A Cost Breakdown and Production Uncertainty Analysis of Additive Manufacturing : A Study of Low-Volume Components Produced with Selective Laser Melting

Sammanfattning: Background: Additive manufacturing has recently gained cogency as a final part manufacturing technique. The method uses a layer-upon-layer technique to build three-dimensional objects. This technique has many advantages creating new opportunities regarding production. Purpose: The purpose of the study is to investigate cost elements, cost drivers, their weight distribution, and to explore production uncertainties of the additive manufacturing process. The production uncertainty parameters of the explored uncertainties are then evaluated to investigate how some of them impacts the production cost of the case component. Method: The following study have used qualitative data collection methods in terms of interviews together with a pre-study and a sensitivity analysis tool to identify cost impacts of uncertainty parameters. Five primary interviews were performed with employees at the company with relevant knowledge of the studied field. Results: The result shows that the product cost can be divided into two categories of material cost and manufacturing costs, these two categories then have different cost elements that drives cost. The explored uncertainties of the process consist of both aleatory and epistemic uncertainty. The explored production uncertainty parameter that affects the final product cost the most is the time needed to finish the AM build. Conclusions: Considering production uncertainty is important in order to have reliable and accurate cost estimations. The three explored production uncertainties that have the most significant impact on the final product cost is the yearly machine running time, the SLM machine time needed to finish the component, and reduced manning time in the operations. These three uncertainty parameters should, therefore, have a larger focus than variables that do not have the same impact on the final product cost, to create better cost estimations.