Progress in applied research for sustainable machine tools and forming technologies bases upon industrial and environmental requirements for resource efficiency. Relevant technical trends base upon impact studies and applied research projects on the lifecycle resource consumption for manufacturing processes and systems. This paper gives an overview about a unified methodological approach of the evaluation of resource efficiency of machine tools. It answers the scientific question on sustainability: which technological parameters and machine tool characteristics lead to their lowest resource consumption/losses and part manufacturing costs. Therefore, the method allows to consider them as an energy-information model, in which the transformation of any forms and types of energy, material, and information takes place. It is shown that innovative hollow shaft forming technologies become sustainable alternatives to cutting technologies. A smart factory uses digitalization, manufacturing data management, and self-learning methods for resource efficiency. Sustainable production requires robust and error-free machining processes. Therefore, a collision prevention system protects machining centers and work pieces from collisions in real time will be presented. The gathered information about the product and its properties as well as manufacturing data builds a digital twin and enables a prediction of the resource consumption in smart factories.
Keeping track of locations across self-motion is possible by continuously updating spatial representations or by encoding and later instantaneously retrieving spatial representations. In virtual reality (VR), sensory cues to self-motion used in continuous updating are typically reduced. In passive translation compared to real walking in VR, optic flow is available but body-based (idiothetic) cues are missing. With both kinds of translation, boundaries and landmarks as static visual cues can be used for instantaneous updating. In two experiments, we let participants encode two target locations, one of which had to be reproduced by pointing after forward translation in immersive VR (HMD). We increased sensory cues to self-motion in comparison to passive translation either by strengthening optic flow or by real walking. Furthermore, we varied static visual cues in the form of boundaries and landmarks inside boundaries. Increased optic flow and real walking did not reliably increase performance suggesting that optic flow even in a sparse environment was sufficient for continuous updating or that merely instantaneous updating took place. Boundaries and landmarks, however, did support performance as quantified by decreased bias and increased precision, particularly if they were close to or even enclosed target locations. Thus, enriched spatial context is a viable method to support spatial updating in VR and synthetic environments (teleoperation). Spatial context does not only provide a static visual reference in offline updating and continuous allocentric self-location updating but also, according to recent neuroscientific evidence on egocentric bearing cells, contributes to continuous egocentric location updating as well.
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