Abstract
Due to resolving major technological challenges Additive Manufacturing (AM) is on the brink of industrialization. In order to operate capital-intensive AM equipment in an economically viable manner, service providers must configure their production environment in a way which enables high capacity utilization and short throughput times while minimizing work in process. The interrelation of those three mentioned production-related key performance indicators, also known as the scheduling dilemma, must be addressed with due consideration of the technology’s characteristics. Within the framework of this paper the authors describe the impact of a service provider’s facility configuration regarding machine pool, operator availability and distribution of work content on the production system’s utilization. The evaluations rely on a simulation model developed in Matlab®, which allows for modification and execution of production schedules within AM facilities of different configurations. The validation of the proposed model is based on empirical data gathered on the shopfloor of GKN Additive, a global AM service provider.
Highlights
Today’s rapidly evolving competitive landscape, characterized by shortened innovation cycles and shortened, steeper value chains [1], exerts pressure on Additive Manufacturing (AM) job-shops and OEMs operating capitalintensive AM equipment
The validation of the proposed approach is carried out by modeling the input parameters according to the setting found at GKN Additive’s shopfloor and comparing the simulation results with the actual performance indicators derived from the provided datasets
Assuming that the described model is of sufficient validity for quantifying the performance of a Laser Powder Bed Fusion (LPBF) production setting, the results created by running the simulation with varying input parameters may be utilized in domains such as production planning & scheduling, data preparation, shift planning and capacity dimensioning
Summary
Today’s rapidly evolving competitive landscape, characterized by shortened innovation cycles and shortened, steeper value chains [1], exerts pressure on Additive Manufacturing (AM) job-shops and OEMs operating capitalintensive AM equipment. Despite offering a technology lead, AM job-shops may create a differentiator and competitive advantage by a clear commitment to operational excellence, positively contributing to decreased throughput times, reduced work in process (WIP) and increased machine utilization. The quantification of those Key Perfomance Indicators (KPIs) is influenced by input variables, i.e. shopfloor configuration, such as machine availability, operator presence, distribution of work content and production order release rate. While the situation described above typically applies to one-of-a kind manufacturing, LPBF manufacturing lots are comprised of a set of highly heterogenous part making the deduction of robust default production times a challenging task
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