A machining apparatus error correction method is implemented in a machining apparatus error correction system. The method includes setting initial operating parameters according to a predetermined machining program, obtaining dimensional detection data during machining of a product, calculating a dimensional correction parameter according to the detection data and a dimensional inspection standard according to a predetermined correction model and generating a correction parameter file readable by the machining apparatus, and distributing the correction parameter file to the corresponding machining apparatus. The initial operating parameters include clamping parameters and dimensional inspection standards.

A machining apparatus error correction method is implemented in a machining apparatus error correction system. The method includes setting initial operating parameters according to a predetermined machining program, obtaining dimensional detection data during machining of a product, calculating a dimensional correction parameter according to the detection data and a dimensional inspection standard according to a predetermined correction model and generating a correction parameter file readable by the machining apparatus, and distributing the correction parameter file to the corresponding machining apparatus. The initial operating parameters include clamping parameters and dimensional inspection standards.

In a component mounting system having an inspection device performing a mounting inspection after component mounting, a correction value for correcting a mounting program is calculated based on board inspection information including fed-back component position deviation information, a component is mounted on a board in accordance with the mounting program corrected based on the calculated correction value, and a “present value” based on most recent board inspection information and a “pre-correction evaluation value” are displayed together on an evaluation value display screen as evaluation values representing accuracy at a time of the mounting based on the board inspection information during this component mounting work, the “pre-correction evaluation value” being calculated based on a temporary position deviation amount pertaining to a case where it is assumed that no correction based on the calculated correction value has been performed.

To optimize an operation of a manufacturing facility by presenting a countermeasure for improving quality, even in a manufacturing process where the product quality changes even under constant manufacturing conditions. A countermeasure recommendation device includes a data acquisition unit for collecting a plurality of pieces of facility data, and assigning a label for each process to each piece of the facility data; a countermeasure detection unit for creating countermeasure record data; a countermeasure recommendation unit for calculating the characteristic amount, extracting the characteristic amount, and selecting the countermeasure related to the extracted characteristic amount; and a countermeasure presentation unit for presenting the countermeasure in a visualized state.

A Self-monitoring manufacturing system adapted to produce at least one object. The system includes at least a first processing facility adapted to perform a respective first processing step with the object, and a production control unit, wherein the production data comprises nominal pre-processing object data. A production monitoring unit for checking a pre-processing object state of the object is arranged, such unit being adapted to obtain actual property data of the object in-line of the production in-advance of applying the first processing step, to generate deviation data by comparing the actual property data with the production data for the first processing step, and to provide the deviation data for performing the first production step with the first processing facility in deviation-adjusted manner.

A computer system generates a tactile force model for a tactile force sensor by performing a number of calibration tasks. In various embodiments, the calibration tasks include pressing the tactile force sensor while the tactile force sensor is attached to a pressure gauge, interacting with a ball, and pushing an object along a planar surface. Data collected from these calibration tasks is used to train a neural network. The resulting tactile force model allows the computer system to convert signals received from the tactile force sensor into a force magnitude and direction with greater accuracy than conventional methods. In an embodiment, force on the tactile force sensor is inferred by interacting with an object, determining the motion of the object, and estimating the forces on the object based on a physical model of the object.

To optimize an operation of a manufacturing facility by presenting a countermeasure for improving quality, even in a manufacturing process where the product quality changes even under constant manufacturing conditions. A countermeasure recommendation device includes a data acquisition unit for collecting a plurality of pieces of facility data, and assigning a label for each process to each piece of the facility data; a countermeasure detection unit for creating countermeasure record data; a countermeasure recommendation unit for calculating the characteristic amount, extracting the characteristic amount, and selecting the countermeasure related to the extracted characteristic amount; and a countermeasure presentation unit for presenting the countermeasure in a visualized state.

A machining apparatus error correction method is implemented in a machining apparatus error correction system. The method includes setting initial operating parameters according to a predetermined machining program, obtaining dimensional detection data during machining of a product, calculating a dimensional correction parameter according to the detection data and a dimensional inspection standard according to a predetermined correction model and generating a correction parameter file readable by the machining apparatus, and distributing the correction parameter file to the corresponding machining apparatus. The initial operating parameters include clamping parameters and dimensional inspection standards.

A machining apparatus error correction method is implemented in a machining apparatus error correction system. The method includes setting initial operating parameters according to a predetermined machining program, obtaining dimensional detection data during machining of a product, calculating a dimensional correction parameter according to the detection data and a dimensional inspection standard according to a predetermined correction model and generating a correction parameter file readable by the machining apparatus, and distributing the correction parameter file to the corresponding machining apparatus. The initial operating parameters include clamping parameters and dimensional inspection standards.

A machining apparatus error correction method is implemented in a machining apparatus error correction system. The method includes setting initial operating parameters according to a predetermined machining program, obtaining dimensional detection data during machining of a product, calculating a dimensional correction parameter according to the detection data and a dimensional inspection standard according to a predetermined correction model and generating a correction parameter file readable by the machining apparatus, and distributing the correction parameter file to the corresponding machining apparatus. The initial operating parameters include clamping parameters and dimensional inspection standards.