AI AOI Precision Automatic Optical Inspection Machine for Electroformed Stencil SV-M8L Series

Main Functions Introduction

The SV series high-precision electroformed stencil inspection system is mainly used for appearance defect inspection of electroformed stencils.
Defect inspection is performed automatically through optical imaging, graphic algorithm + traditional algorithm + AI algorithm.

A. Automatically scan and identify CAM data, automatically parse CAM design data;
B. Automatic optical system scans product images for data processing, extracts effective features and coordinates;
C. Automatically identify and classify defects, display actual defect images for secondary confirmation, indicate defect positions via laser positioning targets, and output defect coordinate list;
D. Maintenance workstation can automatically read inspected product defect data and display them one by one for fast repair;
E. Real-time storage of inspection information for later traceability, statistics and SPC analysis;
F. Inspection can be implemented in the whole process of electroformed stencil production;
G. Threshold can be set independently to avoid over-quality control and unify inspection standards;
H. Support automatic code scanning for fast data extraction, connect to MES system. Suitable for electroformed stencil acceptance, all-in-one configuration.

1.Overview

In traditional manufacturing factories producing precision electroformed stencils, tiny defect quality inspection relies on manual labor. However, manual inspection of small and highly variable defects has problems such as high labor intensity, poor repeatability, high error rate, and unstable quality. Diverse product specifications further increase inspection workload. Manual inspection cannot achieve stable, accurate and efficient detection, leading to unstable yield and reduced productivity.

As a cutting-edge AI technology, AI defect detection has natural advantages in electroformed stencil inspection. It enables non-contact, high-efficiency, cost-effective and high-productivity quality control. Supported by machine vision, inspection efficiency is greatly improved, and standardization difficulties and high false rates are solved.

Based on the DLIA deep learning platform and AI industry models, our AI vision inspection system optimizes defect classification algorithms, supports database and production line upgrades. It achieves pixel-level detection accuracy and millisecond-level speed. The system realizes high-precision, high-efficiency, full-format defect detection for electroformed stencils, reduces labor intensity, improves yield, and enhances enterprise competitiveness.

Standard models are available for photovoltaic, microelectronic, semiconductor BGA stencil, and PCB applications. With fixtures, it can also inspect nickel sheets and other sheet materials. All functions are effectively implemented and optimized.

2. Inspection Performance

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Product sample - High-precision electroforming stencil

1.1 Test Dimensions

Electroformed stencil/metal plate outer frame size: 450×450mm, 736×736mm
Electroformed electroforming stencil/metal plate inspection size: 620×620mm, 450×450mm
Note: Inspection dimensions can be customized according to customer product requirements.

1.2 Performance

① Missing detection rate: For defects ≥5μm, missing detection rate ≤1% (unknown defects not evaluated; software continuously improved).
② False alarm rate: Probability of total false points >100 in single inspection ≤15%.

3.Detectable Defect Types

Note: Inspection effect varies with sample structure; some defects require further sample collection.

4. Technical Parameters

5. Equipment dimensions and schematic diagram

Dimensions: 1200*1290*1520mm (excluding the three-color lights)