnJ6LWFpLWNhbGN1bGF0aW9uLWFzc2lzdGFudCUyRiUyMiUzRQ==[\/vc_raw_html][vc_empty_space height=”250″ image_repeat=”no-repeat” background_image=”17675″][vc_column_text]<\/p>\n

BRZ<\/div>\n

AI-supported construction calculation assistant<\/h3>\n

[\/vc_column_text][vc_raw_html]JTNDJTJGYSUzRQ==[\/vc_raw_html][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”grid” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=””][vc_column][vc_empty_space height=”30″][vc_column_text]<\/p>\n

View all projects<\/a><\/p>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”grid” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=””][vc_column][vc_empty_space height=”50″ el_class=”not-on-mobile”][vc_empty_space height=”50″][\/vc_column][\/vc_row][vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”full_width” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern” z_index=”” el_class=”your-contact-person” css=”.vc_custom_1720431431942{background-color: #e8e0e0 !important;}”][vc_column][vc_column_text]<\/p>\n

Are you interested in a project, a service or do you have any other question?<\/h2>\n

[\/vc_column_text][vc_row_inner row_type=”row” type=”grid” text_align=”left” css_animation=””][vc_column_inner][vc_single_image image=”89725″ img_size=”120×120″ alignment=”center” style=”vc_box_circle_2″ css=”” qode_css_animation=””][vc_column_text css=””]<\/p>\n

Dr. Richard Nordsieck<\/b><\/p>\n

Tel. +49 821 885 882 89
\nrichard.nordsieck@xitaso.com<\/a><\/p>\n

[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

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