The authors propose a technique for computerized simulation and tangency of gears provided with real tooth surfaces. The deviations of real tooth surfaces from the theoretical ones are caused by the distortion of surfaces during the heat treatment and lapping. The main ideas of the proposed technique are as follows: (i) The gear real tooth surface is represented by a sum of two vector functions that determine the theoretical tooth surface and the deviations of the real surface from the theoretical one, respectively. (ii) Both vector functions mentioned above are represented in terms of the same Gaussian surface coordinates (the Gaussian coordinates of the theoretical surface). (iii) The deviations of the real surface are initially determined numerically using the data of surface coordinate measurements. The analytical representation of the vector function of deviations is based on the interpolation of a numerically given vector function by a bi-cubic spline. The interpolation provides a relatively high precision because it is accomplished for the surface of small deviations but not for the whole real surface. (iv) The computerized simulation of meshing and tangency of gears with real tooth surfaces is based on the algorithm that describes the conditions of continuous tangency of real tooth surfaces. The proposed approach is illustrated with application to the hypoid gear drive with real tooth surfaces. The data of surface deviations have been determined experimentally at the Nissan Motor Co.
The present study investigated the relationship between verbal and visuospatial working memory (WM) capacity and long-range regression (i.e., word relocation) processes in reading. We analyzed eye movements during a “whodunit task”, in which readers were asked to answer a content question while original text was being presented. The eye movements were more efficient in relocating a target word when the target was at recency positions within the text than when it was at primacy positions. Furthermore, both verbal and visuospatial WM capacity partly predicted the efficiency of the initial long-range regression. The results indicate that WM representations have a strong influence at the first stage of long-range regression by driving the first saccade movement toward the correct target position, suggesting that there is a dynamic interaction between internal WM representations and external actions during text reading.
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