Systems and a method determine a sequence of joint values of an external axis along a sequence of targets. Inputs are received, including robot representation, tool representation, sequence of targets, kinematics of the axis joints, and/or type of robot-axis motion. For each target, it is generated at least one weight factor table representing, for each available configuration of the axis joint motion, a combined effort of the robot motion and the axis motion depending on the type of combined robot-axis motion. Valid weight factor values of the table are determined by simulating collision free trajectories for reaching the target. The sequence of joint values of the at least one external axis is determined by finding from the weight factor table a sequence of joint values for which the sum of their corresponding weight factors for reaching the target location sequence is minimized.

Examples of methods for part packing based on agent usage are described herein. In some examples, a method includes or methods include determining an agent usage for each of a plurality of packings. In some examples, the method includes or methods include selecting a packing from the plurality of packings based on the agent usages.

Method for manufacturing a workpiece with a predefined sequence of machine tools by a computer-controlled manufacturing machine with an optimal tool configuration, wherein the initial locations of the tools are allocated, first and second time transfer time functions are calculated for all tools, possible tool location sequences are generated, for possible tool location sequences a respective first cost factor is calculated, obtained tool location sequences are ranked in accordance with the respective first cost factor, the tools are transferred from their initial locations to the optimal locations in accordance with an optimal tool configuration, and the workpiece is manufactured with the optimal tool configuration by the manufacturing machine.

A semiconductor fabrication scheduling method includes creating a load scheduling data schema including facility data of product lots to be dispatched to a plurality of workstations; generating a load schedule profile using a load-balancing model and based on the load scheduling data schema, wherein the load-balancing model includes one or more objective functions and there is at least one weight factor in an objective function; generating a current load schedule based on the load schedule profile; dispatching the product lots to the plurality of workstations using the current load schedule to complete fabrication of the product lots; obtaining a set of current key performance indicators (KPIs) of the completed fabrication of the product lots; and automatically adjusting the weight factors of the objective functions of the load-balancing model based on the current KPIs using a big-data architecture to generate a next load schedule for next cycle of fabrication.

Embodiments of the present disclosure provide a method of fast optimization and compensation for volumetric positioning errors of rotary axes of a five-axis CNC system machine tool. The method comprises: establishing a volumetric positioning error model; forming an error database containing 12 geometrical error vectors; constructing a volumetric positioning error compensation table; establishing a compensation value optimization model; completing an iterative optimization of compensation values of volumetric positioning errors; generating a volumetric positioning error compensation file for a CNC system to complete compensation for the volumetric positioning errors; and updating the error database, detecting linkage trajectories of the rotary axes, and setting a linkage trajectory positioning error threshold, and guaranteeing accuracy by iteratively implementing detection, optimization, and compensation.

A semiconductor fabrication scheduling method includes creating a load scheduling data schema including facility data of product lots to be dispatched to a plurality of workstations; generating a load schedule profile using a load-balancing model and based on the load scheduling data schema, wherein the load-balancing model includes one or more objective functions and there is at least one weight factor in an objective function; generating a current load schedule based on the load schedule profile; dispatching the product lots to the plurality of workstations using the current load schedule to complete fabrication of the product lots; obtaining a set of current key performance indicators (KPIs) of the completed fabrication of the product lots; and automatically adjusting the weight factors of the objective functions of the load-balancing model based on the current KPIs using a big-data architecture to generate a next load schedule for next cycle of fabrication.

Systems and a method determine a sequence of joint values of an external axis along a sequence of targets. Inputs are received, including robot representation, tool representation, sequence of targets, kinematics of the axis joints, and/or type of robot-axis motion. For each target, it is generated at least one weight factor table representing, for each available configuration of the axis joint motion, a combined effort of the robot motion and the axis motion depending on the type of combined robot-axis motion. Valid weight factor values of the table are determined by simulating collision free trajectories for reaching the target. The sequence of joint values of the at least one external axis is determined by finding from the weight factor table a sequence of joint values for which the sum of their corresponding weight factors for reaching the target location sequence is minimized.

The method includes collecting a schedule job list from a database, generating a plurality of schedules for a schedule job to be processed with respect to the schedule job list, calculating an evaluation index for the plurality of generated schedules, determining whether the calculated evaluation index for the plurality of schedules has reached a target evaluation index, selecting a schedule corresponding to two evaluation indices when the calculated evaluation index does not reach the target evaluation index and generating two new schedules using a genetic algorithm, and setting a selection probability so that a schedule having the highest evaluation index is selected and returning the selection probability to a user when the calculated evaluation index reaches the target evaluation index.

A facility abnormality prediction model generation system includes: a data receiver receiving data of sensors of a facility previously obtained during an operation of the facility; an abnormality notification time predictor detecting a malfunction time of a malfunction of the facility based on the data of the sensors and determining an abnormality notification time for pre-notification of the malfunction of the facility based on the detected malfunction time; an optimal sensor combination calculator generating a chromosome based on the data of the sensors and performing a genetic algorithm using the generated chromosome to calculate an optimal sensor combination which is a combination of sensor data related to the determined abnormality notification time; and a facility abnormality prediction model generator generating a facility abnormality prediction model for the pre-notification of the malfunction of the facility, based on the optimal sensor combination.

Disclosed are a method and apparatus for automatically scheduling jobs in computer numerical control machines using machine learning. The method includes collecting a schedule job list from a database, generating a plurality of schedules for a schedule job to be processed with respect to the schedule job list, calculating an evaluation index for the plurality of generated schedules, determining whether the calculated evaluation index for the plurality of schedules has reached a target evaluation index, selecting a schedule corresponding to two evaluation indices when the calculated evaluation index does not reach the target evaluation index and generating two new schedules using a genetic algorithm, and setting a selection probability so that a schedule having the highest evaluation index is selected and returning the selection probability to a user when the calculated evaluation index reaches the target evaluation index.