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Posted on Oct 8 Originally published at siliconlogix.it

SLX SynthForge on GitHub: AI for industrial vision

#ai #machinelearning #cpp #python

SLX SynthForge is available on GitHub, bringing together computer vision for electronic board inspection, 3D scenes and synthetic data. The Silicon LogiX desktop application lets operators edit a virtual board, capture its image and analyse defects with an AI model that runs locally.

The application uses C++17, Qt Quick and QML. The proprietary Silicon LogiX SynthForge Core engine handles rendering, visual analysis and inference, while Python and NumPy train the classifiers. The project documents the workflow from preparing examples to comparing model versions and preserving inspection evidence.

From a 3D board to defect detection

The reference scene is PowerBoard, a demonstration board with 46 editable components. Operators can remove a component, change its position or rotate it before starting an inspection. The board model distinguishes nominal appearance, missing components and changes in position or appearance.

Each result connects the CAD reference to the captured image detail, the score and the model identity. Outcomes are labelled PASS, DEFECT and REVIEW: an accepted component, a detected defect or a case requiring review. Insufficient visibility or captures outside the expected profile require review, as described in the inspection workflow.

Virtual board inspection: defects in C2 and U4 are associated with the captured image and their CAD references.

The vision engine: Silicon LogiX SynthForge Core

At the centre of the application is a C++ engine distributed as a binary SDK in the SiliconLogiX.SynthForge.Core.dll library. Public APIs expose rendering, dataset generation and inspection functions. The Engine module produces the scene image and applies the numerical model to the capture. The SDK documentation describes the separation between the interface and the engine.

Board recognition uses a 640 × 480 pixel RGB image and a defined inspection camera. The engine analyses the captured pixels; commands used to introduce defects remain preparation and traceability data. Model compatibility, asset identity and capture conditions are checked before inference.

CAD scene
  ↓ RGB capture
  ↓ Visual features
  ↓ Linear softmax classifier
  ↓ PASS / DEFECT / REVIEW and report

The C++ engine connects the virtual scene to image analysis and the inspection outcome.

Machine learning: visual features and softmax classification

The AI technology is a multiclass linear classifier with a softmax function. Numerical image characteristics, called features, are normalised and combined with weights learned during training. Softmax produces class scores, which are used alongside acceptance thresholds and capture checks.

The editable board inspector works with 16 visual features per example and three classes: nominal, missing, and changed position or appearance. The inspector training pipeline preserves normalisation parameters, weights and the model identifier.

The dedicated C1 and U4 component laboratory uses 24 × 24 pixel RGB crops aligned to the component regions. Its model distinguishes five scenarios: nominal board, missing component, misalignment, reversed polarity and solder bridge. This is a separate pipeline with its own visual softmax classifier. Scores describe the model response and need to be assessed in the context of the experiment.

Python and NumPy for model training

Training uses NumPy for numerical computation and the Adam optimiser to update weights. The loss function is cross entropy, which measures how far classifications differ from example labels. The model is selected using validation loss.

Training, validation and testing have distinct roles. Normalisation is fitted on training data; a separate test set evaluates the model after selection. Parameters are exported as versioned JSON files and loaded by the C++ engine for inference. Inspection with the included models works without installing Python; a Python environment is needed to train new models.

Synthetic data and capture robustness

Dataset forge generates examples from the 3D scene and associates them with simulated defect labels. This makes experiments repeatable, organises cases and diversifies training examples. Capture groups are separated between training, validation and testing to reduce data leakage.

Robustness lab varies lighting, reflections, blur and camera tilt. Its result grid shows where the model maintains acceptable decisions and where review or a new capture is needed. The evaluation workflow connects image quality to classifier behaviour.

Qt Quick and C++ for the desktop application

Qt Quick, QML and Qt Quick Controls provide the operator interface: board editing, capture controls, inspection results and AI laboratories. OpenGL handles the interactive 3D preview, with a software mode available for workstations that need it.

C++ services coordinate processing outside the interface flow, keeping presentation separate from rendering and generation tasks. CMake and CTest organise builds and automated checks. The documented architecture distinguishes presentation, the vision engine and training tools.

Model comparison and result traceability

Assembly AI lab collects development cases, prepares new examples and compares the initial model with a candidate. An independent benchmark is frozen before collection: both versions are evaluated on the same data with the same threshold. New examples feed training and validation.

The comparison considers missed defects, false alarms, REVIEW cases and the proportion of accepted decisions. Candidate activation is explicit. Experiments and reports preserve images, CAD references, edits and model identity; HTML, CSV and JSON formats make outcomes accessible. The published validation documents results and the conditions of synthetic experiments.

How to try SLX SynthForge

The v0.4.1 release includes a portable Windows package. The GitHub repository contains application sources, screenshots, documentation and examples. The proprietary engine is supplied as a binary SDK under the Silicon LogiX evaluation licence.

SynthForge is an industrial vision demonstration environment based on synthetic data. Applying the workflow to a production line requires validation with real images, optics, lighting and defects representative of the product.

Industrial vision and custom software

For businesses developing inspection tools or integrating visual analysis and reporting, Silicon LogiX offers custom software development and AI and machine learning services. SynthForge shows how a desktop interface, native engine, training data and verifiable results can work together.

Contact Silicon LogiX about an industrial vision project: describe the process, available data and required functions to define requirements and feasibility experiments.

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Originally published on Silicon LogiX.

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