This thesis proposes a data-driven approach for the analysis and synthesis of product characteristics, drawing upon the WEBER model of product characteristics and properties. In the context of change and adaptation design, product properties and characteristics are conceptualized as variables within a machine learning model. The generation of a machine learning model follows a systematic sequence of steps, beginning with data preparation, modeling, evaluation, and optimization, as developed from the CRISP-DM. This systematic approach enables the application of data-driven analysis and synthesis. Ensuring consistent data management throughout the entire modeling process is imperative. Tothisend, aconcept fordata-drivenanalysisand synthesis has been developed, which is presented in the context of the required data and categorized accordingly. The objective of this work is to enhance three areas: metamodeling, solution space generation, and data management. The integration of metamodeling with global and local component properties through deep learning (data-driven analysis) facilitates the generation of solution spaces for novel component designs in data-driven synthesis. The management of data and the integration of semantics through metadata are integral components of the respective modeling processes. In summary, it can be concluded that deep learning in particular facilitates the generation of novel metamodels that enable, among other capabilities, a spatially resolved description with sufficient accuracy. Furthermore, the presented approaches demonstrate novel methodologies for leveraging solution spaces to expedite the generation of applicable component designs. The principles and methodologies outlined in this thesis are directly applicable to the development process.<\/p>","protected":false},"excerpt":{"rendered":"
In der vorliegenden Arbeit wird ein datengetriebenes Vorgehen f\u00fcr die Analyse und Synthese von Produkteigenschaften entwickelt, das auf dem Modell der Produktmerkmale und -eigenschaften von WEBER basiert. Im Kontext der Ver\u00e4nderungs- und Anpassungsgestaltung werden die Produktmerkmale und -eigenschaften als Variablen innerhalb eines Machine Learning Modells betrachtet. Die Erzeugung eines Machine Learning Modells erfolgt anhand der […]<\/p>\n","protected":false},"featured_media":6241,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false},"product_brand":[],"product_cat":[34],"product_tag":[],"class_list":{"0":"post-6240","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-fau-studien-aus-dem-maschinenbau","7":"has-post-title","8":"has-post-date","9":"has-post-category","10":"has-post-tag","11":"has-post-comment","12":"has-post-author","13":"","15":"first","16":"instock","17":"taxable","18":"shipping-taxable","19":"purchasable","20":"product-type-simple"},"acf":{"autor":"Sauer, Christopher","orcid":"0000-0002-8750-9955","ror":"00f7hpc57","herausgeber":"","orcid_hrsg":"","rorhrsg":"","bearbeiter":"","sonstige_beteiligte_personen":"","untertitel":"","zusatz":"","jahr":"2025","zeitschrift":"","serie":"","serie_name":false,"serie_band":"","serie_jahr":"","serie_titel_band":"","vortrage":"","issn":"","isbn":"","isbn13":"978-3-96147-854-5","seiten":"xiv, 165","sonstiges":"","band":270,"auflage":"","opus":"","dspace":"38019","e-isbn_pdf":"978-3-96147-855-2","doi":"https:\/\/doi.org\/10.25593\/978-3-96147-855-2","epub":"","e-isbn_epub":"","doi_epub":"","html":"","e-isbn_html":"","doi_html":"","e-isbn_reader":"","doi_reader":"","digibib":"","urn":"urn:nbn:de:101:1-2511280405468.493134360765","supplementary_material":"","sprache":["Deutsch"],"ausgabe":"Softcover","suche":" 9783961478545 9783961478552 ","lizenz":"BY-NC-4","bemerkung":"1\tFAU Press\r\n1\tHB\r\n4\tBSB \/ DNB\r\n1\tTNZB\r\n1\tTIB\r\n"},"builder_content":"","_links":{"self":[{"href":"https:\/\/www.university-press.fau.de\/en\/wp-json\/wp\/v2\/product\/6240","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.university-press.fau.de\/en\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/www.university-press.fau.de\/en\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/www.university-press.fau.de\/en\/wp-json\/wp\/v2\/comments?post=6240"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.university-press.fau.de\/en\/wp-json\/wp\/v2\/media\/6241"}],"wp:attachment":[{"href":"https:\/\/www.university-press.fau.de\/en\/wp-json\/wp\/v2\/media?parent=6240"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/www.university-press.fau.de\/en\/wp-json\/wp\/v2\/product_brand?post=6240"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/www.university-press.fau.de\/en\/wp-json\/wp\/v2\/product_cat?post=6240"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/www.university-press.fau.de\/en\/wp-json\/wp\/v2\/product_tag?post=6240"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}