A method for adjusting a parameter value of a position controller and a parameter value of a rotational speed controller is provided. The position controller and the rotational speed controller are cascaded and are a component of a control circuit. The method includes generating and displaying a two-dimensional parameter adjusting field, wherein points in the two-dimensional parameter adjusting field are selectable by a user, and a specific parameter value of the position controller and a specific parameter value of the rotational speed controller are allocated to a point, and after the user has selected a point adopting the specific parameter value of the position controller that is allocated to the selected point and adopting the specific parameter value of the rotational speed controller that is allocated to the selected point.

The present invention discloses an adaptive-learning intelligent scheduling unified computing frame and system for industrial personalized customized production. Based on a deep neural network and reinforcement learning, a most suitable optimization algorithm is selected by automatic decision-making for a global customized production task with an industrial big data module at the bottom as an information basis, and a global optimal static scheduling plane is generated; a current dynamic event in a factory are monitored in real time; if no dynamic event requiring dynamic scheduling optimization is monitored, the global optimal static plan is executed sequentially; when a dynamic event impact requiring dynamic scheduling optimization is monitored, information of the current dynamic event is interpreted and classified, and corresponding optimization algorithms are automatically selected for dynamic scheduling optimization according to different types of dynamic events; and a dynamic scheduling scheme is evaluated by a subsequent module, an optimization scheme is regenerated or a most suitable optimization algorithm is automatically decided based on the scheme according to an evaluation result, and an equipment deployment sequence is generated for an automatic deployment. Considering the features of complicated procedures, a large amount of customization information and the high frequency of diversified dynamic events in personalized customized production, the present invention provides the adaptive-learning intelligent scheduling unified computing frame and system for industrial personalized customized production, that adopt two steps in the three aspects of static scheduling planning, dynamic scheduling planning and equipment deployment based on deep learning, that is, targeted optimization is performed after classification, thus improving the optimization efficiency and effect; and the system better fits the features of personalized customized production, and can effectively improve the efficiency of personalized customized production and minimize manual decision-making costs.

A three-dimensional (3D) printer includes a nozzle and a camera configured to capture a real image or a real video of a liquid metal while the liquid metal is positioned at least partially within the nozzle. The 3D printer also includes a computing system configured to perform operations. The operations include generating a model of the liquid metal positioned at least partially within the nozzle. The operations also include generating a simulated image or a simulated video of the liquid metal positioned at least partially within the nozzle based at least partially upon the model. The operations also include generating a labeled dataset that comprises the simulated image or the simulated video and a first set of parameters. The operations also include reconstructing the liquid metal in the real image or the real video based at least partially upon the labeled dataset.

Methods and apparatus for two-dimensional and three-dimensional scanning path visualization are disclosed. An example apparatus includes a parameter determiner to determine at least one of a laser beam parameter setting or an electron beam parameter setting, a melt pool geometry determiner to identify melt pool dimensions using the parameter setting, the melt pool geometry determiner to vary the parameter setting to obtain multiple melt pool dimensions, and a visualization path generator to generate a three-dimensional view of a scanning path for an additive manufacturing process using the identified melt pool dimensions. The visualization path generator adjusts the laser beam parameters based on the generated three-dimensional view.

[Problem] To enable a highly effective medium to be manufactured.

[Solution] a manufacturing method of a medium is provided with: a step of creating a prediction model at least based on values of parameters related to manufacturing of another media manufactured in the past and being different in at least any one among an object of culture, an index of the culture, and a manufacturing condition of the medium manufacturing; a step of creating a value of the parameter using the prediction model; and a step of manufacturing the medium using the created value of the parameter.

A method implemented by a computing system that facilitates formation of an object from a material comprises receiving an object model that specifies a top surface and a side surface connected to an edge of the top surface via a fillet that extends along the edge. At a particular region along the edge, adjacent planar regions of the side surface define an obtuse angle therebetween. The fillet is adjusted along the edge to have a first radius at a particular distance from the particular region and to have a second radius, smaller than the first radius, proximate the particular region. Output data associated with an adjusted model is communicated to equipment configured to form a mandrel that facilitates formation of the object. Adjusting the fillet to have a smaller radius proximate the particular region facilitates elimination of wrinkles in the material when draped over the mandrel to form the object.

Systems and methods for training a surrogate model for predicting system states for a building management system based on generated data from a simulation model are disclosed herein. The simulation model is calibrated for a building of interest. The building of interest includes building equipment operable to control a variable state of the building. The simulated data of system states are generated using the calibrated simulation model. A surrogate model is trained based on the simulated data of system states from the calibrated simulation model. System state predictions are generated using the surrogate model. The surrogate model is re-trained based on updated operational data. An updated series of system state predictions is generated using the re-trained surrogate model.

Methods and apparatus for determining a subset of a plurality of relationships between a plurality of parameters describing operation of a lithographic apparatus, the method comprising: determining a first set of data describing first relationships between a plurality of parameters of a reference apparatus; based on one or more measurements, determining a second set of data describing second relationships between the plurality of parameters of the reference or a further apparatus; comparing the first set of data and the second set of data; and selecting from the second set of data a subset of the second relationships based on differences between the first set of data and the second set of data.

A bridge crane anti-swing method based on first-order dynamic sliding mode variable structure includes steps of: constructing a two-dimensional bridge crane system model and a crane system control model, respectively; performing differentiation on two sliding mode surfaces containing swing angle dynamic change and rope length dynamic change to obtain a crane position dynamic sliding surface s1 and a rope length dynamic sliding mode surface s2, respectively; combining a displacement x, a length l and a swing angle θ in the two-dimensional bridge crane system model with the crane position dynamic sliding surface s1 and rope length dynamic sliding mod surface s2 in the crane system control model to obtain a relationship among a horizontal traction force f1, an along-rope traction force f2, the displacement x, the length l and the swing angle θ.

The invention relates to a method for the operational analysis of an engine and/or for calibrating a controller of the engine, in particular an internal combustion engine, wherein run-up occurs of test points defined by values of a plurality of predetermined operating parameters and selected from a multidimensional test space using a statistical experiment design, whereby at least one operating parameter is in each case changed from one test point to the next test point in a plurality of steps in the run-up of the test points, wherein operational measurements are performed at measurement points resulting from a respective increment and at the actual test points, whereby measurement data from the operational measurements for the analysis and calibration of the controller are output and continuously stored, as well as a corresponding system.