Industrial Technology Research Institute

Te-Ming Chen

Sen-Chia Chang

Po-Yu Huang

Hong-Chi Ku

Chung-Wei Lin

Chuang-Hua Chueh

Tsann-Tay Tang

Li-Hua Wang

Chien-Liang Lai

Chi-Ting Day

Cheng-Yang Tsai

Yi-Heng Yang

Pei-Yi Lo

Chun-Fang Chen

A system and a method for parameter optimization with adaptive search space and a user interface using the same are provided. The system includes a data acquisition unit, an adaptive adjustment unit and an optimization search unit. The data acquisition unit obtains a set of executed values of several operating parameters and a target parameter. The adaptive adjustment unit includes a parameter space transformer and a search range definer. The parameter space transformer performs a space transformation on a parameter space of the operating parameters according to the executed values. The search range definer defines a parameter search range in a transformed parameter space based on the sets of the executed values. The optimization search unit takes the parameter search range as a limiting condition and takes optimizing the target parameter as a target to search for a set of recommended values of the operating parameters.

System and method for parameter optimization with adaptive search space and user interface using the same

A dynamic prediction model establishment method, an electric device and a user interface are provided. The dynamic prediction model establishment method includes the following steps. An integration model is established by a processing device according to at least one auxiliary data set. The integration model is modified as a dynamic prediction model by the processing device according to a target data set. A sampling point recommendation information is provided by the processing device according to an error degree or an uncertainty degree between the at least one auxiliary data set and the target data set.

Dynamic prediction model establishment method, electric device, and user interface

The disclosure provides a structure diagnosis system and a structure diagnosis method. The structure diagnosis system includes: a lidar scanner scanning a structure to generate a point cloud data; an input interface receiving the point cloud data; and a processor receiving the point cloud data and generating a point cloud data set. The processor executes a surface degradation and geometry abnormal coupling diagnosis module to: marking a first point cloud range of a surface degradation area according to color space value of the point cloud data set; marking a second point cloud range of a geometry abnormal area according to coordinate value of the point cloud data set; when an abnormal area includes the first point cloud range and the second point cloud range at least partially overlapping each other, determining surface degradation or geometry abnormal occurring at the abnormal area and mark the abnormal area with a predetermined mode.

Structure diagnosis system and structure diagnosis method

A parameter optimization method includes: a parameter search is performed on an input parameter, an output response value and a target value through a plurality of optimization schemes to search for a plurality of candidate recommended parameters. Each optimization scheme is assigned to a weight value according to user historical decision information. At least one recommended parameter is selected from the candidate recommended parameters according to the weight values. An user interface is provided for a user to input a decision instruction. A new input parameter is selected from the at least one recommended parameter according to the decision instruction; the new input parameter is inputted into the target system; and a new output response value is evaluated whether meets a specification condition. The user historical decision information is updated based on the decision instruction to adjust the weight values.

Methods, devices and non-transitory computer-readable medium for parameter optimization

A prediction model building method for a processing machine is provided. While a workpiece is manufactured by the processing machine, a machine parameter set is generated. After the workpiece is manufactured, the workpiece is measured and a workpiece quality parameter set is generated. Then, a component status is determined according to the machine parameter set. Then, a workpiece quality prediction model in the component status is built according to the machine parameter set, the workpiece quality parameter set and the component status.

Building a workpiece quality prediction model

A health assessment method and a health assessment device of a workpiece processing apparatus are disclosed. The health assessment method includes the following steps. Acquire a first sensing data related to the workpiece processing apparatus at an operation stage of the workpiece processing apparatus. Set the first sensing data as a substitution of a first transform model to acquire a virtual workpiece quality. Set the virtual workpiece quality as a substitution of a second transform model to acquire a first virtual apparatus health index.

Health assessment method and health assessment device for workpiece processing apparatus

A method for monitoring a manufacturing apparatus includes collecting a control signal, of a controller and a corresponding sensing signal, of the manufacturing apparatus over a predetermined period of time; segmenting the control signal and the corresponding sensing signal to obtain at least one control step; calculating a character of the controller based on at least one of the control signal, the sensing signal, the at least one control step, or information from a user manual; generating, based on the character, a health index indicating health of the controller; and generating a warning signal when the health index exceeds a predetermined threshold.

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Taipei, Taiwan

Te-Ming Chen