A communication system is equipped with a mobile terminal and a controller of an industrial machine for forming a shaped product. Mutual communications are carried out between the controller and the mobile terminal. The mobile terminal includes an image capturing unit for capturing an image of an object to be imaged, an image conversion unit which converts the captured image data captured by the image capturing unit and generates converted image data according to an image format rule corresponding to the controller, a setting data creation unit which creates setting data for the converted image data, so that the converted image data can be used by the controller, and a data transmission and reception unit which carries out communications with the controller and transmits the converted image data and the setting data to the controller.

A numerical controller which controls a machine tool acquires tool information including a shape of a tool, a machining condition in machining, and information related to a machining result of a workpiece after machining. A machine learning device performs machine learning on tendency of the information related to a machining result with respect to the tool information and the machining condition based on the tool information and the machining condition used as input data and based on the information related to a machining result used as teacher data, so as to construct a learning model. The machine learning device determines whether or not a machining result is good by using the learning model based on the tool information and the machining condition before the machine tool machines a workpiece.

A cut-out glass plate positioning apparatus includes: a cut line forming device 4 provided in a cut line forming position 4a; a bend-breaking and separating device 6 for cutting out unworked plate glasses 5 from an unworked plate glass 2 along the cut lines 3; a pair of position and angle correcting devices 8 for effecting correction of the position and angle with respect to the unworked plate glass 5; a pair of sucking and transporting devices 9 for suckingly lifting and transporting the unworked plate glass 5 to each position and angle correcting device 8; and two CCD cameras 10 respectively installed above the position and angle correcting devices 8.

A blank shape determining method includes: a process of making a forming analysis of forming a reference blank into a reference formed product and a acquiring sheet thickness distribution and a plastic strain distribution; a process of acquiring a forming failure evaluation index for the reference blank; a process of estimating a region, which includes an end edge portion at which the forming failure evaluation index exceeds a predetermined threshold, within the reference blank as a forming failure region; a process of generating a plurality of corrected blanks; a process of making a forming analysis of forming the corrected blanks into corrected formed products and acquiring a sheet thickness distribution and a plastic strain distribution; a process of acquiring the forming failure evaluation index for the corrected formed product; and a process of determining a shape of the corrected blank having a smallest maximum value of the forming failure evaluation index as a shape of a blank provided for press forming.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes. A method includes: obtaining a design space and design criteria for a modeled object including a design constraint on an acceptable likelihood of failure, wherein a statistical model that relates a structural performance metric to specific likelihoods of failure for material(s) is used to translate between the acceptable likelihood of failure and a value for the structural performance metric; iteratively modifying a generatively designed shape of the modeled object in the design space in accordance with the design criteria including the design constraint to stay under the acceptable likelihood of failure for the physical structure, wherein the numerical simulation includes computing the structural performance metric, which is evaluated against the design constraint; and providing the generatively designed shape of the modeled object for use in manufacturing a physical structure.

Techniques for determining print quality for a 3D printer are disclosed. An example method includes obtaining an image of a stream of material jetted from a nozzle of the 3D printer, and binarizing the image to distinguish background features from foreground features contained in the image. The method also includes identifying elements of jetted material in the foreground features, and computing statistical data characterizing the identified elements. The method also includes generating a quality score of jetting quality based on the statistical data and controlling the 3D printer based on the quality score. The quality score indicates a degree to which the elements of jetted material form droplets of a same size, shape, alignment, and jetting frequency.

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes. A method includes: obtaining a design space and design criteria for a modeled object including a design constraint on an acceptable likelihood of failure, wherein a statistical model that relates a structural performance metric to specific likelihoods of failure for material(s) is used to translate between the acceptable likelihood of failure and a value for the structural performance metric; iteratively modifying a generatively designed shape of the modeled object in the design space in accordance with the design criteria including the design constraint to stay under the acceptable likelihood of failure for the physical structure, wherein the numerical simulation includes computing the structural performance metric, which is evaluated against the design constraint; and providing the generatively designed shape of the modeled object for use in manufacturing a physical structure.

An exemplary system and method are disclosed for a shape similarity search via a trained AI model configured on both part shape and material properties, among others, to allow for searching against a database of parts to identify a set of candidate like models or parts. In some embodiments, the trained AI model can learn the capabilities of a manufacturing facilities to which the trained AI model can be interrogated to identify a candidate manufacturer for a given part having both part shape and material properties. The operation can be performed via federated learning for improved privacy.

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.