The present invention relates to a computer-implemented method and a planer for calculating at least one supplementary processing plan for a workpiece to be processed by a processing machine. The method comprises the steps of: Measuring workpiece properties, including a thickness parameter of the workpiece; Providing at least one supplementary processing plan, which is specific for the measured workpiece properties. Due to the present invention, measurement of the workpiece properties is performed before starting to process the workpiece. Therefore, time and material can be saved, and scrap and waste are reduced.

A computer-implemented method is provided for determining a cut pattern of a lathe. The lathe is numerically controlled by a control device and includes a tool with a cutter acting on a workpiece. The workpiece has a start contour and a target contour to be achieved by cutting the workpiece according to the cut pattern. The method includes determining a path of a n-th layer of the cut pattern, wherein the n-th layer includes: for n≥2: an infeed path linear and/or parallel to the target contour; a circular infeed path starting tangent to the target contour; an intermediate path linear and/or parallel to the target contour; a circular outfeed path ending tangent to the target contour; and for n≥2: a smoothing path linear and/or parallel to the target contour.

Disclosed herein is a handheld device for training at least one movement and at least one activity of a machine. The handheld device may include a handle, an input unit configured to input activation information for activating the training of the machine, an output unit configured to output the activation information for activating the training of the machine to a device external to the handheld device, and a coupling structure for releasably coupling an interchangeable attachment configured according to the at least one activity.

A PLC according to an embodiment of the present invention is disclosed. The PLC according to an embodiment of the present invention comprises: a master unit; and a plurality of interface units which access the master unit via a system bus. The master unit includes: a control unit for controlling input/output and processing commands; and a plurality of first RJ45 terminals for input/output of data, wherein a maximum of eight signals are arranged via each pin of each of the first RJ45 terminals. Each of the interface units includes a connection means for connecting a signal between a second RJ45 terminal for connecting a signal line from each of the first RJ45 terminals and an external device, wherein the interface units are configured to be expandable by the number of the first RJ45 terminals.

The present invention discloses a networked control system (NCS) time-delay compensation method based on predictive control. The method comprises the following steps: (1) acquiring random time-delay data in an NCS, and preprocessing the data; (2) predicting the current time-delay by using a fuzzy neural network (FNN) optimized by a particle swarm optimization (PSO) algorithm; (3) compensating the predicted time-delay by using an implicit proportional-integral-based generalized predictive control (PIGPC) algorithm; (4) determining whether a preset work end time is up according to a clock in the NCS; if yes, ending the process; if no, returning to step (2). The method disclosed by the present invention can accurately predict and effectively compensate the NCS time-delay and has excellent development prospect.

In the present disclosure, methods, systems, and apparatuses for in-process assembly error correction are described. In various embodiments, a target arrangement of parts of an assembly may be obtained, with the target arrangement including a first target position of a first part, a second target position of a second part, and a third target position of a third part. The first part and the second part may be robotically joined based on the first target position and the second target position to obtain a first subassembly of the assembly, with the first subassembly having a first physical arrangement that includes the physical arrangement of the first and second parts after joining. The first physical arrangement may be fitted to the target arrangement to obtain a fitted first physical arrangement. The first subassembly and the third part may be robotically joined based on the fitted first physical arrangement.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

Provided is a simulation method performed by a process simulator, implemented with a recurrent neural network (RNN) including a plurality of process emulation cells, which are arranged in time series and configured to train and predict, based on a final target profile, a profile of each process step included in a semiconductor manufacturing process. The simulation method includes: receiving, at a first process emulation cell, a previous output profile provided at a previous process step, a target profile and process condition information of a current process step; and generating, at the first process emulation cell, a current output profile corresponding to the current process step, based on the target profile, the process condition information, and prior knowledge information, the prior knowledge information defining a time series causal relationship between the previous process step and the current process step.

Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.

A digital platform enables 3D printing where the designs are protected from piracy/redistribution. A single board computer (SBC) communicates with a first server and a second server. The SBC requests a unique hardware ID from the first server, which assigns and sends the ID to the SBC. The SBC submits the ID and a secret key to the second server to request registration of a user and a printer, and the second server sends private certs, a client ID, and a unique public identifier to the SBC. The second server also receives and stores 3D print designs through a designer portal, and on-demand displays the designs in a GUI screen. The SBC user may purchase a 3D print design, and the second server, in response, sends an access token to the SBC. The SBC redeems the access token for a selected 3D print, and the second server adjusts geode for the selected 3D design for the particular printer, and streams the adjusted geode to the printer through the SBC, thereby protecting the code from unauthorized user/replication.