A processing apparatus includes a central control unit, and a processing quality prediction unit, a processing unit, and a tool compensation unit which are respectively connected with the central control unit electrically. The processing quality prediction unit implements a virtual processing quality prediction method to predict the processing quality of the workpiece, output an accurate data of quality to the central control unit, and generate tool path for the processing unit to process the workpiece. The central control unit judges the data from the processing quality prediction unit and outputs the data to the tool compensation unit to calculate tool compensation data. The tool compensation unit provides the tool compensation data to the processing quality prediction unit to form a new processing path. Then the processing unit implements the compensated processing path to process the workpiece.

A processing apparatus with vision-based measurement includes a central control unit and a workpiece transporting unit, a vision-based measurement unit, a processing quality prediction unit, and a processing unit that are respectively connected to the central control unit electrically. The workpiece transporting unit is controlled by the central control unit to transport the workpiece to the vision-based measurement unit to be measured. The data obtained by the vision-based measurement unit from measuring the workpiece is provided to the processing quality prediction unit for conducting quality prediction. The processing quality prediction unit implements a virtual processing quality prediction method to establish a quality prediction model, wherein the workpiece transporting unit is utilized to assist the processes of establishing or modifying the model. The vision-based measurement unit can optically take photograph of the workpiece rapidly and convert it into a dimension data so as to facilitate the processing device to enhance the measuring efficiency.

There is provided a method of performing an analysis of a defect in a pattern of an imprint material on a substrate that has undergone an imprint process of transferring a pattern of a mold onto the substrate. The method includes obtaining a defect distribution of the pattern on the substrate, obtaining map information indicating an arrangement of the imprint material on the substrate, and determining a type of a defect based on a relationship between a position of the defect in the defect distribution and a position of a gap in the imprint material generated in a process of spreading the imprint material by the imprint process, wherein the position of the gap is predicted based on the map information.

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a computing system. The computing system receives an image of the product at a step of the multi-step manufacturing process. The computing system determines a current state of the product based on the image of the product. The computing system determines, via a deep learning model, that the product is not within specification based on the current state of the product and the image of the product. Based on the determining, the computing system adjusts a control logic for at least a following station. The adjusting includes generating, by the deep learning model, a corrective action to be performed by the following station.

A method evaluates a sample of measurement data from measuring multiple workpieces by at least one coordinate measuring machine. A system of statistical distributions describes a frequency of measurement data values. The distributions are distinguishable based on skewness and kurtosis. The method includes defining a set of statistical distributions that are able to describe a frequency of measurement data values in the entire value interval from the system of statistical distributions for a value interval of the measurement data, which is a specified value interval or a value interval of the measurement data actually arising in the sample. The method includes ascertaining the skewness and the kurtosis from the sample of measurement data corresponding to a first statistical distribution. The method includes checking, using the ascertained moment values, whether the defined set contains a statistical distribution that has the ascertained skewness and kurtosis, and producing a corresponding test result.

Embodiments of the present disclosure provide a method for predicting an occurrence of a tool processing event, thereby determining whether to activate a virtual metrology. In a model-building stage, plural sets of model-building data are used to create at least one classification model in accordance with at least one classification algorithm, in which each classification model includes plural decision trees. Then, probabilities of the decision trees are used to create at least one reliance index model, and the sets of model-building data are used to create at least one similarity index model in accordance with a statistical distance algorithm. In a conjecture stage, a set of processing data of a workpiece is inputted into each classification model, each reliance index model and each similarity index model to determine whether to activate (start) virtual metrology.

In a quality control method applied in manufacturing, product information of a product is obtained. Manufacturing parameters corresponding to the product information are queried. The manufacturing parameters are input into a product quality prediction model which is trained to obtain the value of at least one quality inspection of each product. If such quality inspection value is not equal to a standard value or is not within a standard value range, an incorrect manufacturing parameter is identified from all the manufacturing parameters applicable to each product, the incorrect manufacturing parameter being output when identified.

A cyber-physical system type machining system includes: a machine tool disposed in a real world and including a machine body and a control device; and a computer device connected to communicate with the control device and including a processor and a memory storing a program for generating, in a virtual world, a virtual machining phenomenon corresponding to an actual machining phenomenon with regard to a workpiece and the machine body. The program, when executed by the processor, causes the computer device to perform: acquiring a command value in synchronization with the control device, the command value for controlling the machine body by the control device; generating a future virtual machining phenomenon, which is the virtual machining phenomenon in a future, based on the command value; and outputting, to the control device, an optimal command value for correcting the command value based on the future virtual machining phenomenon.

According to an example, a computing apparatus may include a processing device and a machine readable storage medium on which is stored instructions that when executed by the processing device, cause the processing device to access, from a sensing device, information pertaining to formation of a part of a 3D object in a layer of build materials upon which fusing agent droplets have been or are to be selectively deposited. The instructions may also cause the processing device to predict, based upon the accessed information, a quality of the part and output an indication of the predicted quality of the part.

A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system. The apparatus, engine, system and method may include a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor associated with a computing memory and suitable for executing code from the computing memory. The code may comprise an adaptor capable of pushing the received raw data to one or more databases to processed data; an extractor capable of extracting the processed data from the one or more databases; predictive analytics capable of receiving the extracted processed data and applying thereto at least one predictive model comprised of target data for the at least one machine, and capable of providing feedback to the at least one machine to modify performance of the at least one machine based on the application of the at least one predictive model; and a visualizer capable of providing at least a visualization of the feedback and of the performance.