Journal of the Japan Society for Precision Engineering
Online ISSN : 1882-675X
Print ISSN : 0912-0289
ISSN-L : 0912-0289
Volume 87 , Issue 8
Showing 1-16 articles out of 16 articles from the selected issue
Special Issue : Recent Trends in Image Measurement Technology
My Experience in Precision Engineering
Gravure & Interview
Introduction to Precision Engineering
Introduction of Laboratories
  • Shota MATSUI, Nobutoshi OZAKI, Toshiki HIROGAKI, Eiichi AOYAMA
    2021 Volume 87 Issue 8 Pages 691-697
    Published: August 05, 2021
    Released: August 05, 2021

    In this study, we focused on screw cutting with a thread mill tool based on helical interpolation motion and proposed a novel monitoring method to achieve a high accuracy of screw cutting with a wireless holder system. In the present report, we investigated the influence of pilot hole diameter on the accuracy of machining the screw for three kinds of steel materials, which were JIS S50C (200 HV2 hardness), JIS SKD61 (400 HV2 hardness) and JIS SKD61 (600 HV2 hardness) steels., based on measured cutting force from the wireless holder system. As a result, the proposed monitoring method is effective to estimate the processes and to improve the accuracy of machining screws from various work materials using helical interpolation motion of a thread mill tool.

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  • Koichiro NARIMATSU, Soichi IBARAKI, Naruhiro IRINO
    2021 Volume 87 Issue 8 Pages 698-703
    Published: August 05, 2021
    Released: August 05, 2021

    Thermal deformation due to a machine tool's internal heat generation or heat exchange with the ambient environment deteriorates machining accuracy. To suppress the thermal displacement, this paper presents a scheme to estimate and then compensate for the thermal deformation on a turning center by using a deep learning neural network model. A critical issue with its practical implementation is its response to temperature sensor failures. If the compensation drives the machine in an abrupt, unpredictable manner when a sensor fails, it may damage the workpiece or the machine. In this paper, a scheme to train the deep learning model is presented such that it becomes more robust against temperature sensor failures. The deep learning model was trained considering the assumed profiles by the temperature sensors in failure. By using a commercial machine tool, the robustness of the thermal displacement prediction model against the sensor failures is experimentally verified.

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  • —Proposal and Verification of Damping Model for Contact between Ball and R Groove—
    Tatsuya IMAI, Shoji NOGUCHI
    2021 Volume 87 Issue 8 Pages 704-709
    Published: August 05, 2021
    Released: August 05, 2021

    In the previous report, we explained the formulation of dynamic simulation by applying the translational system / rotating system model in vibration theory to the load distribution theory, which is a static analysis method for linear motion ball guides (LMBG). We also conducted an impulse response test to intentionally excite the three-degree-of-freedom mode of LMBG, and reported the contents of verifying the natural frequency of each mode. In this simulation, the spring constant and torsional spring constant with five degrees of freedom, which affect the dynamic rigidity, are calculated by the load distribution theory, so the natural frequency of each mode can be predicted with any model number. However, there is no effective calculation method for the damping ratio that affects the vibration intensity, and after all, it is necessary to perform a test in advance to identify the damping ratio or to assume a value from past measurement results, which is complete. Therefore, this time, we made a two-row experimental LMBG and conducted an impulse response test. By analyzing the correlation between the damping ratio obtained and various cont act parameters derived from Hertz's contact theory, a mathematical expression of the damping ratio, that is, I tried to build a damping model. In addition, we report the contents of applying this model to the dynamic simulation of LMBG and verifying it with an actual product.

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