»Costs are more closely spaced than expected«

by Thomas Masuch — 2020/04/16

AM cost calculation tools help to identify components and select technology

The beauty of additive manufacturing is certainly the fact that it enables product innovations that engineers hadn't even thought of at the beginning of the development process. On the other hand, every company also has to think economically - that's where investment, material, part and production costs come in. For the metal sector, two new cost calculation tools now shed more light on additive budgeting. We have taken a look at both of them.

Ampower Cost Calculator

The cost calculator from the Hamburg-based consulting firm Ampower allows users to choose from four material families and seven production technologies for a specific component. »The tool is helpful to get an initial estimate of the internal component costs,« explains Matthias Schmidt-Lehr, founder and CEO of Ampower. »This is especially useful in the early phase of component development, for example when it is not yet clear in which manufacturing process a component is to be produced.«

Information such as component volume and dimensions, support material and the desired volume are included in the analysis. The respective unit costs for each technology can then be read off in a user-friendly diagram. For example, the tool shows how the component costs develop differently for higher unit numbers depending on the technologies. Even for an experienced expert like Matthias Schmidt-Lehr, the results revealed some surprises: »The costs are more closely spaced than we initially assumed. This is not only due to the printing processes themselves, but especially to the upstream and downstream process steps. For example, data preparation for otherwise very fast DED processes can be extremely time-consuming.«

Cost-Benefit-Tool

Even more complex and with more variables the »cost-benefit tool« of the CORNET project appears. In addition to production costs, the tool also takes into account possible benefits of AM components (e.g. electricity or fuel savings). Using numerous parameters such as machine type, material, component size or volume, the tool determines a value for the production costs over the entire machine running time. According to the developer, the entire product life cycle from product design, engineering and production to quality control and service/after sales is also taken into account. This means that users can not only have various options for post-processing, but can even individually factor in the distance between the components on the building plate, the wage for the machine operator or the electricity costs into the result. In addition, the »cost-benefit-tool« lists 29 different AM production machines from various manufacturers, which can have a considerable influence on component costs due to their purchase price, for example.

The »cost-benefit-tool« has been developed by researchers of the RWTH Aachen University as part of the CORNET project »AM 4 Industry«, which is led by the ecoplus Plastics and Mechatronics Cluster in Lower Austria. The cost-benefit tool should help to identify components for which additive manufacturing is economically viable. »The tool clearly shows how possible business cases can be identified through an early comparison of a cost-benefit analysis,« says Tobias Schröer, Head of Production Management at RWTH Aachen and co-developer of the model.  

CONCLUSION

Both tools offer a very good orientation for decision makers. The AM Power calculator, which allows price comparisons of different technologies and quantities quite quickly, shows the high practical orientation of the developers. The tool of the Cornet project, which can support investment decisions, for example, takes an even more detailed look at the production process. Significantly more variables can be entered, but this also requires greater process knowledge.

However, like any mathematical model, the calculators are only as good as the data they are fed with. Individual characteristics, short-term challenges and long-term learning effects can cause the figures to vary in practice. And in the end, economic conditions also determine how efficiently a machine is utilized.

Further Information: