Fulian Yuzhan Precision Technology Co., Ltd

Chen-ting Wu

Cheng-lun Lee

Meng Wang

Qing-yu Wang

Deng-kui Tian

Huang-ming Tseng

Tsung-ju Lin

Hong-wei Zhang

Ying-ying Nie

Xin-di Lei

A method for detecting welding defects of workpieces includes obtaining a first image from an image capturing element; extracting welding information from the first image; transmitting the welding information to a logic processing element to obtain welding defects; the logic processing element formed by evolution of self-learning of historical welding information; and generating display information of the welding defects and displaying visual form and/or characteristics values of the welding defects. An apparatus and a non-transitory computer readable medium for detecting welding defects of workpieces are also disclosed.

Method, apparatus, and non-transitory computer readable medium for detecting welding defects of workpieces

A parameter setting method acquires historical processing data, pre-processes the historical processing data to obtain sample data, and creates a trained regression algorithm using the sample data, then acquires preliminary emulated processing data and predetermined objective parameters inputted, wherein the preliminary emulated processing data and the predetermined objective parameters constitute emulated processing data. A reinforcement learning algorithm is trained with the historical processing data, emulated processing data, and the reinforcement learning algorithm; an actual objective parameter are set according to the actual reference parameters and the reinforcement learning algorithm. Scale of the training data for the reinforcement learning algorithm is greatly increased by the trained regression algorithm, which improves the accuracy of the learning algorithm. A parameter setting device, an electronic device, and a storage medium are also disclosed.

Parameter setting method and device for anodic dyeing process, and electronic device

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Taipei, Taiwan

Chen-ting Wu