Provided is a numerical controller capable of easily controlling a pressure without a pressure sensor. The numerical controller estimates a pressure based on at least one of a command value and a feedback value. A machine learning device for learning the pressure corresponding to the at least one of the command value and the feedback value is included. The machine learning device includes a state observation unit for observing the at least one of the command value and the feedback value as a state variable, a label data acquisition unit for acquiring label data indicating the pressure, and a learning unit for associating and learning the state variable with the label data.

A wire electric discharge machine includes a machine learning device that learns an adjustment of an axis feed command of the wire electric discharge machine. The machine learning device determines an adjustment amount of the axis feed command by using data related to a movement state of an axis, and adjusts the axis feed command based on the determined adjustment amount of the axis feed command. Subsequently, the machine learning device performs machine learning of the adjustment of the axis feed command based on the determined adjustment amount of the axis feed command, the data related to the movement state of the axis, and a reward calculated based on the data related to the movement state of the axis.

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 robot for object manipulation may include sensors, a robot appendage, actuators configured to drive joints of the robot appendage, a planner, and a controller. Object path planning may include determining poses. Object trajectory optimization may include assigning a set of timestamps to the poses, optimizing a cost function based on an inverse kinematic (IK) error, a difference between an estimated required wrench and an actual wrench, and a grasp efficiency, and generating a reference object trajectory based on the optimized cost function. Grasp sequence planning may be model-based or deep reinforcement learning (DRL) policy based. The controller may implement the reference object trajectory and the grasp sequence via the robot appendage and actuators.

A first selection of a first fabrication process and/or first manufacturing equipment to perform manufacturing operations of the first fabrication process is received. The first selection is input into a digital replica of the first manufacturing equipment, where the digital replica outputs physical conditions of the first fabrication process. Environmental resource usage data indicative of a first environmental resource consumption of the first fabrication process run on the first manufacturing equipment based on the physical conditions of the first fabrication process is determined. A modification to the first fabrication process that reduces the environmental resource consumption of the first fabrication process run on the first manufacturing equipment is determined. Applying the modification to the first fabrication and/or providing the modification for display by a graphical user interface (GUI) is performed.

A method of training a neural network includes obtaining two ground truth thickness profiles a test substrate, obtaining two thickness profiles for the test substrate as measured by an in-situ monitoring system while the test substrate is on polishing pads of different thicknesses, generating an estimated thickness profile for another thickness value that is between the two thickness values by interpolating between the two profiles, and training a neural network using the estimated thickness profile.

A computer aided process for creation of a manufacturing facility, for production of a user-selected product, relies on a set of functional modules for specification of the facility's floorspace requirements, manufacturing equipment, and equipment layout to allow optimization of the facility for a production capacity specified by the user.

Techniques are presented for the application of neural networks to the fabrication of integrated circuits and electronic devices, where example are given for the fabrication of non-volatile memory circuits and the mounting of circuit components on the printed circuit board of a solid state drive (SSD). The techniques include the generation of high precision masks suitable for analyzing electron microscope images of feature of integrated circuits and of handling the training of the neural network when the available training data set is sparse through use of a generative adversary network (GAN).

A textile mill system and associated method include a plurality of spinning mills each having textile machines. A computer system determines adapted machine parameters for the textile machines and processes within the spinning mills. The computer system includes a receiving and transmitting section configured to receive operational information from the spinning mills and the textile machines, and a first database configured to store the received operational information. A processing section includes an optimizer section with a neural network, wherein the neural network uses the operational information stored in the first database with processes for or derived from supervised or unsupervised machine or deep learning to determine the adapted machine parameters.

Embodiments of the disclosure provide for intelligent model integration within a process simulation system. Some embodiments receive data associated with the operation of a plant, determine, using at least one specially configured algorithm and based on the received data, at least one qualifying dataset determined qualified to train an intelligent model, train the intelligent model using the at least one qualifying dataset, and deploy the trained intelligent model for use.