The present invention provides an on-machine real-time measurement method and system for a full-closed loop five-axis computer numerical control machine tool. In the system, a high-precision coaxial fixture in a displacement measurement component is used for connecting the component with the machine tool spindle; a multifunctional evaluation electronics box reads the signals of the grating scale of each machine axis in real time, and synchronously triggers the displacement sensor to collect the measured workpiece surface information; a synchronous communication module caches the grating scale signals of each machine axis and the measurement signals of the sensor to the FIFO rotation buffering module of the host computer in parallel to reduce the burden of high-speed transmission; and the host computer performs data processing and coordinate transformation of the grating scale data and the measurement information in the FIFO module, and finally obtains the three-dimensional geometric information of the measured workpiece surface.

In order to stabilize control of a driving section, a robot control apparatus includes a control section that acquires a driving position of the driving section that drives a robot and an operation force that is a force operating on the robot, and performs first control of the driving section based on the driving position and second control of the driving section based on the operation force; and a changing section that changes a size of servo stiffness of the robot that is realized by the control of the control section.

In order to stabilize control of a driving section, a robot control apparatus includes a control section that acquires a driving position of the driving section that drives a robot and an operation force that is a force operating on the robot, and performs first control of the driving section based on the driving position and second control of the driving section based on the operation force; and a changing section that changes a size of servo stiffness of the robot that is realized by the control of the control section.

An automated method for discovering features in a repeatable process includes measuring raw time series data during the process using sensors. The time series data describes multiple parameters of the process. The method includes receiving, via a first controller, the time series data from the sensors, and stochastically generating candidate features from the raw time series data using a logic block or blocks of the first controller. The candidate features are predictive of a quality of a work piece manufactured via the repeatable process. The method also includes determining, via a genetic or evolutionary programming module, which generated candidate features are most predictive of the quality of the work piece, and executing a control action with respect to the repeatable process via a second controller using the most predictive candidate features. A system includes the controllers, the programming module, and the sensors.

An automated method for discovering features in a repeatable process includes measuring raw time series data during the process using sensors. The time series data describes multiple parameters of the process. The method includes receiving, via a first controller, the time series data from the sensors, and stochastically generating candidate features from the raw time series data using a logic block or blocks of the first controller. The candidate features are predictive of a quality of a work piece manufactured via the repeatable process. The method also includes determining, via a genetic or evolutionary programming module, which generated candidate features are most predictive of the quality of the work piece, and executing a control action with respect to the repeatable process via a second controller using the most predictive candidate features. A system includes the controllers, the programming module, and the sensors.