9 results about "Defect prevention" patented technology

A method for preventing defects in a thin film deposited on a semiconductor substrate includes forming a plurality of trenches on a periphery-region of the semiconductor substrate to yield a trenched surface. The semiconductor substrate includes a pixel array; the periphery-region surrounds the pixel array. The trenched surface includes (i) a plurality of trench regions each forming a respective one of the plurality of trenches and (ii) between each pair of adjacent trenches, a respective one of a plurality of inter-trench surfaces. The method also includes depositing the thin film on the surface such that the thin film covers each inter-trench surface and conformally covers each trench region.

The utility model discloses an existing railway roadbed disease prevention and control structure without interrupting driving, and relates to the technical field of railway roadbed diseases, the existing railway roadbed disease prevention and control structure comprises a C30 concrete retaining wall, a self-propelled hollow anchor rod, an inclined steel floral tube and a drainage system, the drainage system is arranged on the C30 concrete retaining wall, the C30 concrete retaining wall is arranged at a slope toe of a roadbed, and the self-propelled hollow anchor rod is arranged on the C30 concrete retaining wall. A self-propelled hollow anchor rod is arranged on the side face of the roadbed. The existing railway roadbed disease prevention and control structure without interrupting driving is simple and easy to construct, remarkable in disease prevention and control effect, high in practicability and capable of achieving implementation of a design scheme and guaranteeing effectiveness of measures without an ultra-large instrument.

The application is suitable for the technical field of data processing, and provides a production data backtracking method and system for tank suspicious defect calibration, which comprises the following steps: collecting multi-modal data of tank defects, production process parameters and historical environment data, and establishing a multi-source database with time and space correlation; constructing a defect evolution model based on the multi-modal data, reconstructing a full life cycle track and outputting initial morphology and evolution rate parameters; dynamically correcting the defect expansion rate in combination with the historical environment data, and outputting environment weighted correction parameters; identifying composite defects and decoupling their coupling effect, and outputting characteristic parameters of each independent defect; and positioning the specific production link where the defects are generated and constructing a defect-process correlation graph through matching analysis of the above parameters and the multi-source database. The application realizes accurate tracing of tank defects from the current state to the production source, and provides reliable support for defect prevention and process optimization.

The application discloses a kind of switch rail flash welding construction methods, comprising the following operating steps: S1, construction preparation and switch investigation;S2, rail state inspection and pretreatment;S3, welding machine alignment and clamping;S4, dynamic compensation-based flash welding;S5, postweld normalizing treatment;S6, rough polishing;S7, welding head flaw detection;S8, fine polishing and profile finishing;S9, flatness and appearance inspection;S10, line locking.Effectively improve the consistency of welding quality, enhance the defect prevention capability, optimize the construction organization efficiency, reduce the operation threshold and realize the welding process traceability.

The invention relates to the technical field of welding process optimization and defect detection, in particular to a welding process parameter recommendation and defect prediction system which comprises a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a parameter recommendation module, a defect prediction module and a visual interaction module. The output end of the data acquisition module is connected with the input end of the feature extraction and fusion module, and the output ends of the feature extraction and fusion module and the feature processing module are connected with the input end of the feature extraction and fusion module; time sequence process parameters, molten pool images and historical data are collected and preprocessed and then input into the collaborative model, CNN-LSTM extracts time sequence features, ResNet-Attention extracts spatial features, and weighted fusion is carried out on the time sequence features, the molten pool images and the historical data; optimal parameters are output in combination with a full-connection network and a genetic algorithm, defects are predicted and early warning is triggered through the full-connection network, 'parameter optimization-defect prevention 'closed-loop control is achieved, multiple welding types are adapted, and the welding quality stability and the production efficiency are effectively improved.

The invention discloses an LPBF hole defect prevention and control method and system based on bimodal fusion digital twinning, and the method comprises the steps: constructing a virtual-real fusion digital twinning system, and obtaining technological parameters, geometric parameters, kinematics parameters and dynamic thermal characteristic data in real time through an online state management and data collection module; and uniformly processing static parameters and dynamic thermal characteristic parameters by using a bimodal collaborative adaptive model. The model adopts a single deep network architecture, is internally provided with a situation gating mechanism, fuses thermal characteristics in an online state management mode to realize foresight prediction of pore defects, only depends on static parameters to drive virtual iterative optimization in an offline simulation mode, and outputs a global optimal process parameter combination. According to the system, Score-CAM interpretability analysis is introduced, a prediction black box is converted into a defect positioning thermodynamic diagram, a complete closed loop from data acquisition to intelligent prediction to virtual optimization to precise regulation and control is realized, quality control is converted into beforehand active prevention from post passive correction, the material object trial and error cost is remarkably reduced, and the manufacturing quality and credibility are improved.

Aspects of the disclosure relate to s computing system that is configured to use heuristic and / or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and / or adaptable solution to detecting and preventing defects in software applications.

Aspects of the disclosure relate to a computing system that is configured to use heuristic and / or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and / or adaptable solution to detecting and preventing defects in software applications.

The present invention discloses a system and method for automatic defect inspection and operator assessment in a circular knitting machine (104). The system comprises at least one an external camera (102) to capture at least one image of surroundings of a circular knitting machine (104). Further, the system may comprise an internal inspection unit (106) to analyze at least one captured image to detect defects in the fabric and / or data collection unit attached to knitting machine. Furthermore, the system may comprise an automatic defect processing unit (110) to receive the at least one analyzed image data from the internal inspection unit (106) for decision making Additionally, the automatic defect processing unit (110) classifies the detected defects into preventable defects and non-preventable defects. Thereafter, the automatic defect processing unit (110) based on the classification determines whether to stop the circular knitting machine (104) or to trigger an alert for operator intervention.