Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "product data" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Identifying part commonalities in a manufacturing company database
Autorzy:
Kwapisz, J.
Infante, V.
Powiązania:
https://bibliotekanauki.pl/articles/94923.pdf
Data publikacji:
2016
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
commonality
data mining
industrial database analysis
part reuse
product platform
product family
Opis:
Manufacturing companies that produce and assemble multiple products rely on databases containing thousands or even millions of parts. These databases are expensive to support, maintain and the inherent complexity does not allow end users to utilize fully such databases. Designers and engineers are often not able to find previously created parts, which they could potentially reuse, and they add one more part to the database. Engineered improvements without removal of the previous version of the component also cause the avoidable increase of elements in the database. Reuse of parts or planned development of common parts across products brings many benefits for manufacturers. Search algorithm utilized across part databases and varying projects allows identifying similar parts. The goal is to compare part names and attributes resulting in the assignment of a similarity score. Determining common and differentiating part attributes and characteristics between pairs of components allows nominating parts that can become shared in different products. The case study utilizes an industrial example to evaluate and assess the feasibility of the proposed method for identifying commonality opportunities. It turned out that it is possible to find many parts that can be potentially shared between different products.
Źródło:
Information Systems in Management; 2016, 5, 3; 336-346
2084-5537
2544-1728
Pojawia się w:
Information Systems in Management
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fundamentals of a recommendation system for the aluminum extrusion process based on data-driven modeling
Autorzy:
Perzyk, Marcin
Kochański, Andrzej
Kozłowski, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/29520062.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
aluminum extrusion
advisory system
product defect
data mining
neural networks
system doradczy
wada produktu
eksploracja danych
sieci neuronowe
Opis:
The aluminum profile extrusion process is briefly characterized in the paper, together with the presentation of historical, automatically recorded data. The initial selection of the important, widely understood, process parameters was made using statistical methods such as correlation analysis for continuous and categorical (discrete) variables and ‘inverse’ ANOVA and Kruskal–Wallis methods. These selected process variables were used as inputs for MLP-type neural models with two main product defects as the numerical outputs with values 0 and 1. A multi-variant development program was applied for the neural networks and the best neural models were utilized for finding the characteristic influence of the process parameters on the product quality. The final result of the research is the basis of a recommendation system for the significant process parameters that uses a combination of information from previous cases and neural models.
Źródło:
Computer Methods in Materials Science; 2022, 22, 4; 173-188
2720-4081
2720-3948
Pojawia się w:
Computer Methods in Materials Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies