Skip to main content

10.04.2018 | Manufacturing | News | Onlineartikel

Efficient Use of Resources for Manufacturing Metal Components

Nadine Klein

Even marginal reductions in the material and resources used per component yield high cost savings in series production of lightweight components made of aluminium.

The 3D-Printing Lab for Metals and Structural Materials at Fraunhofer Institute for High-speed Dynamics, Ernst-Mach-Institute, EMI, in Freiburg, Germany, houses one of the largest commercially available 3D printers for metal currently in existence. Metal structures with dimensions of up to 40 centimetres can be additively manufactured using the selective laser melting technique. 3D printing offers completely new ways of designing components with highly complex shapes while optimising their weight. However, Fraunhofer researchers advocate that it is only by combining additive manufacturing and intelligent lightweight design that maximum resource efficiency in manufacturing can be achieved. Researchers in the 3D-Printing Lab have been investigating just how resource-efficient the manufacturing process is when manufacturing lightweight aluminium components based on the example of a wheel carrier. The focus was on energy and material consumption, manufacturing time and CO2 emissions that arise during a small production run of twelve wheel carriers.

Reducing manufacturing costs for small-scale series production

The researchers used the numerical finite element method (FEM) to construct a wheel carrier with an optimised lightweight design that is adapted for the defined load scenarios and offers maximum performance. Due to their geometric complexity, structures produced in this way cannot be manufactured by conventional methods such as milling or turning. Using the numerically optimised version of the wheel carrier, fifteen percent less energy was required for the additive process than for the conventional design. Twelve kilowatt hours of electricity were needed for the conventional design compared to ten kilowatt hours for the numerically optimised design (in each case, the measured value refers to a series-manufactured component). Manufacturing time was cut by 14 percent and CO2 emissions by 19 percent, while material consumption was reduced even more significantly, by 28 percent.

The results of the small-scale series production of the wheel carrier suggest that additive manufacturing can also be useful when a component does not have to be structurally optimised as such. "A heat exchanger and a tool mould, for example, do not have to be lightweight to improve their functionality. Nevertheless, it makes sense to design them with reduced weight and volume when manufacturing them additively, because this way you can bring down manufacturing costs," explains Klaus Hoschke, scientist and group leader at Fraunhofer EMI.


Weiterführende Themen

Die Hintergründe zu diesem Inhalt

Das könnte Sie auch interessieren

20.03.2018 | Manufacturing | News | Onlineartikel

Making Tools in Zero Gravity

Premium Partner

in-adhesivesMKVSNeuer Inhalt

BranchenIndex Online

Die B2B-Firmensuche für Industrie und Wirtschaft: Kostenfrei in Firmenprofilen nach Lieferanten, Herstellern, Dienstleistern und Händlern recherchieren.



Künstliche Intelligenz und die Potenziale des maschinellen Lernens für die Industrie

Maschinelles Lernen ist die Schlüsseltechnologie für intelligente Systeme. Besonders erfolgreich ist in den letzten Jahren das Lernen tiefer Modelle aus großen Datenmengen – „Deep Learning“. Mit dem Internet der Dinge rollt die nächste, noch größere Datenwelle auf uns zu. Hier bietet die Künstliche Intelligenz besondere Chancen für die deutsche Industrie, wenn sie schnell genug in die Digitalisierung einsteigt.
Jetzt gratis downloaden!


Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.