The present invention is directed to a system for measuring surface flatness, deformation and/or coefficient of thermal expansion (CTE) of a specimen comprising an image capture and analysis processing calibration means for performing image capture and analysis processing calibration of said system, a measuring means for measuring surface flatness of a specimen in a specimen holder, a heating means for heating said sample holder with a predetermined profile, and a control means for providing the predetermined heating profile onto the surface of said specimen and controlling operations of said image capture and analysis processing calibration means, said measuring means, and said heating means.

A magnetic core structure for a Linear Variable Differential Transducer (LVDT) comprising an elongate core of magnetic material mounted within a protective tube and means for positioning the core within the protective tube, the means for positioning comprising a ball provided within the protective tube at one end of the core, the ball being formed of an elastic material having a coefficient of thermal expansion selected to compensate the difference in elongation between magnetic core structure components caused by thermal expansion.

A magnetic core structure for a Linear Variable Differential Transducer (LVDT) comprising an elongate core of magnetic material mounted within a protective tube and means for positioning the core within the protective tube, the means for positioning comprising a ball provided within the protective tube at one end of the core, the ball being formed of an elastic material having a coefficient of thermal expansion selected to compensate the difference in elongation between magnetic core structure components caused by thermal expansion.

A machine learning device includes: a measured data acquisition unit that acquires a measured data group; a thermal displacement acquisition unit that acquires a thermal displacement actual measured value about a machine element; a storage unit that uses the measured data group acquired by the measured data acquisition unit as input data, uses the thermal displacement actual measured value about the machine element acquired by the thermal displacement acquisition unit as a label, and stores the input data and the label in association with each other as teaching data; and a calculation formula learning unit that performs machine learning based on the measured data group and the thermal displacement actual measured value about the machine element, thereby setting a thermal displacement estimation calculation formula used for calculating the thermal displacement of the machine element based on the measured data group.

A method of measuring physical properties includes: a preparation step of preparing a moisened member containing an organic material and having a known water absorption rate and a known mass; a heating and cooling step of performing cooling after heating the member; and a measurement step of measuring a mass of the member after cooling the member in the heating and cooling step, in which in the heating and cooling step, a deformation rate of the member is measured using a digital image correlation method.

A method of measuring physical properties includes: a preparation step of preparing a moisened member containing an organic material and having a known water absorption rate and a known mass; a heating and cooling step of performing cooling after heating the member; and a measurement step of measuring a mass of the member after cooling the member in the heating and cooling step, in which in the heating and cooling step, a deformation rate of the member is measured using a digital image correlation method.

A machine learning device includes a virtual temperature model calculating unit having an equation including a first coefficient for determining a heat generation amount and a second coefficient for determining a heat dissipation amount. The virtual temperature model calculating unit is configured to calculate virtual temperature data by estimating a temperature of a specific portion of a machine by the equation using heat generation factor data. A thermal displacement model calculating unit is configured to calculate, using the calculated virtual temperature data and actual temperature data acquired from at least one temperature sensor mounted to a portion other than the specific portion, an error between thermal displacement estimated by the equation and actually measured thermal displacement, in which the virtual temperature model calculating unit performs machine learning to search for the first coefficient and the second efficient so that the error is minimized.

A residual thermal strain distribution measurement method of measuring a residual thermal strain distribution as residual thermal deformation in a sample generated under application of a thermal load, comprises recording images of a periodic pattern present on the surface of the sample by an image recording unit at a first temperature and a sample formation temperature at which the sample is formed, generating moire fringes based on each recorded image of the periodic pattern, calculating a phase of the moire fringes for the sample at the first temperature, calculating a phase of the moire fringes for the sample at the sample formation temperature, acquiring a phase difference of the moire fringes at the sample formation temperature with respect to the first temperature, and calculating a residual thermal strain of the sample at the first temperature with respect to the sample formation temperature based on the acquired phase difference.

An improved method and apparatus for determination of the absolute coefficient of thermal expansion of materials, including ultralow expansion materials, utilizes a metrology frame that is regulated within a first narrow temperature range that varies by only a small fraction of a degree Celsius from a set point temperature (e.g., less than about 0.01° C. from the set point temperature), while the temperature of the sample is varied to determine the coefficient of thermal expansion over a larger temperature range (e.g., 30, 40 or 50° C.). The method and apparatus permit determination of the coefficient of thermal expansion of a material to levels approaching 10.sup.−9/° C.

An improved method and apparatus for determination of the absolute coefficient of thermal expansion of materials, including ultralow expansion materials, utilizes a metrology frame that is regulated within a first narrow temperature range that varies by only a small fraction of a degree Celsius from a set point temperature (e.g., less than about 0.01° C. from the set point temperature), while the temperature of the sample is varied to determine the coefficient of thermal expansion over a larger temperature range (e.g., 30, 40 or 50° C.). The method and apparatus permit determination of the coefficient of thermal expansion of a material to levels approaching 10.sup.−9/° C.