International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The paradigm of Industry 4.0 pushes additive manufacturing (AM) from rapid prototyping towards the position of series production. Especially in metal 3D printing, increased attention is being paid to the topics of sustainability and resource efficiency. Energy demand during production and the calculation of building times play a decisive role here. Science has developed models for calculating energy consumption based on analytical and empirical approaches. Building time calculators have been introduced using a wide variety of analytical, analogical and parametric approaches. The present review summarizes the results and the state of the art, illustrates the results graphically and thus paves the way for further research approaches. The specific energy consumption per kilogram of processed material has risen over the last decades, which can be explained by higher technical requirements for production machines. Building time calculations continue to be subject to errors, depending on the type of calculation. The introduction of machine learning approaches has the potential to reduce this discrepancy.

Additive manufacturing is an established production method to realize Direct Digital Manufacturing in Industry 4.0. Especially for metal components, production requires high investment sums and high levels of know-how in the organisation. To make the advantages of the technology accessible even without high initial investment costs, co-called online hubs became an external and decentralised alternative to additive in-house production. After uploading the geometry to the online portals, material and post processing can be selected. The hub gives the customer a direct pricing response which is one of the main economic indicators for a purchase decision. The present paper focuses on the influence of the order quantity and the complexity of the components on the price algorithm. Therefore, sample parts of varying complexity and sizes are developed and uploaded to analyse data. Based on the in-depth findings of the study, the results are discussed.