The invention relates to a test stand comprising a support (19, 25) which is moveably connected to a wall (18, 18′, 18″), a base, a frame (26) of the test stand or another part of the test stand and can be moved on a predetermined path; an actuator (22) which is connected to the support and by means of which the support (19, 25) that can be moved on the predetermined path, two clamping devices (13) respectively comprising a ball joint, wherein one of the two clamping devices (13) is seemed to the support (19, 25) and the other of the two clamping devices (13) is arranged in an axis (10) with the first of the two clamping devices (13), such that a test body (1) is clamped between the two clamping devices (13) on outer surfaces of the test body and can be maintained by the clamping devices (13), and a test force exerted by a test body by moving the support (19, 25) through the first of the two clamping devices (13) acts essentially along the axis (10). The test body is fixed by means of an elastic element (23) in order to limit a rotation of the test body about the axis (10).

A modular test fixture is configured to quickly support test samples of different configurations for testing.

A modular test fixture is configured to quickly support test samples of different configurations for testing.

The present invention relates to a method for evaluating physical properties of a thin film specimen and a thin film specimen for a tensile test of the present invention, and according to the present invention, reliability of measured physical properties can be increased, and an abnormal damage of a thin film specimen can be suppressed by analyzing the strain rate of a speckle pattern formed on the thin film specimen by using a digital image correlation analysis scheme during a tensile test of the thin film specimen.

The present invention relates to a method for evaluating physical properties of a thin film specimen and a thin film specimen for a tensile test of the present invention, and according to the present invention, reliability of measured physical properties can be increased, and an abnormal damage of a thin film specimen can be suppressed by analyzing the strain rate of a speckle pattern formed on the thin film specimen by using a digital image correlation analysis scheme during a tensile test of the thin film specimen.

According to the metal foil fatigue test system and metal foil fatigue test method of the present invention, the fatigue degree and lifespan of the metal foil may be easily predicted by injecting gas into the tube of a roll structure and discharging the gas to simulate charge/discharge of the electrode assembly.

A method of predicting joining strength of joined dissimilar materials, includes performing a joining strength test on a plurality of specimens of joined dissimilar materials each having different joining information, and acquiring force-displacement data on a basis of the joining information; constructing, in a prediction system, an artificial neural network model for predicting the force-displacement data and the joining strengths from the joining information; learning the artificial neural network model by inputting the force-displacement data to the prediction system, the force-displacement data obtained through the joining strength test; inputting joining information to be predicted to the prediction system by using a computer running a software for performing prediction for the joining strength and connected to a host computer of the prediction system through a network; and predicting, by the learned artificial neural network model, force-displacement value and joining strength.

A method of predicting joining strength of joined dissimilar materials, includes performing a joining strength test on a plurality of specimens of joined dissimilar materials each having different joining information, and acquiring force-displacement data on a basis of the joining information; constructing, in a prediction system, an artificial neural network model for predicting the force-displacement data and the joining strengths from the joining information; learning the artificial neural network model by inputting the force-displacement data to the prediction system, the force-displacement data obtained through the joining strength test; inputting joining information to be predicted to the prediction system by using a computer running a software for performing prediction for the joining strength and connected to a host computer of the prediction system through a network; and predicting, by the learned artificial neural network model, force-displacement value and joining strength.

The present disclosure relates to the drilling of oil wells and more particularly to a testing tool, a testing assembly and method for use in measuring selected rock mechanics characteristics downhole.

The present disclosure relates to the drilling of oil wells and more particularly to a testing tool, a testing assembly and method for use in measuring selected rock mechanics characteristics downhole.