Chinese Academy of Sciences

Beijing Ten Dimensions Technology Co., Ltd.

Gang Xiong

Zhen Shen

Feiyue Wang

Xue Ping Liu

Chao Guo

Xiuqin Shang

Jun Yan

Xiao Wang

Bo Li

Yuqing Li

Yi Xie

Li Wan

Bin Hu

Hang Gao

Fenghua Zhu

Jiawei Liao

Xisong Dong

Zhengpeng Wu

Fei-Yue Wang

Bin Tian

Kunfeng Wang

Can Luo

Meihua Zhao

An online monitoring device of 3D printing equipment includes a signal collection module, a signal processing module, a feature extraction module, a monitoring module and a knowledge base module. A vibration signal of a preset component of the 3D printing equipment is collected by a vibration sensor. The collected vibration signal of each preset component is converted from an analog signal to a digital signal and the spectrum characteristics are extracted. Based on the spectrum characteristics of each preset component, the operation state type of the preset component is obtained by a comparative analysis model. The knowledge base module is configured to store newly added samples and initial samples of the 3D printing equipment. The initial samples include spectrum characteristic information and corresponding fault category of known faults, and the newly added samples include spectrum characteristic information and corresponding fault category of new faults.

Online monitoring device and system for a 3D printing device

A neural network-based error compensation method for 3D printing includes: compensating an input model by a deformation network/inverse deformation network constructed and trained according to a 3D printing deformation function/inverse deformation function, and performing the 3D printing based on the compensated model. Training samples of the deformation network/inverse deformation network include to-be-printed model samples and printed model samples. The deformation network constructed according to the 3D printing deformation function is marked as a first network. During training of the first network, the to-be-printed model samples are used as real input models, and the printed model samples are used as real output models. The inverse deformation network constructed according to the 3D printing inverse deformation function is marked as a second network. During training of the second network, the printed model samples are used as real input models, and the to-be-printed model samples are used as real output models.

Neural network-based error compensation method, system and device for 3D printing

The present invention relates to a technical field of 3D printing, and more particularly to a 3D printer spray nozzle structure and a method thereof for controlling speed and precision. According to the present invention, a feeding pipeline is embedded in an external shell, the feeding pipeline and an extruder are coaxially connected; the extruder is driven by a driving device, so as to rotate relative to the feeding pipeline. A rotation angle of the extruder relative to the feeding pipeline is controlled by rotation of a motor, for controlling a filament area actually sprayed by the extrude, in such a manner that printing speed and precision is controlled for suiting different requirements of different printing area. The present invention controls the printing speed and precision, for improving overall printing speed with precision requirements satisfied, and is applicable to 3D printer spray nozzle structure and controlling.

3D printer spray nozzle structure and method thereof for controlling speed and precision

A 3D printer spray nozzle includes a feeding pipeline, an extruder located under the feeding pipeline, an external housing and a driving device; wherein the feeding pipeline is embedded in the external housing, the extruder is coaxially fixed under the feeding pipeline, a center of gravity of a cross section area of an inner channel of the feeding pipeline and that of the extruder are located on a same axis which is perpendicular to the cross section area of the inner channel of the feeding pipeline and that of the extruder, the feeding pipeline is driven by the driving device to rotate around the axis relative to the extruder, thereby aiming at different rotation angles, widths of extruding forming areas of the extruder at a same direction are different so as to adjust a cross section area of a sprayed filament.

3D printer spray nozzle capable of adjusting cross section areas of extruded materials and method for controlling printing speed and precision of the 3D printer spray nozzle

The present invention relates to a technical field of traffic monitoring, and more particularly to a method for detecting traffic violation. The present invention includes firstly localizing vehicle salient parts through salient features including vehicle license numbers and vehicle rear lights, and representing a vehicle with the vehicle salient parts, then tracking the vehicle with a Kalman filter based on the vehicle salient parts, and finally detecting vehicle violation through moving trajectory analysis and setting violating detecting areas. The present invention solves vehicle violation detection problems in complex engineering application conditions such as illumination change and detection noise, and is suitable for city traffic management under complex conditions.

Method for detecting traffic violation

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Guangdong, China

Gang Xiong