T/SDMTGM 0015-2024 Active Group standards

T/SDMTGM 0015-2024 Machine vision online detection method for surface defects of stamping forming parts

Publish Date: 2024-10-16 Implement Date: 2024-10-16 For services related to genuine standard inquiry, procurement, translation, and other related services in China, please Contact Us

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Basic Information

Standard Code: T/SDMTGM 0015-2024

Standard Type: Group standards

Standard Status: Active

is_force_gb: no

CCS Name: Forging and pressing machinery

ICS Name: Forging equipment, stamping machines, and shearing machines

Publish Date: 2024-10-16

Implement Date: 2024-10-16

Scope

Main technical content: 1) Clarified the terminology and definitions of online machine vision detection of surface defects in stamped parts. 2) Specified the categories of surface defects in stamped parts detected by machine vision, including scratches, wrinkles, necking, cracks, under-punching, over-punching, stretch marks, and burrs. 3) Outlined the principle of online machine vision detection of surface defects in stamped parts. Based on a historical database of surface defects, the system extracts defect feature information, trains a model using this data, and then uses the trained model to identify, classify, statistically analyze, store, and query surface defects in stamped parts. 4) Specified the equipment components and requirements for online machine vision detection of surface defects in stamped parts. The detection equipment includes a vision camera, vision software, a vision system controller, light sources, switches, PLCs, monitors, computers, camera brackets, light source brackets, conveyor belts, and cables. 5) Detailed the detection process of surface defects in stamped parts. After the stamped parts are stamped by the final press, a robot retrieves them and transfers them to the conveyor belt. Once they reach the designated position and are within the camera's field of view, the camera receives a signal from the PLC to capture an image. The stamped part image is analyzed in real-time by vision algorithms to identify and classify defects, followed by statistical analysis of the defect data and real-time feedback. 6) Clarified the data processing for online machine vision detection of surface defects in stamped parts, including data recording, analysis, and feedback. 7) Specified the detection requirements for surface defects in stamped parts. Through image processing, identification, and classification, the system determines the type of surface defects (e.g., scratches, cracks) and ensures detection of defects larger than 1mm, as well a