Provided is a defect occurrence prediction system for a machine tool that makes it possible to identify the factors causing the occurrence of defects efficiently and effectively, and predict the occurrence of the defects accurately with good precision. A defect occurrence prediction system includes an information data accumulation unit that accumulates various types of information and various types of data relating to a machining operation of the machine tool; a defective product occurrence information data extraction unit that extracts from the information data accumulation unit the various types of information and the various types of data when the defective product is produced in the machined products; and a defect occurrence prediction unit that performs a defect occurrence prediction on a basis of the various types of information and the various types of data extracted by the defective product occurrence information data extraction unit and various types of information and various types of data relating to a machining operation of the machine tool obtained in real time.

A method for processing a part from a workpiece using an industrial cutting system. The method includes receiving first data corresponding to the part to be processed from the workpiece using the industrial cutting system. The method further includes receiving second data corresponding to expertise data generated over a time period. The method also includes identifying features of the part based on the first data and the second data. The method further includes generating a part program design including geometry data and processing parameters for at least one of the features of the part. The method also includes processing the part from the workpiece using the industrial cutting system based on the part program design.

Systems, methods, and apparatuses for implementing a multi tenant blockchain platform for managing the Einstein cloud platform's decisions using Distributed Ledger Technology (DLT) in conjunction with a cloud based computing environment. For example, according to one embodiment there is a system having at least a processor and a memory therein executing within a host organization, in which such a system includes means for operating a blockchain interface to a blockchain on behalf of a plurality of tenants of the host organization, in which each one of the plurality of tenants operate as a participating node with access to the blockchain; configuring the blockchain to share a training data set between two or more of the plurality of tenants pursuant to a consent agreement to share the training data set; training an AI model to make recommendations based on the training data set shared between the two or more of the plurality of tenants; receiving a request to register the AI model with an audit record keeping service; receiving a transaction at the blockchain; issuing a decision by the AI model to accept or reject the transaction; and transacting a new asset onto the blockchain recording the decision to accept or reject the transaction and the data set utilized to train the AI model and a version of the AI model. Other related embodiments are disclosed.

A device for determining a spindle status of a spindle of a machine tool includes a detector for detecting sensor data of the spindle for a defined time window. A processing unit analyses the sensor data through artificial intelligence by calculating a defined feature of the sensor data for the defined time window and determining a spindle status from the sensor data. An output member outputs the determined spindle status.

Exemplary systems, implement a multi-tenant blockchain platform for managing the Einstein cloud platform's decisions using Distributed Ledger Technology (DLT) in conjunction with a cloud based computing environment. The system operates a blockchain interface to a blockchain on behalf of a plurality of tenants of the host organization, configures the blockchain to share a training data set between two or more tenants pursuant to a consent agreement, trains an AI model to make recommendations based on the training data set, receives a request to register the AI model with an audit record keeping service, receives a transaction at the blockchain, issues a decision by the AI model to accept or reject the transaction; and then proceeds to transact a new asset onto the blockchain recording the decision to accept or reject the transaction and the data set utilized to train the AI model with a version of the AI model.

A programmable manufacturing advisor includes an information unit that receives measurements for at least one parameter of each operation of a plurality of operations in the manufacturing process, and an analytics unit that determines a baseline performance metric for the manufacturing process based on the measurements of the at least one parameter. The programmable manufacturing advisor also includes an optimization unit that determines a recommended improvement action by determining a predicted performance metric for the manufacturing process based on an adjusted value of the at least one parameter and comparing the predicted performance metric to the baseline performance metric. The optimization unit also automatically presents the recommended improvement action to the operations manager.

An artificial intelligence (AI) robot capable of performing imitation learning of an imitation target to be imitated may collect olfactory information of the imitation target and motion information about a motion executed by the imitation target according to the olfactory information for imitation learning, and may perform machine learning. When learned olfactory information is detected by the AI robot, the AI robot may be caused to execute the motion information about the motion executed by the imitation target. Accordingly, an imitation robot may imitate the imitation target based on olfactory information, in addition to sound and image information.

A method for controlling a robot. The method includes receiving an indication of a target configuration to be reached from an initial configuration of the robot, determining a coarse-scale value map by value iteration, starting from an initial coarse-scale state and until the robot reaches the target configuration or a maximum number of fine-scale states has been reached, determining a fine-scale sub-goal from the coarse-scale value map, performing, by an actuator of the robot, fine-scale control actions to reach the determined fine-scale sub-goal and obtaining sensor data to determine the fine-scale states reached, starting from a current fine-scale state of the robot and until the robot reaches the determined fine-scale sub-goal, the robot transitions to a different coarse-scale state, or a maximum sequence length of the sequence of fine-scale states has been reached and determining the next coarse-scale state.

Aspects of the disclosure are directed towards artificial intelligence-based modeling of target objects, such as aircraft parts. In an example, a system initially trains a machine learning (ML) model based on synthetic images generated based on multi-dimensional representation of target objects. The same system or a different system subsequently further trains the ML model based on actual images generated by cameras positioned by robots relative to target objects. The ML model can be used to process an image generated by a camera positioned by a robot relative to a target object based on a multi-dimensional representation of the target object. The output of the ML model can indicate, for a detected target, position data, a target type, and/or a visual inspection property. This output can then be used to update the multi-dimensional representation, which is then used to perform robotics operations on the target object.

Aspects of the disclosure are directed towards artificial intelligence-based modeling of target objects, such as aircraft parts. In an example, a system initially trains a machine learning (ML) model based on synthetic images generated based on multi-dimensional representation of target objects. The same system or a different system subsequently further trains the ML model based on actual images generated by cameras positioned by robots relative to target objects. The ML model can be used to process an image generated by a camera positioned by a robot relative to a target object based on a multi-dimensional representation of the target object. The output of the ML model can indicate, for a detected target, position data, a target type, and/or a visual inspection property. This output can then be used to update the multi-dimensional representation, which is then used to perform robotics operations on the target object.