Visual Inspection for injection Molding Plastic Components

DeepInspect- Automated visual inspection software for injection molded parts

SwitchOn is a leading plastic injection molding component quality inspection company, leveraging our AI-powered DeepInspect software to transform the inspection process. DeepInspect uses advanced visual inspection systems to detect defects such as cracks, surface imperfections, misalignments, dimensional inconsistencies, flash, sinkage, warpage, weld lines, flow lines, cracking, and crazing in plastic molded components. By integrating machine learning and computer vision, DeepInspect ensures automated quality control throughout the injection molding process, detecting flaws in real-time and reducing waste. It seamlessly integrates into existing production lines, offering high-speed inspection without compromising on precision. Dimensional inspection ensures every component meets strict specifications, while defect detection addresses surface defects and mold imperfections. DeepInspect improves quality assurance, enhancing efficiency, productivity, and consistency, while ensuring the highest molded part quality for industries such as automotive, electronics, and consumer goods manufacturing.

injection molding inspection

APPLICATION

Impact

Using DeepInspect for injection molding component inspection ensures real-time detection of defects like flash, sinkage, warpage, weld lines, flow lines, cracking, and crazing. It enhances dimensional accuracy, reduces waste, boosts productivity, and improves overall quality assurance, ensuring precise, consistent, and high-quality molded components.

Detect all kind of injection molding defects with 99.5% Accuracy

Flash in Injection Molding

Machine Vision for Quality Control in plastic molded components

Warpage in Plastic Molding

Molded Plastic Quality Inspection System

Crazing in Injection Molding

AI-powered Mold Defect Detection

Injection Molding Flow Marks

Machine Vision for Quality Control

Interested in Streamlining Injection Mold Quality Inspection Process?

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    What is DeepInspect and how does it enhance quality control?

    DeepInspect is an AI-powered visual inspection system designed to automate quality control in injection molding, ensuring high precision in dimensional inspection and defect detection.

    How does DeepInspect integrate into existing production lines?

    DeepInspect seamlessly integrates with existing plastic injection molding lines, providing real-time defect detection without disrupting production, ensuring consistent quality and higher throughput.

    How does DeepInspect improve productivity in plastic molding?

    By automating defect detection, DeepInspect reduces manual inspection time, increases line efficiency, and ensures quick, accurate quality assurance for molded plastic components.

    What cameras does tire inspection software Deepinspect support?

    We support Area Scan, Line Scan, and Thermal cameras. Our software is compatible with industry-standard vendors like Basler, Baumer, Allied Vision, FLIR, and others.

    How many images are needed to train DeepInspect's machine vision model?

    DeepInspect requires fewer than 200 good images for model training, significantly reducing setup time for defect detection 

    Does DeepInspect need defective (NG) images for AI model training?

    No. DeepInspect uses unsupervised AI, eliminating the need for NG images in training.

    How long does it take to train a model with DeepInspect?

    Model training is completed in less than 45 minutes, allowing rapid deployment of vision inspection system

    How quickly can a production line demo be done with DeepInspect?

    A line demo for DeepInspect system can be conducted within a day, showcasing its fast implementation and ease of use

    What industries can benefit from using DeepInspect in quality inspection?

    DeepInspect is ideal for industries such as automotive, electronics, and consumer goods manufacturing, providing automated quality inspection for high-precision plastic molded parts.