Anomaly Detector

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Deploy AI-Powered Anomaly Detection into Your Production Line.

The GoPxL Anomaly Detector toolset leverages traditional and AI-based tools in GoPxL to provide powerful 3D defect detection for challenging applications in the production of parts for the automotive, food, building material, tire, and many other industries. Users can train and deploy the solution all onboard GoMax without relying on cloud or additional GPU for initial and subsequent model training.

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Common Use Cases

Part Inspection

Surface Inspection

Find defects and irregularities on machined or casted parts with complex geometry. Train an AI model to identify good and bad parts directly on the production line without a cloud connection, a CAD file, or complex thresholds typically used with traditional tools.

Find defects and irregularities on flooring and building materials by easily connecting traditional and AI-based tools in GoPxL. Use surface edge and feature tools to mask the board and anomaly detection to locate defects using intensity data.

Key Features

Train on 2D intensity or
3D height map data

Scan parts with intensity and surface data and select the best option at time of training. Use a 3D visualizer to view complex parts and improve labeling accuracy.

Preview functionality without a license

Evaluate anomaly detection with a GoMax in replay mode and train and perform inference without a license.

Generate synthetic and augmented data

Reduce the number of images required for training with integrated generation of synthetic defects and augmented frames that improve the performance of your model.

Store new production data directly on GoMax

Use GoMax to store production data directly to a project archive that can be used to quickly train a new model and improve performance of your application.

Integrated with GoPxL

Benefits

Detect features of varying
shape and size

While traditional tools are excellent at blob and segmentation, they require tuning of thresholds, often specific to each part. With Anomaly Detector, the user is not required to manage detection thresholds. Training relies on providing a dataset of OK and NG parts and creating a detection model specific to the parts in the dataset.

Integrated AI modelling workflow

Model training and related datasets are managed directly inside of GoPxL tools, so you spend less time moving files and datasets compared to using a separate application for training models.

Train on the production line

Train data directly on GoMax and avoid the time, cost, and data security concerns related to moving data to the cloud or local PC. Training and inference uses the same license, allowing for models to be updated in production without an additional, development license.

Use Prediction to label new production data

After initial training, rely on assisted labeling to make iteration fast and easy.

Measure anomalies and define acceptable thresholds

Surface anomalies are passed to subsequent tools for measurement and gauging. Users can pass anomalies of a certain size or shape, depending on what is considered acceptable to the end user.

Add custom functionality with Script and Python GDK

Use the Python-based script tool to add custom logic or pull measurement thresholds from a local file. Power users can leverage the Python GDK to train models using open source and proprietary tools for subsequent deployment.

Let Our
Experts
Do The Work

For more demanding applications or teams without internal resources, contact our FactorySmart AI Solutions team and let us develop the pipeline, model, factory communication and production interface to solve the inspection.

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Let's Talk AI-Powered Anomaly Detection for Your Production Line