Postural stability is an important measure for many medical diseases such as Parkinson. In the last years, research focused on using inexpensive and portable devices to measure postural stability, while the visual targets were physical objects in the environment. Sensing balancing boards were used to measure stance forces, while movements of the upper body were not taken into account. Within this paper, postural stability was measured using the HTC Vive. A variation of a virtual fixation point's distance was analyzed and compared to a reference condition with closed eyes. It is shown that body sway in the VR conditions is increased in the anteriorposterior and decreased in the medial-lateral direction.
Nickel-based super alloys are popular for applications in the energy and aerospace industries due to their excellent corrosion and high-temperature resistance. Direct metal deposition (DMD) of nickel alloys has reached technology readiness for several applications, especially for the repair of turbomachinery components. However, issues related to part quality and defect formation during the DMD process still persist. Laser remelting can effectively prevent and repair defects during metal additive manufacturing (AM); however, very few studies have focused on numerical modeling and experimental process parameter optimization in this context. Therefore, the aim of this study is to investigate the effect of determining the remelting process parameters via numerical simulation and experimental analyses in order to optimize an industrial process chain for part repair by DMD. A heat conduction model analyzed 360 different process conditions, and the predicted melt geometry was compared with observations from a fluid flow model and experimental single tracks for selected reference conditions. Subsequently, the remelting process was applied to a demonstrator repair case. The results show that the models can well predict the melt pool shape and that the optimized remelting process increases the bonding quality between base and DMD materials. Therefore, DMD part fabrication and repair processes can benefit from the remelting step developed here.
In metal additive manufacturing, moving heat sources cause spatial and time-dependent variations of temperature and strain that can lead to part distortions. Distortion prediction and optimized deposition parameters can increase the dimensional accuracy of the generated components. In this study, an analytical approach for modeling the effect of clad height and substrate thickness is experimentally validated. Additionally, the influence of the scanning pattern as a function of clad height and substrate thickness is determined experimentally. The analytical model is based on the cool-down phase mechanism and assumes the formation of constant thermal shrinking forces for each deposited layer. The model accurately predicts longitudinal cantilever distortion after experimental calibration when compared with similar experimental conditions. For multi-layer deposition, the scanning pattern has the largest influence on distortion for thin-walled substrates. An optimized deposition strategy with longitudinal scanning vectors leads to a distortion reduction of up to 86%. The results highlight the potential of mechanical modeling and scanning strategy optimizations to increase the shape accuracy for industrial applications in the field of additive manufacturing.
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