Andreas Bardenhagen

The traditional sintering of metallic components shaped via Material Extrusion Additive Manufacturing (MEAM) is a time-consuming process that involves sophisticated energy-intensive heating systems. This work describes a novel induction heater capable of efficiently tailoring temperature profiles to densify MEAM powder compacts. In situ sintering within the same device is achieved indirectly by heating a graphite crucible, whereby the heater is based on an inverter with a half-bridge topology using the Zero-Voltage Switching (ZVS) technique. The system comprises a bank of capacitors that, in conjunction with a work coil, form a parallel-topology resonant circuit. This design allows the inverter to be used as a current amplifier, thereby increasing its efficiency to deliver an output power of up to 5 kW. The device operates at a 62.86 kHz resonant frequency, achieving a 2.01 mm penetration depth and a 1365.7 °C crucible temperature with only 1.313 kW of consumption, providing an increase in efficiency compared to other low-cost systems. Equipped with a feedback circuit, it offers five distinct control techniques that enable the self-tuning of the crucible temperature. The results indicate that the Cohen–Coon tuning method is more robust compared to the Ziegler–Nichols, damped, no overshoot, and mixed techniques. Sintering with this novel induction heater provides an alternative method for reducing the processing times for MEAM geometries, paving the way for increased efficiency and reduced energy consumption. Circuit diagrams, simulations, and experimental data on the temperature, time, and output voltage are provided in this article.

The search for a rational design subspace of aircraft configurations can be addressed by using the advanced morphological approach (AMA). It allows the decomposition of design problems into functional and/or characteristic attributes and their technological implementation options. These are systemized in a morphological matrix (MM). Based on expert evaluations of each option, an exhaustive space is generated containing option combinations as possible solutions. Therefore, extensive MMs lead to immense solution spaces that are hard to analyze. However, removing attributes without justification might leave potential rational solutions out of scope. In this context, a sensitivity analysis technique is suggested based on analysis of variance (ANOVA) tests for use with the AMA. It can be applied to assess the sensitivity of solution scores against the attribute options and their importance. As a result, optimization of the MM and solution space can be achieved and improvement proposals can be drawn for future problem statements with the AMA. This is demonstrated on the data from a previously conducted AMA workshop on the conceptual design of a search and rescue aircraft.