A machine learning apparatus includes: a state observation unit that observes a characteristic shape, an adopted plan, and a determination result as state variables, the characteristic shape representing a shape of a part of a product of wire electric discharge machining, adjustment of machining conditions being deemed as necessary for the part of the product, the adopted plan being an adjustment method selected from among one or more adjustment methods for adjusting the machining conditions to improve machining performance for the part indicated by the characteristic shape, the determination result indicating whether implementation of the adopted plan is effective in improving machining performance for the part corresponding to the characteristic shape; and a learning unit that learns the machining condition adjustment method according to a data set created based on the state variables.

A machine learning apparatus includes: a state observation unit that observes a characteristic shape, an adopted plan, and a determination result as state variables, the characteristic shape representing a shape of a part of a product of wire electric discharge machining, adjustment of machining conditions being deemed as necessary for the part of the product, the adopted plan being an adjustment method selected from among one or more adjustment methods for adjusting the machining conditions to improve machining performance for the part indicated by the characteristic shape, the determination result indicating whether implementation of the adopted plan is effective in improving machining performance for the part corresponding to the characteristic shape; and a learning unit that learns the machining condition adjustment method according to a data set created based on the state variables.

A method for operating equipment according to sequence of operation using geometric models including obtaining a first geometric model for a first set of equipment and a second geometric model for a second set of equipment, the first set of equipment and the second set of equipment defined by the sequence of operation for the equipment, locating, on the first geometric model, a first nearest operating point based on a desired operating point, generating a first modified geometric model by removing one or more operating points that do not satisfy the first nearest operating point, generating a merged geometric model by merging the first modified geometric model with the second geometric model, locating a second nearest operating point based on a modified desired operating point, and operating the equipment in accordance with the first nearest operating point and the second nearest operating point.

A method for operating equipment according to sequence of operation using geometric models including obtaining a first geometric model for a first set of equipment and a second geometric model for a second set of equipment, the first set of equipment and the second set of equipment defined by the sequence of operation for the equipment, locating, on the first geometric model, a first nearest operating point based on a desired operating point, generating a first modified geometric model by removing one or more operating points that do not satisfy the first nearest operating point, generating a merged geometric model by merging the first modified geometric model with the second geometric model, locating a second nearest operating point based on a modified desired operating point, and operating the equipment in accordance with the first nearest operating point and the second nearest operating point.

A machine learning module is provided trained to generate from a design data record specifying a design variant, a predictive performance distribution and a constraint compliance distribution of the design variant. A predictive performance distribution and a constraint compliance distribution are generated by the machine learning module. The predictive performance distribution is compared with performance values of previously evaluated design data records. A simulation of the corresponding design variant is either run or skipped. A design evaluation record is output which includes a performance value and constraint compliance data each derived from the simulation if the simulation is run or, otherwise, each derived from the predictive performance distribution and the constraint compliance distribution. Depending on the design evaluation records, a performance-optimizing and constraint-compliant design data record is selected from the variety of design data records. The selected design data record is then output for controlling the production system.

Methods and systems for improving tire uniformity through identification of characteristics of one or more candidate process effects are provided. The magnitudes of process harmonics associated with one or more candidate process effects can be identified by combining uniformity measurements for a set of tires to achieve an enhanced resolution for a sampling of the process harmonic. The enhanced resolution approach can combine uniformity measurements for a set of a plurality of tires that are slightly offset from one another to generate a composite process harmonic sampling. In particular, the composite process harmonic sampling can be generated by aligning the uniformity measurements for each tire in the set of tires based on the azimuthal location of the maximum magnitude of the process harmonic on each tire. The magnitude of the process harmonic can then be determined using the composite process harmonic sampling.