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One major goal of production system design is to decide on space requirements in the plant. In this phase, detailed information about the supply chain network (e.g. which suppliers deliver which parts) is often unavailable. Nevertheless, typical decisions in supply chain management, like the definition of replenishment processes and quantities or make-or-buy decisions, impact space requirements and the capital investment in raw materials. Therefore, it should be considered in factory and production planning. This research article proposes a simulation approach for a production simulation in which raw material replenishment is integrated to evaluate the capital investment and space requirements for raw materials.
A new approach to coat PA12 powders with laser-generated nanoparticles for selective laser sintering
(2018)
The modification of selective laser sintering (SLS) powder materials by nanoadditives offers the possibility to adapt the powder properties to the laser sintering process or the resulting part properties. To avoid agglomeration of the nanofiller, a new approach in which surfactant-free laser-generated colloidal nanoparticles are adsorbed onto the polymer surface directly in an aqueous solution is demonstrated. Based on this novel approach, polyamide 12 (PA12) powders are decorated with metal and oxide nanoparticles and processed via SLS. Electron microscopy and confocal laser scanning imaging are utilized to analyze the dispersion of the filler.
In modern manufacturing systems, milk run (MR) systems represent route-based, cyclic material-handling systems that are used widely and enable frequent deliveries of containerized parts in small lot sizes from a central storage area to multiple points of use on the factory floor. Usually, deliveries take place at predetermined, constant time intervals. As in real-world MR systems, the number of containers to deliver in each interval varies in the short term, peaks in the number of deliveries may lead to capacity bottlenecks and late deliveries. In this paper, we develop three alternative strategies to determine starting times of MR cycles in real-time based on the current number of delivery orders in the system aiming to better handle variations in the number of orders. We evaluate the three strategies for different system load scenarios with respect to delivery service levels, order lead times, systems utilization and necessary number of MR trains using a discrete-event simulation model.